Data Analyst Jobs in India 2026: The Real Salary, Skills, and Hiring Numbers Nobody’s Telling You

The post Data Analyst Jobs in 2026: Real Salary, Skills, and Hiring Numbers Nobody’s Telling You appeared first on Vskills Blog.

Data literacy simply means the ability to read, understand, question, analyse, and communicate data in a meaningful way. It does not mean that everyone needs to become a data scientist. Instead, it means that every professional should know how to look at data carefully, understand its context, identify patterns, avoid misleading conclusions, and use evidence to support decisions. For example, a business manager should be able to understand why sales are falling, an HR professional should be able to interpret employee attrition trends, and a marketing executive should be able to judge whether a campaign is actually performing well.

The importance of data literacy has increased further because of the rapid growth of artificial intelligence and automation. AI tools can now generate reports, summaries, charts, and predictions within seconds. However, these tools are only useful when people know how to evaluate the output. Without data literacy, professionals may blindly trust incorrect numbers, biased insights, or incomplete information. With data literacy, they can use AI more intelligently and responsibly. 

What Is Data Literacy?

Data literacy is the ability to read, understand, question, analyse, and communicate data in a useful way. In simple words, it means knowing how to look at numbers, charts, reports, dashboards, or tables and understand what they are trying to tell you.

However, data literacy is not just about reading numbers. It is also about understanding the context behind those numbers. For example, if a company says its sales increased by 20%, a data-literate person will not stop at that number. They will ask: Compared to which period? Was this growth across all regions or only one market? Did profits also increase? Was the growth due to higher demand, price increases, discounts, or one-time factors?

This is what makes data literacy important. It helps people move beyond surface-level information and understand the real meaning behind data. A data-literate person can:

• Read basic charts, tables, and dashboards
• Understand common terms like average, percentage, growth rate, trend, and KPI
• Ask the right questions before trusting a number
• Identify missing, outdated, or misleading data
• Compare data across time, groups, or locations
• Explain insights in simple language
• Use data to support better decisions

Data literacy does not mean that everyone must learn advanced coding, machine learning, or complex statistics. Those are specialised skills. Basic data literacy is more practical. It helps a professional understand whether a report makes sense, whether a chart is misleading, whether a business claim is supported by evidence, and what action should be taken based on the data.

For example, a sales executive may use data literacy to understand which product is selling faster. An HR professional may use it to identify why employee attrition is increasing. A teacher may use it to track student performance. A business owner may use it to understand which customers are most profitable.

In short, data literacy is the skill of turning raw information into a better understanding. In 2026, this skill will become essential because workplaces are becoming more digital, AI-driven, and evidence-based.

Skill 1: Understanding Basic Data Terms

The first step towards becoming data literate is understanding the basic language of data. Many people feel uncomfortable with data because reports and dashboards often use terms like KPI, metric, variable, benchmark, conversion rate, outlier, or growth rate. Once these terms become clear, data becomes much easier to read and use.

Basic data terms help professionals understand what exactly is being measured. For example, a business dashboard may show revenue, profit, customer acquisition cost, conversion rate, retention rate, and average order value. These numbers may look simple, but each one tells a different story about business performance. Some important data terms everyone should know include:

Understanding these terms also helps people avoid wrong conclusions. For example, average and median are often confused. If a few very high salaries are included in a company’s salary data, the average salary may look high. But the median salary may show a more realistic picture of what most employees actually earn.

Similarly, a company may say that customer traffic increased by 30%. But a data-literate person will ask whether this increase also led to more sales, higher revenue, or better customer retention. This is because one metric alone rarely gives the full picture.

In 2026, professionals do not need to memorize technical definitions. But they should be comfortable with the basic vocabulary of data. Once they understand these terms, they can read reports more confidently, ask sharper questions, and participate better in data-driven discussions.

Skill 2: Asking the Right Questions Before Using Data

Good data literacy does not begin with a dashboard, spreadsheet, or AI tool. It begins with the right question. Before using any data, a person must be clear about what they are trying to understand and what decision the data will support. Many people make the mistake of looking at data without a clear purpose. They open a report, see many numbers, and start drawing conclusions too quickly. But data becomes useful only when it is connected to a specific question.

For example, instead of asking: “Why are sales bad?”

A better question would be: “Which product category, region, or customer segment has seen the sharpest fall in sales over the last three months?”

The second question is more useful because it is specific. It helps the person know what data to check and what pattern to look for.

Some important questions to ask before using data include:

Asking the right questions also helps people avoid misleading conclusions. For example, if a company sees a fall in website traffic, it should not immediately assume that customers are losing interest. The fall could be due to a technical issue, seasonality, reduced advertising spend, or changes in search engine visibility.

In 2026, this skill will become even more important because professionals will increasingly use AI tools to analyse data. But AI can only give useful answers when the question is clear. A vague question will usually lead to a vague answer. A sharp question will lead to a sharper insight. Therefore, one of the most important data literacy skills is knowing how to question data before trusting it. A data-literate person does not just ask, “What does the data show?” They also ask, “Why does it show this? What is missing, and what should we do next?”

Skill 3: Understanding Data Sources and Data Quality

Not all data is equally reliable. Some data is accurate, up to date, and useful. Some data is incomplete, outdated, biased, or wrongly collected. That is why understanding data sources and data quality is one of the most important data literacy skills. A data source means where the data comes from. It could come from customer surveys, company records, government reports, website analytics, social media platforms, financial statements, research studies, or third-party databases. Before using any data, professionals should know its source.

For example, a business may use customer feedback from social media to understand customer satisfaction. But this data may not represent all customers. Unhappy people are often more likely to post online than people who are satisfied. So, while social media feedback is useful, it may not give the full picture. Good data quality usually means that the data is:

• Accurate
• Complete
• Updated
• Consistent
• Relevant
• Clearly defined
• Collected from a reliable source

Poor data quality can create serious problems. If a company has duplicate customer records, it may send the same message many times to one person. If employee data is outdated, HR may make wrong workforce decisions. If sales data is entered incorrectly, business teams may misunderstand demand. Here are some common data quality issues:

Understanding data quality helps professionals become more careful and responsible. It reminds them that data is not automatically correct just because it appears in a report or dashboard. In 2026, as more decisions become data-driven and AI-assisted, the quality of data will matter even more. AI tools, dashboards, and analytics systems can only produce useful insights if the data given to them is reliable. Poor data will lead to poor decisions.

This is why every professional should learn to ask: Where did this data come from? Is it complete? Is it recent? Is it relevant? Can I trust it? These simple questions can prevent many mistakes and make data-based decisions much stronger.

Skill 4: Cleaning and Organizing Data

Data is rarely perfect in its raw form. In most cases, it has spelling mistakes, duplicate entries, missing values, inconsistent formats, or unnecessary columns. This is why cleaning and organizing data is an important data literacy skill. Data cleaning means improving the quality of data before using it for analysis. If the data is not cleaned properly, the final insights may be wrong, even if the analysis looks professional. For example, imagine a company has customer location data written in different ways:

If these entries are not cleaned, the system may treat them as different locations. This can affect sales analysis, customer segmentation, marketing campaigns, and regional performance reports. Some basic data cleaning tasks include:

Organizing data is equally important. A well-organized dataset should be easy to read, filter, sort, and analyse. In a spreadsheet, each column should represent one variable, such as name, age, city, product, date, or sales value. Each row should represent one record, such as one customer, one transaction, one employee, or one product.

For example, a messy sales sheet may have merged cells, blank rows, mixed date formats, and unclear headings. Such a sheet may look fine visually, but it will be difficult to use for pivot tables, dashboards, or automated analysis. A clean dataset, on the other hand, allows faster and more accurate decision-making.

In 2026, this skill will become even more important because more professionals will use AI tools, dashboards, and automation systems. These tools need clean and structured data to work properly. If the input data is messy, the output will also be unreliable. Cleaning and organising data may sound basic, but it is one of the most practical data literacy skills. It helps professionals avoid errors, save time, and build more confidence in their analysis.

Skill 5: Basic Statistics for Everyday Decisions

Basic statistics is one of the most useful data literacy skills because it helps people understand what numbers actually mean. Many professionals do not need advanced statistics, but they should know how to interpret common measures like averages, percentages, growth rates, and trends. Statistics helps people avoid surface-level conclusions. For example, if a company says that its average employee salary is ₹80,000 per month, that number may not show the full reality. If a few senior employees earn very high salaries, the average may look higher than what most employees actually receive. In this case, the median salary may give a better picture. Some basic statistical concepts everyone should know include:

These concepts are useful in everyday business decisions. A marketing team may use percentage change to understand whether a campaign improved conversions. An HR team may use averages and distribution to study salary differences. A finance team may use growth rates to track revenue. A teacher may use median scores to understand student performance more accurately.

However, basic statistics should be used carefully. A rise in one number does not always mean that one thing caused another. For example, if social media followers and sales both increase in the same month, it does not automatically mean that social media caused the sales increase. There may be other reasons, such as discounts, festivals, advertising, or seasonal demand.

In 2026, professionals will increasingly work with dashboards and AI-generated summaries. These tools may quickly show trends and patterns, but a person still needs basic statistical understanding to judge whether the insight makes sense. Without this skill, it becomes easy to misread data or accept misleading claims. Basic statistics gives professionals the confidence to move beyond “the number looks good” and ask, “What does this number really mean?”

Skill 6: Spreadsheet and Dashboard Skills

Spreadsheet and dashboard skills are among the most practical data literacy skills for 2026. Even with the rise of AI tools, most workplaces still depend heavily on Excel, Google Sheets, Power BI, Tableau, Looker Studio, and internal dashboards. These tools help professionals organise data, track performance, compare results, and present insights. A person does not need to become an advanced data analyst to use these tools well. But they should know the basic functions that help them work with data confidently. Important spreadsheet skills include:

Dashboard skills are slightly different. A dashboard usually presents data in a visual and summary form. It may include KPIs, graphs, tables, filters, and performance indicators. A data-literate professional should know how to read a dashboard properly instead of only looking at the biggest number on the screen. When using dashboards, professionals should ask:

• What time period does this dashboard cover?
• What does each KPI measure?
• Is the data updated daily, weekly, or monthly?
• Are filters applied?
• What is being compared?
• Is the chart showing numbers, percentages, or growth rates?
• Is any important category missing?

For example, an HR dashboard may show employee attrition at 12%. But a better reading would ask whether attrition is higher among new employees, specific departments, women employees, senior roles, or certain locations. This turns a simple number into a useful business insight. Similarly, a sales dashboard may show that revenue increased. But the real question is whether this growth came from more customers, higher prices, repeat purchases, or one large order. Spreadsheet and dashboard skills help people break down such numbers and understand the story behind them.

In 2026, these skills will remain essential because most organisations want employees who can work independently with data. Professionals who can clean a spreadsheet, create a pivot table, read a dashboard, and explain key insights will have a strong advantage in almost every field.

Skill 7: Data Visualization

Data visualisation is the skill of presenting data through charts, graphs, maps, dashboards, and other visual formats. It helps people understand patterns more quickly than raw numbers. A large table may take time to read, but a well-designed chart can immediately show whether sales are rising, costs are increasing, or performance is uneven across regions. In 2026, data visualisation will be an important skill because professionals are expected to explain insights clearly and quickly. Whether someone is preparing a business presentation, marketing report, HR dashboard, policy brief, or financial update, the right visual can make the message more powerful. However, good visualisation is not just about making a chart look attractive. It is about choosing the right chart for the right purpose.

A data-literate person should also know how charts can mislead people. For example, a bar chart with a broken axis may make a small difference look very large. A pie chart with too many categories may become confusing. A line chart without proper labels may hide the real trend. Similarly, using percentages without showing actual numbers can create a false impression.

Good data visualisation should be:

• Simple and easy to understand
• Properly labelled
• Based on the right chart type
• Free from unnecessary decoration
• Honest in scale and proportion
• Focused on the main message
• Supported by clear explanation

For example, if a company wants to show monthly sales performance, a line chart will be more useful than a pie chart because it shows movement over time. If an HR team wants to compare attrition across departments, a bar chart will be clearer than a long table. In 2026, professionals who can create clean and meaningful visuals will stand out because they can make complex data easier for others to understand. Data visualisation turns numbers into clarity. It helps teams see what is happening, where the problem lies, and what action may be needed next.

Skill 8: Data Storytelling

Data storytelling is the ability to turn data into a clear, meaningful, and useful message. It is one of the most important data literacy skills because data alone does not create understanding. People need context, explanation, and direction. A report may say that customer churn increased from 12% to 18%. But this number becomes more useful when it is explained as a story:

“Customer churn has increased from 12% to 18% in the last six months, mainly among first-time users. This suggests that the onboarding experience may not be strong enough, and the company may need to improve customer support during the first few weeks.”

This is data storytelling. It connects the number with the reason, the impact, and the possible action. A good data story usually answers four questions:

Data storytelling is especially useful in workplaces because decision-makers often do not have time to study every row of data. They need a clear explanation of what the data means and why it matters. A good data story helps them make faster and better decisions.

For example, instead of saying: “Website traffic declined by 20%.”

A stronger data story would be: “Website traffic declined by 20% after the advertising budget was reduced in March. The decline was highest among new visitors, while returning visitors remained stable. This means the brand is still retaining its existing audience, but new customer discovery has weakened.”

This kind of explanation is much more useful because it gives direction. Good data storytelling includes:

• A clear main message
• Relevant numbers
• Context behind the data
• Comparison with previous periods or benchmarks
• Simple visuals
• Practical interpretation
• Actionable recommendations

However, data storytelling should not become data manipulation. The goal is not to force the data to support a fixed opinion. The goal is to explain the data honestly and clearly. A responsible data storyteller also mentions limitations, missing information, or uncertainty where needed.

In 2026, data storytelling will become even more valuable because organisations will have more data than ever before. The challenge will not be only collecting data, but making sense of it. Professionals who can explain data in a simple, logical, and action-oriented way will become stronger communicators, better decision-makers, and more valuable team members.

Skill 9: AI and Data Interpretation

AI is changing the way people work with data. In 2026, many professionals are using AI tools to summarise reports, create charts, find patterns, write insights, and even generate business recommendations. This makes work faster, but it also makes data literacy more important than before.

AI can process large amounts of information quickly, but it does not always understand context perfectly. It may give an answer that looks confident but is incomplete, outdated, biased, or incorrect. This is why professionals should not blindly trust AI-generated insights. They should know how to interpret, check, and question them.

For example, if an AI tool says that sales are falling because customer demand is weak, a data-literate professional will ask whether the tool has checked all possible reasons. Sales may also fall because of supply issues, pricing changes, reduced marketing spend, seasonal patterns, or a problem in distribution. To use AI responsibly with data, professionals should know how to:

A simple example is asking AI to analyze customer feedback. If the prompt is vague, such as “Analyze this data,” the output may be too general. A better prompt would be: “Identify the top five reasons customers are dissatisfied, group them by theme, and suggest which issues need immediate attention based on frequency and severity.”

AI can be very useful, but it works best when humans guide it properly. A person who understands the data, the business context, and the decision being made can use AI much more effectively than someone who only copies and pastes information into a tool. In 2026, AI and data literacy will go together. Professionals will not only need to know how to use AI tools, but also how to judge whether the output makes sense. The real advantage will belong to people who can combine AI speed with human reasoning, context, and responsibility.

Skill 10: Data Ethics, Privacy, and Bias Awareness

Data literacy is not only about analyzing numbers. It is also about using data in a fair, safe, and responsible way. As companies collect more information about customers, employees, students, patients, and users, professionals need to understand the ethical side of data. Data ethics means using data in a way that respects people’s privacy, avoids harm, and supports fair decisions. Just because data is available does not always mean it should be used. Professionals must think carefully about how data is collected, stored, shared, and applied. Some important areas of data ethics include:

Bias is one of the biggest concerns in data use. Data may look neutral, but it can reflect past inequalities or incomplete information. For example, if a company uses past hiring data to train an AI recruitment tool, and past hiring was biased towards certain groups, the tool may repeat the same bias in future hiring decisions. Similarly, customer data may not represent all groups equally. If a survey is answered mostly by urban customers, the company may misunderstand the needs of rural customers. If healthcare data is collected mainly from one age group or income group, the results may not apply to everyone. Professionals should ask important ethical questions before using data:

• Does this data include personal or sensitive information?
• Was the data collected fairly?
• Do people know how their data is being used?
• Could this data harm any group?
• Are some groups missing from the dataset?
• Could the analysis create unfair decisions?
• Is the data stored securely?

In 2026, data privacy and responsible AI will become even more important because businesses, governments, and institutions are using data for major decisions. These decisions can affect jobs, loans, healthcare, education, insurance, and public services. A data-literate professional should therefore understand not only what the data says, but also whether it is being used responsibly. Good data use should be accurate, fair, transparent, and respectful of people’s rights. Data ethics is what separates smart data use from harmful data use.

Data Literacy Is the New Workplace Language

Data literacy is no longer an optional skill. In 2026, it has become one of the basic skills needed to work confidently in a digital, AI-driven, and data-heavy world. Almost every role now involves some form of data, whether it is reading a dashboard, understanding customer behaviour, tracking performance, preparing reports, using spreadsheets, or checking AI-generated insights.

The important point is that data literacy does not mean everyone must become a data scientist. It means that every professional should be able to understand data, ask the right questions, check its quality, interpret trends, explain insights, and use information responsibly. These skills help people make better decisions and avoid being misled by incomplete or poorly presented numbers.

The professionals who will stand out in 2026 are those who can combine technical awareness with critical thinking. They will not simply accept a number because it appears in a report. They will ask where the data came from, what it includes, what it excludes, and what action it supports. This ability to question, interpret, and communicate data will be valuable across industries.

The post Top 10 Data Literacy Skills Everyone Needs in 2026 appeared first on Vskills Blog.

But there is an important shift in this hiring wave. Companies are no longer looking at degrees alone. They are increasingly focusing on whether candidates have the skills needed for a changing technology landscape shaped by artificial intelligence, cloud computing, digital engineering, data, cybersecurity, and automation. TCS’s entry-level hiring pages highlight future-focused domains such as AI and data, cloud, cybersecurity, and enterprise solutions, while Infosys continues to emphasise digital capability-building and continuous learning as part of its talent strategy.

In this blog, we will look at what this hiring push really means, which skills TCS and Infosys appear to value most, and how freshers can prepare themselves to stand out in a crowded applicant pool. 

Why Fresher Hiring is Back in Focus?

India’s IT hiring cycle is beginning to regain strength, and freshers are once again becoming an important part of that recovery. Recent reporting suggests that India’s top IT services companies are planning a sharp rise in fresher hiring in FY26, signalling that firms are preparing for future delivery needs rather than relying only on lateral recruitment. This matters because fresher hiring is often one of the clearest signs that companies are feeling more confident about medium-term demand.

A major reason behind this shift is the changing nature of technology work itself. Companies are now building teams for an environment shaped by artificial intelligence, cloud platforms, cybersecurity, digital engineering, enterprise transformation, and data-led services. TCS’s India careers pages currently position the company around “AI-driven opportunities” and an “AI-ready future,” while Infosys presents its graduate hiring ecosystem as an “AI-first career” supported by AI-powered learning and development.

This means fresher hiring is not just about filling large numbers of entry-level seats. It is also about creating a pipeline of talent that can be trained for newer business areas. TCS’s official hiring pages highlight domains such as AI and data, cloud, cybersecurity, and other digital roles, while Infosys continues to stress future-ready skills, continuous learning, and digital capability-building for graduates.

For students and recent graduates, this creates a clear message: the hiring market may be opening up, but companies are looking for candidates who can fit into the next phase of IT services, not the old one. The comeback in fresher hiring is therefore closely tied to the rise of AI-led and digital-first business demand.

Top Technical Skills They Are Looking For

As TCS and Infosys expand fresher hiring, one thing is becoming very clear: companies are no longer looking only for candidates with a degree and basic subject knowledge. They want freshers who understand where the IT industry is heading and who have started building relevant technical skills accordingly.

The good part is that you do not need to master everything at once. But you do need to show that you are learning skills that match today’s technology environment. From AI and cloud to cybersecurity and programming, the focus is increasingly on practical, future-ready knowledge.

Here are some of the top technical skills that freshers should pay attention to.

Artificial Intelligence and Machine Learning

Artificial intelligence is no longer a niche area. It is now becoming part of how companies build products, automate tasks, improve customer support, and analyse business problems. That is why freshers with even a basic understanding of AI are likely to stand out.

What to learn?

• Basics of artificial intelligence and machine learning
• Common AI use cases in business
• Prompting, automation, and AI tools
• Introductory Python for AI-related tasks

Why it matter?

• AI is influencing almost every part of the technology industry
• Companies want talent that can work in AI-enabled environments
• Even non-specialist roles increasingly require AI awareness

Cloud Computing

Cloud has become a core part of modern IT services. Businesses now depend on cloud platforms for storage, applications, development, and digital operations. Freshers who understand cloud basics can appear more job-ready.

What to learn?

• Basics of cloud computing
• Difference between public, private, and hybrid cloud
• Introduction to AWS, Azure, or Google Cloud
• Cloud services, deployment, and storage concepts

Why it matters?

• Many IT projects now run on cloud-based infrastructure
• Cloud knowledge is useful across software, support, data, and enterprise roles
• It shows familiarity with modern business technology systems

Data Analytics and Data Handling

Data skills are becoming important even for entry-level roles. Companies want employees who can read information, work with datasets, and draw simple insights from numbers.

What to learn?

• Excel and advanced Excel
• SQL basics
• Data visualisation tools
• Introductory data analysis concepts
• Basics of Python for data work

Why it matters?

• Data supports decision-making in almost every business function
• Many digital roles require comfort with numbers and dashboards
• Strong data basics can open pathways into analytics and business intelligence roles

Cybersecurity

As businesses become more digital, security becomes more important. Companies need people who understand safe systems, secure practices, and the basics of protecting data and networks.

What to learn?

• Basics of cybersecurity
• Common security threats and vulnerabilities
• Safe coding and secure digital practices
• Network security fundamentals
• Basic awareness of security tools and frameworks

Why it matters?

• Cybersecurity is now essential across industries
• Security awareness is valuable even outside specialist roles
• It reflects seriousness about working in professional IT environments

Programming Fundamentals

No matter how much the industry evolves, programming remains one of the strongest foundations for a fresher entering IT. Even if a role is not purely development-focused, coding knowledge improves logic, problem-solving, and technical confidence.

What to learn?

• One or two core languages such as Python, Java, or C++
• Data structures and algorithms basics
• Object-oriented programming
• Problem-solving practice
• Basic debugging skills

Why it matters?

• Coding is still central to many fresher hiring processes
• Strong fundamentals make learning other tools easier
• It helps in interviews, assessments, and on-the-job training

Automation and Digital Tools

Companies are also looking for people who can work smarter, not just harder. Automation tools and digital workflows are becoming common across technology and business functions.

What to learn?

• Basic scripting
• Workflow automation tools
• Version control tools such as Git
• Collaboration platforms and project tools
• Basic understanding of APIs and integrations

Why it matters?

• Automation improves efficiency in real work environments
• It shows practical understanding beyond theory
• It prepares candidates for digital-first workplaces

What does this mean for Freshers?

The main message is simple: employers are looking for freshers who are ready to grow into the future of IT, not just fit into the past. You do not need to become an expert in all of these areas before applying. But having exposure to some of them can make your profile much stronger.

A smart approach would be to:

• Build strong programming basics first
• Add one future-focused skill such as AI, cloud, or data
• Work on small practical projects
• Mention tools and certifications clearly in your resume

That combination can make you look far more prepared than someone who depends only on their degree.

Non-Technical Skills That Still Matter a Lot

Technical skills may help freshers get noticed, but non-technical skills often determine who actually performs well in the hiring process and later at work. Companies such as TCS and Infosys are not just hiring people who can code or understand tools. They are also looking for candidates who can communicate clearly, solve problems, work with teams, and adapt to changing business needs.

In large IT companies, freshers usually work in team-based environments, client-facing situations, project deadlines, and fast-changing workflows. That is why non-technical skills continue to matter just as much as technical knowledge. In many cases, two candidates may have similar degrees and similar marks, but the one with better communication, confidence, and learning ability often leaves a stronger impression.

Here are some of the most important non-technical skills freshers should focus on.

Communication Skills

Good communication is one of the most valuable skills in any job. Freshers need to express ideas clearly, understand instructions properly, and interact professionally with team members and managers.

What it includes?

• Clear spoken communication
• Basic professional writing
• Email etiquette
• Explaining ideas in a simple way
• Active listening

Why it matters?

• IT jobs often require teamwork and regular reporting
• Candidates with good communication usually perform better in interviews and group discussions
• Clear communication reduces confusion and improves work quality

Problem-Solving Ability

Employers value candidates who can think through a situation instead of getting stuck at the first difficulty. Even in entry-level roles, freshers are expected to approach tasks logically and look for possible solutions.

What it includes?

• Analytical thinking
• Breaking problems into smaller parts
• Asking the right questions
• Using logic before jumping to conclusions
• Learning from mistakes

Why it matters?

• IT and digital roles often involve troubleshooting and decision-making
• Companies want employees who can handle challenges calmly
• Problem-solving shows maturity and practical thinking

Adaptability

The technology industry changes very quickly. Tools, platforms, project requirements, and client expectations can all shift in a short time. Freshers who are open to change usually adjust better in such environments.

What it includes?

• Willingness to learn new tools
• Comfort with changing tasks
• Openness to feedback
• Ability to work in new environments
• Flexibility in handling different types of work

Why it matters?

• Modern workplaces are constantly evolving
• Companies prefer candidates who can grow with change
• Adaptable employees are easier to train for future roles

Teamwork and Collaboration

Most fresher roles in large companies are not isolated roles. Employees work with team leads, managers, developers, testers, analysts, and sometimes even clients. This makes teamwork an essential skill.

What it includes?

• Respecting others’ ideas
• Working smoothly in groups
• Sharing responsibility
• Supporting team goals
• Handling disagreements professionally

Why it matters?

• Most corporate work is team-based
• Good teamwork creates smoother project execution
• Employers look for people who can contribute without creating friction

Learning Mindset

One of the biggest things employers look for in freshers is the ability to learn. Companies know that not every graduate will come fully prepared. What they want is someone who is curious, trainable, and willing to improve continuously.

What it includes?

• Curiosity
• Self-learning habits
• Interest in improving skills
• Willingness to accept guidance
• Consistency in learning

Why it matters?

• Freshers usually grow through training and project exposure
• A strong learning mindset helps candidates stay relevant
• It shows long-term potential rather than just short-term preparation

Professionalism and Work Discipline

Many candidates focus only on technical preparation and forget that companies also notice attitude and behaviour. Professionalism can make a big difference from the interview stage itself.

What it includes?

• Punctuality
• Polite behaviour
• Responsibility
• Respect for deadlines
• Seriousness towards work

Why it matters?

• Companies want dependable employees
• Professional behaviour creates trust
• It reflects readiness for a corporate environment

What Freshers should understand?

A lot of students think non-technical skills are secondary, but that is not true. In reality, these skills often shape how confidently a candidate presents themselves and how effectively they work after getting hired. Technical knowledge may help you clear assessments, but communication, adaptability, teamwork, and professionalism often help you succeed in the workplace.

A smart fresher should therefore focus on both sides:

• Build technical skills for the role
• Build non-technical skills for long-term career growth

That combination is what makes a candidate truly job-ready.

Degrees Alone Are Not Enough Anymore

For many years, students believed that getting the right degree was the main step towards getting hired in a top IT company. A B.Tech, BCA, MCA, or similar qualification was often seen as the key requirement, and once that box was checked, the next expectation was that the company would provide all the training needed after recruitment. That mindset is changing.

Today, companies such as TCS and Infosys are hiring in a much more competitive and skill-focused environment. A degree still matters because it gives candidates academic eligibility and foundational knowledge, but it no longer guarantees that a candidate will stand out. What increasingly makes the difference is whether a fresher has built skills beyond the classroom.

This shift has happened because the workplace itself has changed. The IT industry now operates in an environment shaped by artificial intelligence, cloud computing, automation, analytics, cybersecurity, and digital transformation. Companies need employees who can adapt to these changes quickly. As a result, they are paying closer attention to practical readiness, learning ability, and skill application rather than only academic credentials.

That is why two candidates with the same degree may be viewed very differently. One may simply hold the qualification, while the other may also have hands-on projects, certifications, coding practice, cloud exposure, data skills, or familiarity with AI tools. Naturally, the second candidate appears more job-ready.

A degree gives you entry, not an advantage. A college degree can help you meet the basic eligibility criteria for hiring, but it is often only the starting point. It gets your profile considered, but it does not automatically make you the strongest candidate.

Why this matters?

• Many applicants may have similar degrees and marks
• Recruiters need stronger ways to differentiate candidates
• Skills, projects, and practical exposure help you stand out

Companies now prefer proof of skills

Employers increasingly value candidates who can demonstrate what they know. This does not always mean work experience. For freshers, it can mean projects, internships, certifications, coding profiles, portfolios, or even well-explained academic work.

What can count as proof?

• Small technical projects
• Internship experience
• Online certifications
• GitHub or portfolio work
• Participation in coding or tech challenges

College learning is often not enough by itself

Many students realise that their college syllabus does not fully match the tools and technologies being used in industry today. This is why self-learning has become so important.

What students often need to learn outside the classroom:

• Modern programming tools
• Cloud and digital platforms
• AI and automation basics
• Practical problem-solving
• Industry-relevant software and workflows

Learning attitude matters more than ever

Companies understand that freshers are still at the beginning of their careers. They do not expect perfection. But they do expect willingness to learn, improve, and keep up with change.

What employers want to see?

• Curiosity about new technology
• Effort beyond the syllabus
• Openness to feedback
• Ability to learn quickly
• Interest in continuous upskilling

What Freshers Should Take From This

The message is not that degrees have no value. They still matter. But in the current hiring environment, a degree alone is not enough to make a candidate job-ready. Employers are looking for people who combine academic knowledge with practical skills, curiosity, and the ability to grow.

For freshers, this is actually a useful shift. It means your future does not depend only on your college name or your marks. You can strengthen your profile by learning relevant tools, doing projects, and building skills that reflect what the industry needs today. In simple terms, the degree may open the door, but your skills are what help you walk through it.

Here are some of the most important things freshers should start learning right now.

Strengthen your programming basics

Programming remains one of the strongest foundations for anyone entering the IT industry. Even if you later move into cloud, data, AI, or cybersecurity, basic coding skills will continue to help you.

What to focus on?

• One core language such as Python, Java, or C++
• Data structures and algorithms basics
• Logical problem-solving
• Basic debugging and code understanding

Why should this come first?

• It builds technical confidence
• It helps in hiring tests and interviews
• It makes advanced tools easier to learn later

Learn one high-demand domain

Instead of trying to learn every trending skill, freshers should choose one area that is currently in demand and build beginner-level comfort in it.

Some good options include:

• Cloud computing
• Data analytics
• Artificial intelligence basics
• Cybersecurity fundamentals
• Automation tools

Why this matters?

• It gives direction to your preparation
• It makes your profile more focused
• It shows recruiters that you are learning with purpose

Build small practical projects

Learning theory is not enough anymore. Even freshers need to show some practical application of what they know. Small projects can make a big difference in how your profile looks.

Examples of useful beginner projects:

• A simple Python-based application
• A basic data dashboard in Excel or Power BI
• A small cloud-based deployment project
• A chatbot or AI tool experiment
• A portfolio website or GitHub repository

Why projects matter?

• They prove that you can apply concepts
• They make your resume stronger
• They give you real examples to talk about in interviews

Become comfortable with digital tools

Modern workplaces expect freshers to be comfortable with more than just textbooks and assignments. You should know how to work with commonly used digital and collaborative tools.

Useful tools to start with:

• Git and GitHub
• Excel
• Power BI or Tableau basics
• Collaboration tools such as Teams or similar platforms
• Basic documentation and presentation tools

Why this matters?

• It reflects workplace readiness
• It helps you adapt faster after joining
• It shows practical awareness of professional environments

Improve communication alongside technical learning

A lot of students focus only on technical preparation and ignore how they speak, write, and present themselves. That can become a weakness during interviews and group discussions.

What to work on?

• Speaking clearly and confidently
• Writing basic professional emails
• Explaining projects in a simple way
• Answering interview questions with structure
• Listening carefully and responding thoughtfully

Why this matters?

• Good communication improves interview performance
• It helps you present your skills better
• It makes you appear more confident and job-ready

Stay consistent with self-learning

The biggest advantage a fresher can build today is the habit of continuous learning. Technology changes quickly, and the students who keep learning regularly are usually the ones who stay ahead.

How to do this?

• Spend time each week learning one skill
• Follow a simple learning schedule
• Take beginner-friendly online courses
• Revise and practice regularly
• Keep updating your resume with new skills and projects

Why this matters?

• Consistency creates stronger results than random effort
• It helps you stay relevant in a changing industry
• It shows seriousness and discipline

A Simple Way to Approach It

If you are confused about where to begin, keep it simple.

Start by:

• Building strong programming basics
• Choosing one future-focused skill area
• Creating two or three small projects
• Practising communication and interview skills
• Learning regularly instead of preparing only at the last minute

This kind of approach can make a fresher profile much stronger over time. The companies hiring today are not only looking for degrees. They are looking for signs of readiness, curiosity, and effort. That means what you start learning now can directly shape the opportunities you are able to access later.

Best Roles Freshers Can Target in TCS and Infosys

When students hear that large IT companies are hiring freshers, they often imagine only one kind of job: a general software role. But the reality is much broader now. Companies such as TCS and Infosys are hiring across multiple technology and business functions, which means freshers have more than one possible entry route. This is useful because not every student has the same strengths, interests, or learning background.

Some freshers may be stronger in coding. Some may be more interested in data. Others may prefer cloud, cybersecurity, testing, or enterprise support roles. The important thing is to understand that large IT firms do not hire only for one fixed profile. They build teams across many areas, and that creates different opportunities for freshers who are willing to prepare well.

Here are some of the best roles freshers can realistically target.

Software Developer or Software Engineer

This remains one of the most common entry-level roles for freshers. It usually involves coding, debugging, testing, maintaining applications, and supporting development teams.

Who this suits?

• Students with strong programming interest
• Candidates who enjoy logic and problem-solving
• Those preparing in languages such as Java, Python, or C++

Why it is a good option?

• It gives a strong technical foundation
• It opens pathways into many advanced roles later
• It remains one of the most recognised fresher entry routes

Data Analyst or Data Support Roles

As data becomes central to business decisions, fresher-level roles related to data are also becoming more important. These roles may involve handling datasets, preparing reports, creating dashboards, or supporting analytics teams.

Who this suits?

• Students comfortable with numbers and patterns
• Candidates interested in Excel, SQL, and visualisation tools
• Those who want to move towards analytics or business intelligence

Why it is a good option?

• Data skills are in demand across industries
• It combines technical and business understanding
• It can lead to strong future growth in analytics fields

Cloud and Infrastructure Support Roles

Cloud has become a major part of IT service delivery, so freshers with cloud basics can target entry-level roles linked to infrastructure, deployment, and support.

Who this suits?

• Candidates interested in how digital systems are hosted and managed
• Students learning AWS, Azure, or Google Cloud basics
• Those who want a role connected to modern enterprise systems

Why it is a good option?

• Cloud knowledge is increasingly valuable
• It aligns well with current business demand
• It creates opportunities in both technical support and advanced cloud careers

Cybersecurity Roles

Cybersecurity is becoming important across almost every digital business environment. While specialist security roles may require deeper expertise, freshers can still begin with support or junior-level cybersecurity pathways.

Who this suits?

• Students interested in secure systems and digital safety
• Candidates who enjoy investigation, systems thinking, and technical awareness
• Those learning basics of networks, threats, and security principles

Why it is a good option?

• Security demand is growing continuously
• It is a high-value domain for long-term career building
• Even foundational knowledge in this area can help a fresher stand out

Testing and Quality Assurance Roles

Not every strong IT career starts in software development. Testing and quality assurance roles are also important and can be a good entry route for freshers.

Who this suits?

• Candidates who are detail-oriented
• Students who enjoy identifying errors and improving quality
• Those interested in software performance and reliability

Why it is a good option?

• It helps develop a strong understanding of software systems
• It can later lead into automation testing and other specialised roles
• It remains a practical entry point into the IT industry

Business and Enterprise Technology Roles

Large companies also hire for roles connected to enterprise systems, digital operations, consulting support, process transformation, and technology-enabled business services.

Who this suits?

• Students who are interested in both business and technology
• Candidates with communication and analytical strengths
• Those who may not want purely coding-based roles

Why it is a good option?

• It combines technical awareness with business understanding
• It can suit candidates from mixed academic backgrounds
• It offers growth into consulting, business analysis, and enterprise solution roles

How Freshers Should Think About These Roles

The best role is not simply the one that sounds the most impressive. It is the one that matches your skills, interests, and preparation. A fresher who enjoys coding and has practised programming may be better suited to software roles. Someone strong in numbers and dashboards may be more suited to data roles. Someone interested in systems and digital infrastructure may find cloud or cybersecurity more meaningful.

So instead of applying everywhere without direction, freshers should try to do three things:

• Understand the broad types of roles available
• Identify which one suits their strengths
• Prepare skills and projects around that path

This approach makes preparation more focused and increases the chance of standing out in a competitive hiring process. In the current hiring environment, the biggest advantage is not just applying early. It is applying with clarity about the role you are aiming for and the skills that support it.

How to Stand Out From Other Applicants

When large companies such as TCS and Infosys announce big fresher hiring plans, it naturally creates excitement among students and recent graduates. But it also creates intense competition. Thousands of candidates may apply for the same opportunities, and many of them may have similar degrees, similar marks, and similar resumes. In that kind of environment, standing out becomes extremely important.

The good thing is that standing out does not always mean being extraordinary. It often means being better prepared, more focused, and more intentional than the average applicant. Small efforts, when done properly, can create a much stronger impression than most students realise.

Here are some of the most effective ways freshers can stand out.

Build a Resume Around Skills, Not Just Qualifications

Many fresher resumes look almost identical. They list education, marks, and a few generic strengths, but they do not clearly show what the candidate can actually do. A stronger resume is one that reflects practical skills and relevant effort.

What to include?

• Technical skills you genuinely know
• Small projects you have completed
• Certifications relevant to the role
• Internship experience, if any
• Tools and platforms you have used

Why it helps?

• It shows more than academic eligibility
• It gives recruiters a clearer picture of your strengths
• It makes your profile look more job-ready

Work on Practical Projects

Projects are one of the best ways to show initiative. Even a small project can make a fresher profile look much stronger because it proves that learning has moved beyond theory.

What kind of projects can help?

• A small coding application
• A dashboard using Excel or Power BI
• A beginner-level AI or chatbot project
• A cloud deployment exercise
• A simple website or portfolio

Why it helps?

• Projects show real application of knowledge
• They give you something meaningful to discuss in interviews
• They make your learning more visible and credible

Choose One Strong Area of Focus

A common mistake freshers make is trying to mention every possible trending skill. This can make the profile look scattered. It is usually better to build strength in one main area and then support it with basics in other areas.

For example:

• Strong programming plus AI basics
• Data skills plus Excel and SQL
• Cloud basics plus scripting
• Cybersecurity fundamentals plus networking basics

Why it help?

• A focused profile looks more serious
• It makes your preparation more strategic
• Recruiters can more easily understand where you fit

Improve Your Communication and Interview Readiness

A candidate may have good technical knowledge but still lose an opportunity because they cannot explain themselves well. Communication plays a major role in hiring, especially for freshers.

What to practise?

• Introducing yourself confidently
• Explaining projects clearly
• Answering common interview questions
• Speaking in a structured and professional way
• Listening carefully before responding

Why it helps?

• Good communication improves first impressions
• It helps recruiters see your confidence and clarity
• It allows you to present your skills more effectively

Show Proof of Continuous Learning

Companies value freshers who are actively learning, especially in a fast-changing technology environment. Showing that you are trying to improve regularly can make a big difference.

How to show this?

• Complete relevant online courses
• Earn beginner-friendly certifications
• Update your LinkedIn profile
• Maintain a GitHub profile, if relevant
• Keep learning new tools step by step

Why it helps?

• It reflects seriousness and discipline
• It shows that you are not depending only on your degree
• It signals long-term potential

Apply With Clarity, Not Randomly

Many students apply to every role they see without understanding whether the role matches their skills. This usually weakens preparation and reduces confidence during the hiring process.

A better approach is to:

• Understand which roles suit your strengths
• Tailor your resume to that direction
• Learn the skills most relevant to that role
• Prepare role-specific interview answers

Why it helps?

• Focus improves quality of preparation
• You appear more aligned with the opportunity
• It increases your chances of performing better in interviews

What Freshers Should Remember

Standing out is not about being perfect. It is about showing effort, direction, and readiness. Recruiters know that freshers are still learning. They do not expect years of experience. But they do notice which candidates have taken their preparation seriously.

In a competitive hiring market, the students who stand out are usually the ones who:

• Learn beyond the syllabus
• Build small but real projects
• Communicate clearly
• Stay consistent in upskilling
• Apply with purpose

That is what turns a fresher from just another applicant into a candidate worth noticing.

Expert Corner

In the end, the hiring push from TCS and Infosys is encouraging news for freshers, but it also comes with a clear message: opportunities are growing, yet companies are looking for candidates who bring more than just a degree. Technical skills, communication, adaptability, practical projects, and a willingness to keep learning are becoming the real differentiators in today’s IT job market. For students and graduates, this means the goal should not only be to apply widely, but to prepare smartly and build a profile that matches the future of the industry.

The post TCS, Infosys hiring 82,000 freshers — What skills they are looking for? appeared first on Vskills Blog.

However, “high-paying technology” does not mean every job in that area pays high immediately. The highest salaries usually go to roles that combine strong fundamentals with rare, applied skills. In simple terms, companies pay more when you can build something that works, maintain it in real conditions, and explain your decisions clearly. That is why this blog is not only a list of technologies. It is a guide to the highest-paying job roles inside each technology, the core skills you need to enter the field, and how you can start building a strong profile even if you are a fresher.

In this blog, you will find the top 10 future technologies shaping 2026 and the highest-paying jobs linked to them. For each technology, you will see what makes it valuable, which roles pay the most, what skills to learn first, and what kind of projects or proof of work can help you get shortlisted. By the end, you will be able to choose one technology path confidently instead of feeling overwhelmed by too many options.

Selection of Future Technologies 2026

This list is not based on hype or “trending” keywords. It is based on what typically drives higher salaries in technology markets: roles that sit close to business-critical outcomes, have a shortage of job-ready talent, and require skills that are hard to copy quickly. we selected these 10 technologies using four filters:

1. Demand and longevity: These technologies are not one-season trends. They are long-cycle areas where companies invest for years because they shape core infrastructure, productivity, and competitiveness.
2. Salary ceiling and career progression: Each technology has roles with strong earning potential at mid-to-senior levels, not only at entry level. The aim is to highlight fields where you can grow into high-paying roles over time.
3. Cross-industry relevance: Technologies that apply across multiple sectors (finance, healthcare, retail, manufacturing, government, SaaS) usually offer more stable opportunities than niche areas.
4. Clear skill roadmap and proof of work: A technology is more practical for readers if you can build measurable skills and create portfolio projects to get shortlisted. Wherever possible, the list favours technologies with learnable entry points.

One important note: salaries vary widely by country, company type, location, and experience level. “Highest paying” here refers to technologies with strong salary potential, especially when you build real skills and proof of work, not just certifications.

Comparison Future Technologies and High-Paying Roles 2026

Let’s now look at each career option in detail!

(1) Generative AI and LLM Applications

Generative AI is the technology behind tools that can create text, code, images, and structured outputs. In workplaces, it is being used to automate drafting, summarising, customer support, internal knowledge search, analytics storytelling, and even parts of software development. The most in-demand area in 2026 is not only “using AI tools,” but building useful applications around them: chatbots grounded in company documents, workflow assistants, and AI features inside products.

Why it is high-paying in 2026?

This field pays well because it sits directly on productivity and product differentiation. Companies are competing on who can ship AI features faster, keep them reliable, and control risks like hallucinations and data leakage. People who can build GenAI apps end-to-end (prompt control + RAG + evaluation + deployment) are still relatively scarce compared to demand.

Highest-paying job roles

• LLM Application Developer / GenAI Developer
• AI Engineer (LLM-focused)
• Conversational AI Engineer
• AI Product Specialist / AI Solutions Engineer
• LLMOps / AI Platform Engineer (for scaling and reliability)

Core skills to learn (in the right order)

• Python basics + working with APIs
• Prompting for control and structured output (JSON, checklists, templates)
• Embeddings + semantic search
• RAG (retrieval-augmented generation) to reduce hallucinations
• App building (Streamlit or FastAPI)
• Evaluation basics (test prompts, pass/fail checks) + logging

Portfolio projects that get you shortlisted

• “Chat with your notes” RAG app that answers questions from PDFs with citations
• Customer support assistant that drafts replies using policy snippets and tags tickets
• Meeting notes to action items tool that outputs structured JSON with priorities

Career path (simple)

Intern/Junior GenAI developer → GenAI developer → AI engineer/LLMOps specialist → lead/architect roles or AI product leadership

(2) Cybersecurity and Cloud Security

Cybersecurity is about protecting systems, data, and users from attacks, misuse, and failures. Cloud security focuses specifically on securing cloud infrastructure and identities (who can access what). In 2026, this matters even more because more business systems are online, remote work remains common, and AI increases both productivity and security risk (faster phishing, faster exploitation, faster misinformation).

Why it is high-paying in 2026?

Security is expensive to get wrong. A single breach can cause financial loss, downtime, legal risk, and reputational damage. As companies move more workloads to the cloud, the attack surface expands, and security roles with strong cloud and identity skills become especially valuable. This combination of high risk and talent shortage pushes salaries up.

Highest-paying job roles

• Cloud Security Engineer
• Security Architect (senior path)
• Incident Response / Threat Hunter (experienced)
• Application Security Engineer
• GRC Specialist (governance, risk, compliance) in regulated sectors

Core skills to learn

• Networking fundamentals (IP, DNS, ports, basic troubleshooting)
• Security fundamentals (threats, vulnerabilities, controls)
• Identity and Access Management (IAM) concepts
• Security operations basics (logs, monitoring, incident response)
• Cloud basics (AWS/Azure fundamentals) + cloud security basics
• Security mindset: least privilege, segmentation, backups, secure configuration

Portfolio projects that get you shortlisted

• A home-lab style security write-up: common attacks + how you would detect them (beginner-friendly)
• A simple incident report template + mock incident walkthrough (what happened, impact, action taken)
• A cloud IAM checklist: secure access rules for a sample startup setup (users, roles, permissions)

Career path (simple)

SOC/Support → Security analyst → Cloud security / AppSec specialization → Architect / Lead roles

(3) Cloud Computing and Platform Engineering

Cloud computing is the backbone that runs modern apps, websites, data systems, and AI tools. Platform engineering is the layer that makes cloud infrastructure reliable and easy for teams to use. In simple terms, platform teams build the “internal cloud platform” so developers and analysts can deploy and run systems safely, quickly, and at controlled cost.

Why it is high-paying in 2026?

Cloud is not optional anymore. Companies want speed, reliability, and cost control. The people who can set up infrastructure properly, automate deployments, handle outages, and optimise performance become extremely valuable. Platform engineering and SRE (site reliability engineering) roles pay well because they sit close to uptime and business continuity.

Highest-paying job roles (examples)

• Cloud Engineer (AWS/Azure/GCP)
• DevOps Engineer
• Platform Engineer
• Site Reliability Engineer (SRE)
• Cloud Solutions Architect (senior path)

Core skills to learn (in the right order)

• Linux basics (commands, permissions, processes)
• Networking fundamentals (DNS, IP, ports, load balancers)
• One cloud platform fundamentals (AWS or Azure)
• Containers: Docker basics
• CI/CD basics (how deployments are automated)
• Monitoring and logging basics (detect issues early)
• Infrastructure-as-Code basics (Terraform style thinking, later)

Portfolio projects that get you shortlisted

• Deploy a simple web app on cloud and set up monitoring (even basic)
• Build a CI/CD pipeline that auto-deploys from GitHub to a test environment
• Create an “incident runbook” for a sample app: what to check when things break

Career path (simple)

Junior cloud/devops → DevOps/Platform engineer → SRE/Lead engineer → Architect roles

(4) Data Science, Analytics Engineering, and Decision Intelligence

This is the technology stack that turns raw data into business decisions. Analytics engineering sits between data and dashboards: it focuses on clean, reliable datasets and models that analysts and teams can use repeatedly. Decision intelligence is the broader idea of using data + AI + experimentation to guide strategy and operational decisions.

Why it is high-paying in 2026?

Companies do not pay for “data” in general. They pay for outcomes: better decisions, better forecasting, faster reporting, and fewer mistakes. People who can build reliable pipelines, explain insights clearly, and influence decisions often earn more than people who only create charts.

Highest-paying job roles

• Analytics Engineer
• Data Scientist (mid to senior)
• BI Engineer / Analytics Developer
• Data Product Analyst (advanced)
• Applied Scientist (senior path)

Core skills to learn (in the right order)

• Excel/Google Sheets for fundamentals (cleaning, pivots, logic)
• SQL (non-negotiable for most roles)
• Dashboarding (Power BI or Tableau)
• Python basics for analysis (pandas, basic plotting)
• Statistics basics for business questions (correlation, testing intuition)
• Data storytelling: turning numbers into decisions

Portfolio projects that get you shortlisted

• One KPI dashboard + a 1-page insight memo (what changed, why, what to do)
• A forecasting mini project (even simple) with assumptions and error checks
• A “data cleaning + modelling” project: messy dataset → clean tables → dashboard

Career path (simple)

Junior analyst → Analyst/BI developer → Analytics engineer/Data scientist → Lead roles or specialised expert roles

(5) Robotics and Automation (Industrial + Service)

Robotics and automation focus on using machines, sensors, and control systems to perform tasks with speed, precision, and consistency. This includes industrial robots in manufacturing, automation in warehouses and logistics, and service robots in areas like healthcare and hospitality. Automation also includes PLC/SCADA systems that control and monitor industrial processes.

Why it is high-paying in 2026?

Automation directly improves productivity and reduces operational errors. Industries that run large physical operations pay well for engineers who can design, deploy, maintain, and optimise automation systems because downtime is expensive and efficiency gains translate into measurable cost savings.

Highest-paying job roles

• Robotics Engineer
• Automation Engineer
• Mechatronics Engineer
• PLC/SCADA Engineer
• Controls Engineer

Core skills to learn (in the right order)

Control systems basics, sensors and actuators, basic electronics, programming fundamentals for automation (depends on role), industrial process understanding, safety standards and troubleshooting. If you are going into robotics software, learn simulation and robotics frameworks later, but start with fundamentals first.

Portfolio projects that get you shortlisted

A simple automation workflow simulation or demo (even in a basic environment), a small sensor-based prototype concept with clear documentation, a case study write-up explaining how you would automate a process end-to-end (inputs, sensors, control logic, outputs, safety checks).

Career path

Technician/Junior engineer → Automation/Controls engineer → Robotics specialist or lead engineer roles

(6) Semiconductors and Chip Design Ecosystem

Semiconductors are the foundation of modern computing. Chip design includes designing digital circuits, verifying that designs work correctly, and implementing them physically on silicon. The ecosystem includes VLSI design, verification, physical design, DFT (design for test), and hardware validation.

Why it is high-paying in 2026?

This field has a high skill barrier and long learning curve, which keeps talent supply limited. Chip design roles also sit in strategic industries where precision and expertise are crucial. The combination of complexity, global competition, and specialised tools pushes salaries up, especially as you gain experience.

Highest-paying job roles

• VLSI Design Engineer
• Verification Engineer
• Physical Design Engineer
• DFT Engineer
• Hardware Validation Engineer

Core skills to learn

• Digital logic and computer architecture basics, Verilog/SystemVerilog, timing and constraints fundamentals
• Verification concepts (testbenches, assertions)
• Clear understanding of the chip design flow. Tool exposure matters, but strong fundamentals matter more at entry level.

Portfolio projects that get you shortlisted

Small RTL designs (simple CPU components, controllers, finite state machines), verification testbench examples, a documented mini project showing your design → verification approach and how you tested correctness.

Career path

Intern/Junior VLSI → Design/Verification engineer → Specialist roles → Lead/Architect roles in chip programs

(7) Internet of Things (IoT) and Edge AI

IoT is about connecting physical devices (sensors, machines, wearables, smart meters) to the internet so they can collect data, communicate, and be controlled remotely. Edge AI adds intelligence at or near the device, meaning the AI runs locally (or partly locally) instead of sending everything to the cloud. This is useful when you need low latency, better privacy, or reliable performance even with weak connectivity.

Why it is high-paying in 2026?

IoT pays well because it combines multiple skill areas: hardware, networking, software, and security. Edge AI increases value because it enables smarter systems in manufacturing, energy, mobility, healthcare, and smart infrastructure. Engineers who can make devices reliable, secure, and scalable are hard to find, so salaries rise with experience.

Highest-paying job roles

• Embedded Systems Engineer
• IoT Developer
• Edge AI Engineer
• IoT Solutions Architect
• Firmware Engineer

Core skills to learn

• Embedded fundamentals (microcontrollers, firmware basics), sensors and communication protocols, basic networking concepts, data handling and messaging (how devices send data), and security basics for devices.
• If you want Edge AI, add fundamentals of ML deployment and model efficiency later.

Portfolio projects that get you shortlisted

A sensor-based project that sends data to a dashboard (even a basic one), a device monitoring workflow with alerts (threshold-based), a short write-up on how you would secure an IoT setup (authentication, updates, encryption, access control).

Career path (simple)

Junior embedded/IoT → Embedded/IoT engineer → Edge AI or IoT architect track

(8) AR/VR/XR and Spatial Computing

AR (Augmented Reality) overlays digital objects on the real world. VR (Virtual Reality) creates a fully immersive virtual environment. XR is the umbrella term that includes AR, VR, and mixed reality. Spatial computing is the broader direction where digital content interacts with physical space, enabling training simulations, product visualisation, remote assistance, and immersive experiences.

Why it is high-paying in 2026?

This field pays well when it is linked to high-value applications like enterprise training, industrial simulation, medical training, defence simulation, design reviews, and immersive retail. The skill mix is specialised: 3D workflows, real-time performance, interaction design, and engine knowledge, so strong talent commands higher pay.

Highest-paying job roles

• XR Developer
• Unity/Unreal Developer (XR focus)
• Simulation Developer
• Technical Artist
• 3D Interaction Designer

Core skills to learn (in the right order)

Pick a platform (Unity or Unreal), learn 3D basics (models, lighting, materials), learn interaction and UI in 3D environments, and develop performance thinking (frame rate, optimisation). If you are design-oriented, strengthen storytelling and user experience for immersive environments.

Portfolio projects that get you shortlisted

Two small XR demos (for example: virtual showroom, training simulation, interactive learning module), a short demo reel (screen recording), a documented breakdown explaining what you built and what you optimised.

Career path (simple)

Junior XR dev/3D generalist → XR developer → Simulation lead or specialist roles

(9) Blockchain and Web3 Infrastructure (Selective and Use-Case Driven)

Blockchain is a type of distributed database where records are stored in a way that is hard to tamper with, and transactions can be verified without a single central authority. In practical career terms, the most serious opportunities are usually in infrastructure, enterprise pilots, payments, identity, tokenisation, and security auditing, not in speculative “quick money” projects.

Why it is high-paying in 2026?

This space pays well in pockets because it requires specialised skills (smart contracts, security auditing, protocol understanding) and the cost of mistakes is high. A small bug in a smart contract can cause large financial loss. That is why security-focused blockchain talent often commands higher pay than general developers in this niche.

Highest-paying job roles

• Smart Contract Developer
• Blockchain Security Auditor
• Protocol Engineer
• Web3 Backend Engineer
• Cryptography Engineer (advanced)

Core skills to learn (in the right order)

Programming fundamentals first, then smart contract development basics (Solidity if you are on Ethereum-type ecosystems), testing and debugging, security mindset (common vulnerabilities), and basic cryptography concepts. If you are serious about this track, security and testing discipline are not optional.

Portfolio projects that get you shortlisted

A simple smart contract project with a full test suite, a mini audit-style report explaining risks and fixes for a sample contract, a small dApp demo that shows end-to-end thinking (contract + frontend + testing + documentation).

Career path (simple)

Junior smart contract dev → Smart contract engineer or auditor → Specialist security/protocol roles

(10) CleanTech and Energy Tech (EV, Batteries, Hydrogen, Grid Tech)

CleanTech and energy tech cover technologies that power the energy transition: electric vehicles and charging networks, battery systems, hydrogen value chains, renewable energy integration, smart grids, and energy storage. This is not only an engineering field. It also includes high-paying analytics and systems roles because energy systems are complex and investment-heavy.

Why it is high-paying in 2026?

Energy is becoming a strategic sector globally. Projects are large, infrastructure-heavy, and regulated, which creates demand for specialised talent across engineering, systems design, safety, and optimisation. Pay rises as you gain domain depth because energy systems involve long timelines, high reliability requirements, and high cost of failure.

Highest-paying job roles

• Battery Engineer
• EV Systems Engineer
• Power Systems Engineer
• Grid Integration Engineer
• Energy Analyst (tech + modelling)
• Energy Product Manager (experienced)

Core skills to learn (in the right order)

• Pick your lane first: engineering lane or analytics lane.
• Engineering lane: fundamentals of power systems, electronics basics, safety, and system design thinking.
• Analytics lane: strong data skills (Excel/SQL/Python), energy metrics, basic modelling, and the ability to translate analysis into operational or investment decisions.
• If you target EV/batteries, add basics of battery performance concepts and system-level trade-offs.

Portfolio projects that get you shortlisted

An energy cost and performance model case study (simple but clearly documented), an EV charging rollout analysis for a city (assumptions + sizing + constraints), a grid reliability or renewable integration explainer with a small dataset and visualisations.

Career path (simple)

Graduate/junior engineer or analyst → Domain specialist → Systems lead/architect roles or strategy/product roles in energy tech

How to Choose the Right Technology for You?

If you like building with code and shipping digital products

• Generative AI, Cloud/Platform Engineering, Data/Analytics Engineering, Blockchain (only if you are comfortable with security and depth)

If you like security, investigation, and risk reduction

• Cybersecurity and Cloud Security (strong long-term path with high ceiling)

If you like hardware, systems, and deep technical specialisation

• Semiconductors, IoT/Embedded, Robotics and Automation, Energy Systems

If you like creativity plus technology

• AR/VR/XR, Generative AI (creative workflows), content and product design roles around AI

If you like numbers, business decisions, and measurable impact

• Data/Analytics, Energy analytics, AI product/solutions roles, cloud cost and operations paths

Skills that Increase Salary 2026

No matter which future technology you choose, the highest-paying roles tend to reward the same set of “career multiplier” skills. These skills make you valuable because they help you deliver outcomes reliably, not just knowledge.

1) Strong fundamentals (the real salary foundation)

• If you are in software-heavy tracks: Python (or one language), APIs, data handling, basic system thinking
• If you are in cloud/security: Linux, networking basics, identity concepts
• If you are in data: SQL + clean reporting logic
• If you are in hardware/energy/robotics: core engineering fundamentals and the ability to reason through systems
  People who skip fundamentals plateau early. People who build fundamentals get faster promotions and better offers.

2) Proof of work (portfolio beats claims)

High-paying hiring processes in 2026 increasingly use work samples. A portfolio shows you can deliver.

• 2–4 strong projects are usually better than 10 weak projects
• Each project should have a README, screenshots, what you built, what you improved, and what you would do next

3) Reliability thinking (how your work behaves in real life)

This is what separates average candidates from high-paid ones. Employers want people who think about:

• performance and speed
• failure cases and edge cases
• security and privacy
• cost and maintainability

Even if you are a fresher, showing this mindset in interviews raises your value.

4) Communication and documentation (underrated, but highly paid)

High-paying teams are cross-functional. If you can write clearly, explain trade-offs, and document decisions, you reduce friction and improve execution speed. This matters especially in remote and global teams.

5) Business awareness (why the technology exists)

Knowing the “why” boosts salary because it helps you prioritise correctly. Examples:

• In cloud, cost optimisation and reliability affect profit and customer retention
• In cybersecurity, risk reduction protects revenue and trust
• In data: insights influence decisions and strategy
• In GenAI, reliability and adoption determine real value capture

6) Learning speed and adaptability

Tools change quickly, especially in GenAI and cloud. High earners are not those who memorise tools. They are those who can learn a new tool fast because their fundamentals are strong and they build consistently.

Learning Pathways 2026

Path A: Non-Tech Students (High-Paying Tech-Adjacent Roles)

If you are not from a technical background, the fastest route is to choose roles that sit close to technology outcomes but do not require heavy coding on day one. Your advantage is communication, coordination, documentation, analysis, and process discipline.

Best technology areas to start with

• Generative AI (workflow use cases)
• Data and Analytics (Excel → SQL)
• Cybersecurity (GRC/compliance track)
• Cloud fundamentals (business + ops angle)
• Energy analytics (if you like numbers and policy/business)

Skill plan (8–12 weeks)

• Week 1–2: Excel/Sheets + communication + documentation basics
• Week 3–4: Choose one track
  • Data track: SQL basics + dashboarding
  • GenAI track: prompting + structured outputs + basic API understanding
  • Security track: security fundamentals + compliance and risk basics
• Week 5–8: Build 2 portfolio samples aligned to the role
• Week 9–12: Apply with a role-specific resume + case-study style portfolio

Target job roles

• AI workflow specialist
• Junior Analyst
• Operations Analyst
• BI Intern
• GRC analyst (entry)
• Product analyst intern
• Customer success/solutions associate (tech products).

Path B: Freshers (Job-Ready in 3–6 Months With Projects)

If you are a fresher and can commit consistent time, pick one technology and build depth. High-paying tracks for freshers usually require projects, not only certificates.

Pick one based on your interest

• If you like building apps: Generative AI or Cloud/DevOps
• If you like risk and investigation: Cybersecurity
• If you like numbers and insight: Data/Analytics
• If you like hardware: Semiconductors or IoT/Embedded
• If you like creative tech: XR

Skill plan (12–24 weeks)

• Month 1: Fundamentals (Python or SQL or Linux + networking depending on track)
• Month 2: Build 2 small projects (portfolio-ready)
• Month 3: Build 1 stronger project + documentation + interview prep
• Month 4–6: Internships, freelancing, or entry roles + continue building

Target job roles

• Junior data analyst/BI, cloud support → junior cloud engineer, SOC analyst trainee, junior QA, junior GenAI developer (if projects are strong), embedded intern roles.

Path C: Working Professionals (Switch With Minimal Risk)

If you already have a job, the safest approach is to shift into a future technology that is closest to your current skills. You will switch faster and protect your income.

Switch strategy

• Identify overlap: process, domain, tools, communication
• Choose a track with strong adjacency
  • Finance/ops → analytics + automation + GenAI workflows
  • IT support → cloud + security
  • Marketing/content → GenAI + performance analytics
  • Engineering roles → automation/robotics/IoT/energy tech
• Build one “work-like” project that matches your current industry problems

Skill plan (10–14 weeks)

• 45–60 minutes daily learning + 2–3 hours on weekends for projects
• One project with clear business impact (before/after, time saved, errors reduced, cost reduced)
• Resume and LinkedIn reframed around outcomes, not tools

Target job roles

Analytics engineer (junior/mid), cloud ops → platform track, security analyst/GRC track, AI solutions associate, automation analyst.

Portfolio Blueprint (What to Build to Get High-Paying Jobs)

A high-paying job in 2026 is rarely given only because you completed a course. Companies want proof that you can deliver real work. Your portfolio is that proof. The best portfolios are not large. They are clear, role-aligned, and well-documented. Below is a simple blueprint, plus examples for each major category.

What a strong portfolio should look like

• 2–4 projects total (quality over quantity)
• Each project has:
  
  • Problem statement (what you were solving)
  • Inputs (data, documents, assumptions)
  • Approach (steps and decisions)
  • Output (demo, screenshots, result)
  • Testing or validation (how you checked it works)
  • Learnings and next improvements

GenAI portfolio (LLM applications)

Build projects that show control, grounding, and reliability.

• Project 1: RAG assistant that answers questions from PDFs/notes with clear citations
• Project 2: Structured output tool (meeting notes → action items JSON, policy → checklist, JD → interview kit)
• Project 3 (optional): Tool-using chatbot (calculator, simple database, or rules engine integration)
  What to document: how you reduced hallucinations, how you tested prompts, edge cases.

Cybersecurity portfolio

Build projects that show security thinking and clear reporting.

• Project 1: Incident response walkthrough (mock incident report, timeline, containment steps
• Project 2: Log analysis exercise (what you observed, what it might mean, next steps
• Project 3 (optional): Cloud IAM security checklist for a sample company (least privilege)
  What to document: threat model, assumptions, and clear mitigation steps.

Cloud/DevOps/Platform portfolio

Build projects that show deployment, automation, and reliability.

• Project 1: Deploy a simple app on a cloud platform (with basic monitoring)
• Project 2: CI/CD pipeline that deploys automatically from GitHub
• Project 3 (optional): Infrastructure-as-code starter (repeatable environment setup)
  What to document: architecture diagram, runbook for failures, cost awareness.

Data/Analytics portfolio

Build projects that show decision-making, not just charts.

• Project 1: KPI dashboard + 1-page insights memo (what changed, why, what to do)
• Project 2: Data cleaning + modelling pipeline (messy data → clean tables → dashboard)
• Project 3 (optional): Simple forecasting or cohort analysis with clear assumptions
  What to document: metrics definition, data quality checks, how you validated results.

XR/Design/Creative tech portfolio

Build projects that show execution and clarity.

• Project 1: One interactive XR demo (training, showroom, walkthrough)
• Project 2: A second demo in a different style (interaction, UI, environment)
• Project 3 (optional): Demo reel + breakdown of assets and optimisation choices
  What to document: design goals, performance constraints, what you learned.

Hardware tracks (Semiconductors/IoT/Robotics/Energy)

Build projects that show fundamentals and system thinking.

• Semiconductors: small RTL modules + verification testbenches
• IoT: sensor project + data pipeline to dashboard + security notes
• Robotics/automation: process automation case study + control logic explanation
• Energy: modelling case study + assumptions + visualisations
  What to document: diagrams, test cases, and why your design choices make sense.

The simplest portfolio rule (works for every track)

• One project should prove you can build.
• One project should prove you can test and validate.
• One project should prove you can explain and document clearly.

Common Mistakes People Make While Chasing High-Paying Technologies in 2026

1) Trying to learn all 10 technologies at the same time

This is the fastest way to get overwhelmed. High-paying roles reward depth, not curiosity across everything. Pick one track for 8–12 weeks, build proof, then expand.

2) Collecting certifications without building projects

Certificates can help, but they rarely substitute for proof. Employers want to see that you can apply skills, not just study them. Even a small project with clean documentation can beat multiple certificates.

3) Choosing a technology based only on salary videos

Salary depends on role, location, and experience. Choose a technology where you can build real competence and stay consistent. Interest and aptitude matter because these fields require long-term learning.

4) Skipping fundamentals

People jump straight into advanced GenAI tools, cloud services, or security topics without learning basics like networking, Linux, SQL, or Python. That creates gaps that show up in interviews and on the job.

5) Not learning how to communicate your work

Many candidates build projects but cannot explain what they did, why they did it, and what they would improve. High-paying teams expect clear communication and documentation, especially in remote and global work environments.

6) Building random projects with no job alignment

A portfolio must match the role you are applying for. A GenAI chatbot project may not help if you are applying for data analyst roles. Always align projects to the job description and required skills.

7) Ignoring safety, security, and reliability

This is a major differentiator in 2026. For GenAI, it is hallucinations and data leakage. For cloud, it is outages and cost blowouts. For security, it is weak identity and monitoring. If your portfolio shows you thought about these, you stand out.

8) Applying without a system

High-paying roles are competitive. Random applications do not work. You need a tracking sheet, weekly targets, tailored resumes, and follow-ups. Consistency is what creates results.

Expert Corner

Future technologies in 2026 are not only buzzwords. They are skill ecosystems that companies are actively hiring for because they shape productivity, security, infrastructure, and long-term competitiveness. The highest-paying jobs sit where the impact is high and the talent is scarce, such as building reliable GenAI applications, securing cloud systems, running scalable platforms, converting data into decisions, and developing deep hardware and energy capabilities.

The best way to benefit from these opportunities is not to chase all ten technologies. Pick one track that matches your strengths, learn the fundamentals that support it, and build a small portfolio that proves you can deliver real outcomes. Two to four strong, well-documented projects will usually do more for your job prospects than a long list of certificates with no proof of work.

If you choose one path, practice consistently, and apply with a clear system, you can enter a high-growth technology career in 2026 and build toward the higher-paying roles over time.

The post Top 10 Future Technologies And Highest Paying Jobs 2026 | High Paying Technologies 2026 appeared first on Vskills Blog.

If you are starting from zero or switching careers, data analytics is also one of the most practical high-growth paths because it rewards skills and proof of work. You do not need an advanced degree to begin. What you need is a strong foundation in Excel and SQL, a clear understanding of business metrics, and a portfolio that demonstrates your ability to solve real-world problems using data.

In this step-by-step roadmap, you will learn exactly how to become a data analyst in 2026. You will get a clear learning sequence, a realistic weekly plan, the tools you should focus on, portfolio project ideas that employers actually value, and interview preparation guidance. By the end, you will have a structured plan you can follow to become job-ready with confidence.

Target Audience

This blog is meant for you if you want a clear, practical path to become a data analyst in 2026 without confusion or random course-hopping. It is designed to work whether you are a complete beginner or you already have some exposure to Excel or data work. You will find this roadmap useful if you are:

• Students who want an entry-level job in analytics
• Freshers who want a job-ready skill set with a portfolio
• Working professional switching from non-technical roles into analytics
• Someone from commerce, economics, management, or humanities backgrounds who wants a structured learning plan
• Anyone who wants to learn analytics in a way that leads to employability, not only certificates

This roadmap will help you choose the right tools, build the right projects, and develop the ability to explain insights clearly, which is what makes data analysts valuable and well-paid.

What Does a Data Analyst Do?

A data analyst helps a company make better decisions using data. In most real jobs, the work is not about complex algorithms. It is about finding patterns, tracking performance, identifying problems early, and recommending what action to take. You will work with raw data that is often messy, clean it, analyse it, and then present the results in a way that business teams can use. Here are the most common things data analysts do in a typical role:

• Collect and organise data from different sources such as spreadsheets, databases, and tools
• Clean data by fixing missing values, duplicates, wrong formats, and inconsistent entries
• Write SQL queries to pull data and create analysis-ready tables
• Track business performance using KPIs and build regular reports
• Create dashboards in tools like Power BI or Tableau for teams to monitor performance
• Answer ad hoc questions from business teams, such as why sales dropped, which segment is growing, or what is driving churn
• Identify trends and patterns, then explain what they mean for the business
• Present insights and recommendations in a simple way to managers and stakeholders

A strong data analyst is expected to do more than report. They are expected to connect data to business outcomes. For example, instead of only showing that retention is falling, they explain which user segment is leaving, at which stage they drop off, and what change might improve retention.

Data Analyst vs Data Scientist vs Business Analyst

These three roles overlap, which is why people get confused. The easiest way to understand the difference is to look at what each role mainly focuses on and what kind of output they are expected to deliver.

Data Analyst

• A data analyst focuses on understanding what is happening in the business and why it is happening, using data. The work is usually centred on SQL, dashboards, reporting, and actionable insights.
• Common outputs include KPI dashboards, weekly performance reports, funnel and cohort analysis, and recommendations for business teams.
• Typical tools: Excel/Sheets, SQL, Power BI/Tableau, sometimes Python.

Data Scientist

• A data scientist focuses more on building predictive or automated solutions using data. They often create models for churn prediction, demand forecasting, recommendation systems, fraud detection, and similar use cases. The role typically requires stronger statistics and programming skills.
• Common outputs include predictive models, experimentation frameworks, and data-driven product improvements.
• Typical tools: Python/R, SQL, ML libraries, cloud or deployment tools in some roles.

Business Analyst

• A business analyst focuses on solving business problems by gathering requirements, improving processes, defining metrics, and aligning stakeholders. Some business analysts work heavily with data, but many roles focus more on documentation, operations, and process improvement.
• Common outputs include requirement documents, process maps, business cases, and stakeholder alignment.
• Typical tools: Excel, documentation tools, sometimes SQL and BI tools, depending on the company.

Data Analyst vs Data Scientist vs Business Analyst

Which one should you choose in 2026?

• Choose a data analyst if you want the most straightforward entry into analytics and you enjoy working with metrics, reporting, and insights.
• Choose a data scientist if you enjoy math, coding, and building predictive or automated solutions, and you are ready for a steeper learning curve.
• Choose a business analyst if you enjoy stakeholder coordination, problem solving, and process improvement, and you want a role that can lead to product, strategy, or operations paths.

Skills You Need to Become a Data Analyst in 2026

To become job-ready, you do not need to learn everything at once. You need the right sequence. A data analyst is valued for two things: strong basics (Excel and SQL) and the ability to convert data into clear business insight. The skills below are organised into must-have skills and high-value add-on skills.

Must-have Core skills

• Excel or Google Sheets: You should be comfortable with formulas, data cleaning, pivot tables, charts, and building clean summary tables. Excel is still used heavily in real jobs for quick analysis and reporting.
• SQL: SQL is the most important skill for most data analyst roles because most business data lives in databases. You should learn how to filter data, aggregate it, join tables, and create reusable queries. If you have strong SQL, you instantly become more employable.
• Data cleaning and data validation: Real data is messy. You must know how to handle missing values, duplicates, incorrect formats, inconsistent categories, and outliers. You should also learn to validate results so you do not report wrong numbers.
• Basic statistics for analysis: You do not need advanced math for most entry roles, but you do need basics like mean and median, variance, correlation, trend understanding, and how to interpret results without making false claims.
• Data visualisation fundamentals: Your charts should be easy to read, not decorative. You should know how to pick the right chart, label it properly, and avoid misleading visuals.
• Business understanding and KPI thinking: A data analyst should know common metrics like revenue, growth, conversion rate, retention, churn, customer acquisition cost, and customer lifetime value. This is what helps you convert analysis into decisions.

Additional Skills (High-value add-ons)

• Power BI or Tableau: Dashboarding is a major part of many analyst roles. Knowing one dashboard tool well is enough. Power BI is often preferred in many companies because it integrates well with Microsoft ecosystems, while Tableau is common in some product and consulting environments.
• Python (optional but strongly helpful): Python helps you clean data faster, analyse larger datasets, and automate repetitive reports. It is also useful if you want to grow into product analytics or data science later. Focus on pandas and basic visualisation first.
• A/B testing and experimentation basics: Many companies run experiments for marketing or product decisions. Even basic knowledge of how experiments are designed and interpreted can make you stand out.
• Presentation and communication: This is what separates average analysts from strong analysts. You must be able to explain what the numbers mean and what action is recommended in simple language.

Tools to Learn in 2026 (Practical Stack)

You do not need to learn every tool in the market. Most data analyst jobs are won by people who are strong in a small set of tools and can show proof through projects. The goal is to pick one tool per category and get good enough to solve real problems.

• Excel or Google Sheets: This is your fastest tool for analysis and reporting. Learn formulas, pivot tables, charts, and clean reporting layouts.
• SQL (MySQL or PostgreSQL): SQL is essential because company data usually sits in databases. If you can write strong SQL queries, you can answer business questions quickly and reliably.
• Power BI or Tableau: Choose one dashboard tool and learn it well. Focus on dashboard design, filters, drill-downs, and building KPI views that business teams can actually use.
• Python (optional but recommended): Python gives you an edge, especially in product analytics and automation-heavy roles. For data analyst work, you mainly need pandas for analysis and a basic plotting library for charts.
• GitHub or a portfolio page (for proof): Even for analyst roles, showing your projects clearly matters. GitHub is useful for storing SQL queries, dashboards, and project documentation. If you prefer, you can use a simple portfolio page or Notion, but make sure it looks professional.
• Documentation tool (Notion or Google Docs): What makes you stand out is not only doing analysis, but documenting the problem, approach, assumptions, and recommendations. A clean write-up improves credibility.

Step-by-Step Roadmap to Become a Data Analyst in 2026

This roadmap is designed so that you build employable skills in the right order. Each step includes what to learn and what to produce, so you have proof of work by the end.

Step 1: Understand data basics and business metrics (Week 1)

In your first week, your goal is to learn how businesses measure performance. This matters because a data analyst is hired to support decisions, and decisions are usually driven by metrics.

What to learn?

• What are KPIs, metrics, dimensions, and measures
• The difference between leading metrics and lagging metrics
• Common business metrics you will see in analytics roles
• How to ask a good business question before analysing data

Metrics you should know early

• Revenue, costs, profit, margin
• Conversion rate and funnel stages
• Retention and churn
• Customer acquisition cost and customer lifetime value
• ARPU and cohort behaviour (basic understanding)

What to produce by the end of Week 1

• A one-page notes document listing 15 to 20 common KPIs with simple definitions
• A short case-style exercise: pick any app you use and write 5 business questions you would answer with data

Step 2: Master Excel for analysis (Weeks 2–3)

Excel is still one of the fastest ways to analyse data and communicate results. In many companies, you will still be asked for quick Excel outputs even if dashboards exist.

What to learn?

• Cleaning: removing duplicates, fixing formats, text-to-columns, data validation
• Core formulas: IF, IFS, SUMIF, COUNTIF, XLOOKUP or VLOOKUP, INDEX-MATCH, text functions
• Pivot tables and pivot charts
• Conditional formatting and basic report layouts
• Building a clean summary table and writing insights

Mini-project for Excel (end of Week 3)
Sales performance report

• Use any public sales dataset
• Create a pivot-based report showing revenue by month, region, and product category
• Add 3 charts that are easy to read
• Write 5 insights and 3 recommendations based on the data

What to produce by the end of Step 2

• One clean Excel file with analysis + charts
• A one-page insight note summarising what changed, why it matters, and what action you recommend

Step 3: Learn SQL properly (Weeks 4–6)

SQL is the most important skill for most data analyst jobs because it is how you access business data. Employers often shortlist candidates based on SQL alone because it shows you can work independently with real datasets.

What to learn?

• SELECT, WHERE, ORDER BY, LIMIT
• GROUP BY with aggregates like SUM, COUNT, AVG
• JOINs (INNER, LEFT), and when to use each
• CASE WHEN for conditional logic
• Subqueries and Common Table Expressions (CTEs)
• Basics of window functions (ROW_NUMBER, RANK, running totals)
• Data validation habits: checking duplicates, missing values, and unexpected spikes

Mini-project for SQL (end of Week 6): Customer and orders analysis

• Use any sample database with customers, orders, products, and dates
• Answer business questions such as:
  • Which products drive the most revenue and repeat purchases?
  • Which customer segments have the highest average order value?
  • What is the month-wise trend of sales and repeat rate?
  • Which regions have high sales but low retention?
• Write your queries cleanly and add comments so someone else can understand your logic

What to produce by the end of Step 3

• A single SQL file with 20 to 30 well-structured queries
• A short document summarising the key insights in plain language
• A simple table of KPIs: revenue, orders, repeat rate, AOV, top categories

Step 4: Learn dashboarding (Power BI or Tableau) (Weeks 7–8)

Dashboards help stakeholders track performance without asking you for the same report repeatedly. Companies value analysts who can build dashboards that are clean, fast, and decision-friendly.

What to learn?

• How to structure a dashboard: overview → drill-down → detail
• Filters and slicers, drill-through, and tooltips
• Basic calculated fields and measures
• Dashboard design principles: clarity, consistency, minimal clutter
• Building KPI cards and trend visuals the right way

Mini-project for Dashboarding (end of Week 8): KPI dashboard with filters

• Build a dashboard that shows:
  • Overall revenue, orders, and conversion or repeat rate
  • Trend line by month
  • Breakdown by product category and region
  • Top products and bottom products
• Add filters for time period, category, and region
• Ensure the dashboard answers a real question, not only displays charts

What to produce by the end of Step 4

• One finished dashboard file
• 5 written insights that a business team can act on
• A short “how to read this dashboard” note (this shows maturity)

Step 5: Learn basic statistics for analysts (Weeks 9–10)

Statistics help you avoid incorrect conclusions. As an analyst, you will often compare performance across time, segments, or campaigns. Basic statistics ensures you interpret patterns correctly and communicate uncertainty honestly.

What to learn?

• Descriptive statistics: mean, median, mode, percentiles, variance, standard deviation
• Correlation and why it does not prove causation
• Trend and seasonality basics
• Sampling basics and why sample size matters
• Confidence intuition: understanding whether a difference is meaningful or random noise
• Hypothesis testing basics: what p-values mean at a high level, and common mistakes
• Simple regression intuition: how to interpret relationships without overclaiming

Mini-project for statistics (end of Week 10): Insight and recommendation report

• Take a dataset with time and segments (marketing, sales, product usage, or finance)
• Compare two segments and explain whether differences are likely meaningful
• Identify trends and possible drivers
• Write recommendations and also mention what additional data would strengthen confidence

What to produce by the end of Step 5

• A short report with: question, analysis, findings, interpretation, and recommendations
• A section called assumptions and limitations (this makes your work credible)

Step 6: Add Python (optional but recommended) (Weeks 11–12)

Python is not mandatory for every data analyst job, but it is a strong advantage. It helps you work faster, handle larger datasets, and automate parts of reporting. It also makes it easier to grow into product analytics or data science later.

What to learn?

• Python basics and working in notebooks
• pandas: reading data, cleaning, filtering, grouping, merging
• Exploratory data analysis: summary stats, distributions, outliers
• Visualisation basics using a plotting library
• Writing reusable functions for repeated analysis tasks

Mini-project for Python (end of Week 12): End-to-end EDA + insights report

• Choose a dataset with enough depth (at least 10,000 rows if possible)
• Clean it, explore it, identify patterns, and build 5 to 7 clear charts
• Write a final summary: what the business should do next based on the data

What to produce by the end of Step 6

• A well-documented notebook or script
• A clean insights document written in plain languag
• A short README that explains dataset, questions, method, and findings

5 Projects That Can Help You Get Hired

Your portfolio is what proves you can do the job. In 2026, many candidates list tools, but hiring managers want to see whether you can take a messy dataset, answer business questions, and communicate insights clearly. You only need 4 to 5 strong projects, but they should look like real work.

A simple template to use for every project

• Business question: what you are trying to solve
• Dataset and tables: what data you used and what each table contains
• Cleaning and assumptions: what you fixed and what you assumed
• Analysis approach: SQL queries, Excel steps, or Python workflow
• Dashboard or charts: visuals that support decisions
• Insights: 5 to 8 clear insights in plain language
• Recommendations: 3 to 5 actions based on insights
• Limitations: what you could not confirm and what additional data you would need

Project 1: E-commerce funnel analysis and drop-off diagnosis

What will you do?

• Analyse user journey from visit → signup → add to cart → checkout → purchase
• Identify where users drop off the most and which segment is affected
• Suggest fixes based on the pattern

What makes this project strong – It shows you can think like a product or growth analyst and connect metrics to decisions.

Project 2: Customer retention and cohort analysis

What will you do?

• Build cohorts by signup month or first purchase month
• Measure retention and repeat rate across cohorts
• Identify which cohorts are improving or worsening and why

What makes this project strong – Cohort analysis is common in real analytics roles and shows structured thinking.

Project 3: Finance performance dashboard (revenue, cost, margin)

What will you do?

• Track monthly revenue, costs, gross margin, and profit
• Break down by category, region, or product line
• Highlight what is driving margin changes

What makes this project strong – It signals that you can work with business and finance teams, not only product data.

Project 4: Marketing channel performance (CAC, ROAS, conversions)

What will you do?

• Compare marketing channels on spend, conversions, CAC, and ROAS
• Identify the best and worst performing channels
• Recommend how to reallocate spend

What makes this project strong – Marketing analytics is widely available as entry-level work and has clear KPIs.

Project 5: Operations analytics (SLA, delays, defects, productivity)

What will you do?

• Analyse delivery time, service delays, defect rates, or resolution time
• Identify bottlenecks and variance across locations or teams
• Suggest process improvements and monitoring metrics

What makes this project strong – Operations analytics is a high-demand area and proves you can improve efficiency, not only track performance.

Build a Resume That Works for Data Analyst Roles (2026)

A strong data analyst resume is not a list of tools. It is proof that you can solve business problems using data. Your resume should quickly answer three questions: What tools can you use, what problems have you solved, and what impact did you create.

What sections to include?

• Summary (2 to 3 lines) focused on analytics skills and tools
• Skills (keep it short and job-relevant: Excel, SQL, Power BI/Tableau, Python if applicable)
• Projects (this is the most important section if you are a fresher or career switcher)
• Experience (if you have work experience, highlight data work even if it was not your job title)
• Education and certifications (only the most relevant ones)

How to write project bullets so they look like work experience

Use this simple format: action + tool + outcome + metric.

Examples of strong bullets

• Built a Power BI KPI dashboard to track monthly revenue, margin, and region-wise performance, reducing manual reporting effort by creating a single source of truth.
• Used SQL joins and CTEs to analyse repeat purchase behaviour and identified the top customer segment contributing to revenue concentration.
• Performed cohort retention analysis and highlighted a drop in week-4 retention for a specific acquisition channel, recommending changes in onboarding flow.
• Cleaned and validated a dataset with duplicates and missing values, ensuring consistent category mapping before reporting.

What to avoid in bullets

• “Worked on dashboard” or “Used SQL” without explaining what you achieved
• Long paragraphs that hide the result
• Too many tools listed without proof of usage

How to present projects professionally?

For each project, include:

• 1 line project title that sounds like a business problem
• Tools used
• 2 to 4 bullets showing what you did and what insight you found
• Link to dashboard, SQL file, or documentation if you have it

Common resume mistakes that reduce shortlist chances

• Writing a generic objective instead of a skills-based summary
• Listing 15 tools but having no strong projects
• Not mentioning SQL, or showing weak SQL proof
• Adding charts/screenshots without explaining insights
• Not tailoring resume keywords to the job description
• Poor formatting, too much text, and unclear section headings

Data Analyst Interview Preparation Guide 2026

Most data analyst interviews test four areas: SQL, Excel, dashboards, and business thinking. You do not need to be perfect in everything, but you must be reliable in SQL and clear in how you explain insights.

1) SQL interview preparation

What will be tested on?

• Aggregations and GROUP BY
• JOINs across multiple tables
• Filtering by time and segments
• CASE WHEN logic
• CTEs and subqueries
• Window functions basics (ranking, running totals) in many roles
• Writing clean queries and validating results

How to practice?

• Solve 30 to 50 SQL questions across difficulty levels
• After every query, cross-check with a small sample to confirm correctness
• Practice explaining your approach, not only writing the final query

2) Excel test preparation

What you will be tested on

• Cleaning and formatting data quickly
• Pivot tables and pivot charts
• Lookups and conditional formulas
• Building a short summary report with insights
• Basic charts with correct labels

How to practice?

• Take a dataset and time yourself to create a report in 30 to 45 minutes
• Focus on clarity and correctness over fancy design

3) Dashboard and visualisation questions

What will be tested on?

• Whether you can pick the right chart for the problem
• Whether your dashboard answers business questions
• Whether you understand filters, drill-downs, and measures
• Whether your dashboard layout is clear

How to practice?

• Build one dashboard and then improve it twice based on feedback
• Practice explaining how a stakeholder should use the dashboard

4) Business case and analytical thinking questions

These questions check whether you can translate a vague business problem into an analysis plan.

Common case prompts

• “Sales dropped last month. How will you analyse why?”
• “Retention is down for new users. What would you check first?”
• “Which marketing channel is best and why?”
• “How would you measure the success of a new feature?”

How to answer?

• Clarify the goal and metric first
• Break the problem into possible drivers
• Identify what data you need
• Explain the analysis steps in order
• Share what decision you would recommend based on likely outcomes

A simple 2-week interview prep plan

Week 1

• SQL practice daily (45 to 60 minutes)
• 2 Excel timed reports
• 1 case question practice per day

Week 2

• Mixed mock interviews: SQL + case + dashboard explanation
• Revise mistakes and build a final project summary you can speak confidently about
• Prepare 2-minute explanations for each portfolio project

Data Analyst Jobs and Internships (2026)

Getting shortlisted is mostly about two things: using the right keywords and showing proof (projects + clear resume). This section helps you find opportunities and apply in a way that actually works.

Best places to find data analyst roles

• LinkedIn Jobs: Use role keywords and apply consistently. Many entry roles are posted here first.
• Company career pages: If you have a target list of companies, apply directly. This often reduces competition compared to job portals.
• Internship platforms and early-career portals: Look for internships, analyst trainee roles, graduate programs, and apprenticeships. These can be easier entry points than full-time analyst roles.
• Referrals and networking: Referrals matter because analytics roles get many applications. A good referral combined with a portfolio link can increase your shortlist chances significantly.

Keywords to search (copy-paste list)

• Data Analyst, Junior Data Analyst, Associate Data Analyst
• Business Analyst (Data), Product Analyst, Marketing Analyst, Operations Analyst
• Reporting Analyst, MIS Analyst, BI Analyst
• SQL Analyst, Analytics Associate, Growth Analyst
• Power BI Analyst, Tableau Analyst

A simple LinkedIn strategy that works

Step 1: Fix your headline and about section

• Mention your target role and core skills clearly, for example: Data Analyst | SQL | Excel | Power BI | Portfolio Projects

Step 2: Pin your portfolio

• Add a featured section with links to your dashboard and project documentation.

Step 3: Post proof of work

• Once a week, share a small insight from a project. Even one chart with a short explanation helps recruiters see your capability.

Step 4: Apply with focus

• Apply to roles where your skills match at least 60 to 70%. Avoid applying randomly to everything.

Cold message format you can reuse

• 1 line: introduce yourself and your target role
• 1 line: mention your key skills (SQL, Excel, Power BI)
• 1 line: share your best project link
• 1 line: ask for a short guidance call or referral if appropriate

Example message

Hi, I am currently building my profile for data analyst roles and I work with SQL, Excel, and Power BI. I recently completed a project on customer retention cohort analysis and built a dashboard with actionable insights. If you have 10 minutes, I would love your guidance on what skills to prioritise for entry roles at your company. I can share my portfolio link as well.

A practical application target

• 10 focused applications per week
• 5 personalised messages per week
• 1 portfolio improvement per week

This keeps you consistent without burnout.

Career Growth Path and Salary for Data Analysts (2026)

Data analytics has one of the clearest growth paths because your value increases as you move from reporting to decision-making. Your salary increases fastest when you become strong in SQL, build business depth, and start owning outcomes, not only dashboards.

Typical career path

• Data Analyst (Entry): You mainly support reporting, KPI tracking, data cleaning, and ad hoc analysis. Your goal is to become fast and accurate with SQL and confident in explaining insights.
• Data Analyst (Mid-level): You begin owning metrics, building dashboards used by teams, and analysing drivers behind performance changes. You are expected to recommend actions, not only show results.
• Senior Data Analyst: You handle complex problems, mentor others, and work closely with leadership. You often own a business area such as retention, growth, revenue, or operations analytics.
• Analytics Lead / Analytics Manager: You manage stakeholders and sometimes a team. You define what should be measured, how dashboards and reporting should be structured, and how analytics should influence strategy.

Transition options from Data Analyst

• Product Analyst: More focus on user behaviour, funnels, experiments, and feature impact.
• Business Analyst: More stakeholder alignment, requirements, process improvement, and decision support.
• BI Developer / BI Engineer: More focus on dashboard engineering, data models, and reporting pipelines.
• Data Scientist (with extra learning): More focus on predictive modelling and advanced analysis.

What skills increase salary the fastest?

• Advanced SQL: Window functions, performance optimisation, and strong data validation habits.
• Dashboard maturity: Designing dashboards that drive decisions, not only display charts. Clear layouts, drill-downs, and correct metric definitions matter.
• Business depth in one domain: Pick one domain and become strong in it: product, finance, marketing, or operations. Domain expertise often pays more than learning a new tool.
• Experimentation and A/B testing basics: Even basic knowledge of how to measure impact can raise your profile.
• Python for automation: Automating reporting and analysis can make you more efficient and more valuable.
• Communication and stakeholder management: This is what moves you into senior roles. If you can explain insights simply and influence action, your salary grows faster.

How to move into higher-paying roles faster?

• Pick one niche: Product Analytics, Marketing Analytics, Finance Analytics, or Operations Analytics
• Build 2 projects in that niche that look like real business work
• Learn the KPIs and decision patterns used in that niche
• Practice case questions from that niche before interviews

Common Mistakes and How to Avoid Them

Most people do not fail because they are not smart. They fail because they learn in the wrong order, build weak projects, or cannot communicate insights clearly. Avoid these mistakes and your chances of getting shortlisted will improve sharply.

• Learning tools without building projects: If you only watch courses, you will not be job-ready. After every tool you learn, build one project that proves you can use it.
• Avoiding SQL or learning it superficially: SQL is the core skill for most analyst roles. Many candidates get rejected because they cannot write joins, CTEs, or clean aggregation logic confidently.
• Making dashboards without business insight: A dashboard is not the final output. The final output is a decision. Always write insights and recommendations along with your dashboard.
• Skipping data cleaning and validation: Wrong numbers destroy trust. Build the habit of validating totals, checking duplicates, and verifying logic before sharing results.
• Using too many tools and becoming average in all of them: It is better to be strong in Excel, SQL, and one dashboard tool than to list many tools with no depth.
• Weak communication: If you cannot explain what changed and why it matters in simple language, you will struggle in interviews and on the job. Practice writing short insight notes.

Expert Corner

To become a data analyst in 2026, focus on fundamentals, not shortcuts. Excel and SQL are your base. A dashboard tool helps you communicate. Statistics improve your judgement. Projects prove you can do the job. If you follow a structured plan and build 4 to 5 strong projects with clear insights, you will be in a strong position to get shortlisted for entry-level roles.

The post How to Become a Data Analyst in 2026 | Step-by-Step Roadmap appeared first on Vskills Blog.

This blog simplifies the process. It gives you a complete beginner-friendly roadmap that shows what to learn first, what to learn next, and how to practice in a way that leads to real skills. You will also understand the difference between learning concepts and becoming job-ready, because Data Science is not only about courses. It is about building projects, working with real datasets, and learning how to explain insights in a clear, structured way. By the end of this roadmap, you will have a practical learning plan you can follow step-by-step in 2026, whether your goal is to become a Data Scientist, move into analytics, or build a strong foundation for machine learning and AI roles.

Who is this roadmap for Data Science?

This roadmap is designed for beginners who want a clear, step-by-step path, without getting overwhelmed by too many tools or advanced topics too early. You will find this roadmap useful if you are any of the following:

• A college student or fresher who wants to start Data Science from scratch in 2026
• A working professional planning a career switch into Data Science, analytics, or AI roles
• Someone who knows basic Excel but wants to move into Python, SQL, and machine learning
• A beginner who has tried multiple courses but still feels unsure about what to learn next
• Anyone who wants to build real projects and a portfolio, not only collect certificates

By following this roadmap, you will be able to learn the fundamentals in the right order, practice using real datasets, and gradually build the confidence needed for internships, entry-level roles, and interviews.

Understanding Data Science in 2026

Before you start the roadmap, it helps to understand what people mean when they say “Data Science,” because many beginners mix it up with Data Analytics or Machine Learning Engineering. Data Science is a mix of three things: working with data (cleaning and preparing it), finding patterns and insights (analysis and storytelling), and building models that can predict or classify outcomes (machine learning). In real jobs, Data Scientists are expected to do all three, but the level of focus depends on the company. To make it clearer, here is how the most common roles are different:

• Data Analytics focuses on answering business questions using reports, dashboards, KPIs, and trends. The typical tools are Excel, SQL, Power BI or Tableau, and basic Python.
• Data Science goes a step further and includes statistical thinking, experimentation, machine learning, and building predictive solutions. The typical tools are Python, SQL, statistics, and machine learning libraries.
• Machine Learning Engineering is more focused on deploying models into real products, building pipelines, and scaling systems. This requires stronger software engineering skills, cloud knowledge, and production tools.

For beginners, the best approach is to first build an analytics and statistics foundation, then move into machine learning, and then gradually add advanced AI topics. That is exactly how this roadmap is structured.

Setting up your Learning Environment

Before you start learning concepts, set up a simple workspace where you can practice daily without friction. This matters because Data Science is learned by doing, and small setup issues often break consistency for beginners. What to install and use regularly:

• Python (either Anaconda or standard Python)
• Jupyter Notebook or VS Code (use one as your main workspace)
• Git and GitHub (to save and showcase projects)
• Google Colab (optional, helpful if your laptop is slow)

A simple setup routine you can follow:

• Create one folder for all Data Science work
• Keep one notebook for practice and one folder for projects
• Push at least one small practice project to GitHub in the first week (even if it is basic)

Outcome you should aim for in this step: you should be able to run a notebook, load a CSV file, and upload your work to GitHub without confusion.

Step 1: Learn the core math you actually need

You do not need advanced mathematics to start Data Science, but you do need a strong grip on basic statistics and probability. These topics help you understand data patterns, interpret results correctly, and avoid wrong conclusions. Topics to focus on first:

• Basic algebra you will use in formulas and transformations
• Descriptive statistics: mean, median, mode, variance, standard deviation
• Probability basics: events, conditional probability, independence
• Distributions you will see often: normal distribution, skewness, outliers
• Sampling and uncertainty: sampling bias, confidence intervals (concept level)
• Correlation and intuition behind relationships in data

How to practice without making it too theoretical:

• Take a small dataset and calculate mean, variance, and percentiles manually once
• Plot distributions and explain what they mean in words
• Read simple graphs and interpret what is happening, instead of memorising formulas

Outcome you should aim for: you should be able to explain in simple words what variance, probability, correlation, and sampling mean, and why they matter in real analysis.

Step 2: Learn Python for Data Science (not general Python)

Your goal is not to learn every part of Python. Your goal is to learn Python that helps you work with data confidently: loading data, cleaning it, transforming it, analysing it, and building repeatable workflows. Python topics you should learn in this stage:

• Variables, data types, conditions, loops (only what is needed)
• Functions (writing reusable code for cleaning and analysis)
• Working with files: CSV, Excel, JSON
• Numpy basics: arrays, basic operations, handling numeric data
• Pandas fundamentals: dataframes, selecting rows and columns, filtering, sorting
• Data cleaning: missing values, duplicates, wrong formats
• Combining data: merge, join, concat
• Grouping and summarising: groupby, aggregations, pivot tables

Mini-project ideas (pick one to start):

• Clean a messy dataset and create a final “analysis-ready” dataset
• Analyse a sales dataset: monthly trends, best products, top regions
• Analyse a simple finance dataset: expenses by category, monthly savings trend

Outcome you should aim for: you should be able to take a raw dataset, clean it, summarise it, and generate basic insights without copying code blindly.

Step 3: Learn SQL (this is non-negotiable)

In real companies, most data lives in databases, not in CSV files. SQL is the skill that helps you pull the right data quickly, validate numbers, and answer business questions without depending on anyone else.

SQL topics you should learn in this stage:

• SELECT, WHERE, ORDER BY (basic filtering and sorting)
• LIMIT, DISTINCT (quick control and clean outputs)
• Aggregations: COUNT, SUM, AVG, MIN, MAX
• GROUP BY and HAVING (metrics by category and filtering on aggregates)
• Joins: INNER JOIN, LEFT JOIN (most important in real work)
• Subqueries (for multi-step logic)
• CTEs (cleaner version of subqueries, very common in practice)
• Window functions (basic level): ROW_NUMBER, RANK, running totals

How to practice SQL properly:

• Practice on a sample database (sales, e-commerce, HR, finance)
• Try to write queries for business questions like “top 5 products by revenue” or “repeat customers per month”
• Cross-check your SQL output using Pandas to build confidence

Outcome you should aim for: you should be able to write SQL queries that pull clean tables for analysis, and you should be comfortable with joins and group-by metrics.

Step 4: Learn data visualization and storytelling

A Data Scientist is not only expected to build models. You also need to communicate what the data is saying in a way that is clear, structured, and decision-friendly. This is where visualization and storytelling matter.

What to learn first?

• How to choose the right chart for the question
• How to write short, clear insights from charts
• How to avoid misleading visuals and wrong comparisons
• How to structure an analysis like a mini business report

Tools you can use at beginner level:

• Python charts: Matplotlib (basics) and Seaborn (for quicker plots)
• Excel charts (still very useful for quick exploration)
• Optional advantage: Power BI or Tableau (only after you are comfortable with basics)

Practice ideas that build real skill:

• Take one dataset and create 8–10 charts that answer specific questions
• After every chart, write 2 lines: what the chart shows and what it implies
• Create a simple “insights summary” at the end (3–5 key takeaways)

Outcome you should aim for: you should be able to turn a dataset into a clear analysis story, not just random charts.

Step 5: Learn Exploratory Data Analysis (EDA) properly

EDA is where you start thinking like a Data Scientist. It is not only about plotting charts. It is about understanding the dataset deeply, spotting data quality issues, finding patterns, and forming hypotheses that can later be tested using statistics or models.

What should you learn in this stage?

• Understanding the dataset structure: rows, columns, units, time period, categories
• Data types and conversions: dates, text, numeric fields, category fields
• Missing data analysis: how much is missing, where it is missing, and why it matters
• Outliers: how to detect them and when to keep or remove them
• Distribution checks: skewness, long tails, unusual spikes
• Relationship checks: correlation, scatter plots, grouped comparisons
• Segment analysis: insights by region, age group, product category, income group, and so on
• Hypothesis framing: what you think is true and what evidence you need

How to practice EDA in a job-like way?

• Start with a real dataset and write a short EDA report as if you are sending it to a manager
• Do not only show charts, but also explain what changed your understanding of the data
• End the report with “next steps” such as what further data you need or what model could be tried

Outcome you should aim for: you should be able to write a clean EDA report with insights, data issues, and next-step recommendations.

Step 6: Learn Machine Learning foundations

Once your data handling and EDA skills are solid, you can start machine learning. At beginner level, focus on classical ML first because it builds the foundation for interviews and real work. Core concepts you must understand:

• Train-test split and why it matters
• Overfitting and underfitting (and how to detect them)
• Model generalisation and why “high accuracy” can still be wrong
• Cross-validation (concept and basic usage)
• Model evaluation metrics and when to use which one

Metrics you should learn first:

• Classification: accuracy, precision, recall, F1 score, ROC-AUC
• Regression: MAE, MSE, RMSE, R-squared

Algorithms to learn in the right order:

• Linear Regression (prediction basics)
• Logistic Regression (classification basics)
• Decision Trees (easy to interpret, good starter model)
• Random Forest (strong baseline model in many problems)
• Gradient Boosting (basic understanding, strong performance)
• KNN and Naive Bayes (quick coverage and intuition building)

How to practice machine learning properly:

• Always start with a baseline model and then improve it step-by-step
• Keep a clear notebook with what you tried, what changed, and what improved
• Learn to explain model results in simple language, not only code output

Outcome you should aim for: you should be able to build a basic ML model, evaluate it correctly, and explain what the results mean and what you would do next.

Step 7: Learn feature engineering and model improvement

This is the stage where you move from “I can train a model” to “I can make the model better and justify my choices.” In interviews and real work, this matters more than knowing many algorithms.

What should you learn in this stage?

• Handling categorical variables: label encoding vs one-hot encoding
• Scaling and normalisation: when it helps and when it does not
• Creating new features from existing columns (dates, text length, ratios, flags)
• Feature selection basics: removing useless features, reducing noise
• Handling imbalanced datasets: why accuracy fails, how to fix with better metrics and sampling
• Hyperparameter tuning basics: grid search and random search
• Model comparison: how to choose between two models using metrics and business logic

How to practice feature engineering without overcomplicating?

• Start with one baseline model
• Improve only one thing at a time (encoding, scaling, new features, tuning)
• Track results in a simple table inside your notebook (model version, changes, score)

Outcome you should aim for: you should be able to improve a baseline model meaningfully and explain why the changes worked.

Step 8: Learn practical tools used in real jobs

Many beginners can write code, but they struggle in real work because they do not know how to organise projects, document work, or collaborate. These practical tools make you look job-ready. Tools and skills to build here:

• Git and GitHub basics: committing, pushing, organising repositories
• Clean project structure: folders for data, notebooks, scripts, outputs
• Writing a good README: problem statement, dataset, approach, results, how to run
• Basic environment management: requirements.txt or conda environment
• Working with APIs (basic level): pulling data using requests
• Basic web scraping (only if needed): collecting data responsibly from websites
• Optional: basic cloud exposure (only awareness level): using notebooks, saving data, running code

Simple practice tasks that build real confidence:

• Convert one notebook project into a clean GitHub repository with a README
• Add clear comments and section headers in notebooks
• Save final outputs (charts, tables) in a results folder

Outcome you should aim for: your projects should look clean, organised, and easy for someone else to understand and run.

Step 9: Build a beginner-friendly portfolio (minimum 4 strong projects)

Your portfolio is your proof of skill. A beginner portfolio should focus on clarity, correct thinking, clean work, and real datasets. You do not need complex deep learning projects to get shortlisted. You need projects that you can explain confidently. What a good beginner portfolio should show:

• You can clean and prepare real-world messy data
• You can do EDA and extract insights that make sense
• You can build baseline models and evaluate them correctly
• You can improve models with clear reasoning
• You can communicate results in a structured way

A recommended portfolio set (pick datasets you genuinely enjoy):

• Project 1: EDA + insights report
  Example themes: consumer spending, public health, education, jobs, sales trends
  What to deliver: a clean notebook with 8–12 charts, insights, and a short summary
  
• Project 2: Regression problem (prediction)
  Example themes: house prices, demand forecasting, ride fares, income prediction
  What to deliver: baseline model, error analysis, improvements, final model
  
• Project 3: Classification problem (decision making)
  Example themes: churn prediction, loan default risk, fraud detection, customer segmentation labels
  What to deliver: correct metrics, confusion matrix, class imbalance handling, improved model
  
• Project 4: End-to-end project (full workflow)
  What to deliver: data cleaning → EDA → model → final recommendation summary
  This project should be the most “job-like” and well-documented one

Where to publish your work:

• GitHub (must)
• Optional but useful: Kaggle notebooks (for visibility)
• Optional but powerful: LinkedIn posts summarising what you learned and built

Outcome you should aim for: you should have 4 projects you can explain clearly, including why you made certain choices and what you would improve next.

Step 10: Interview preparation and job strategy

Once your projects are ready, shift focus to interview skills and a smart job search approach. Many beginners lose opportunities because they cannot explain the basics clearly or because their resume does not highlight real work.

What to prepare for interviews?

• Statistics and probability questions (sampling, distributions, correlation, hypothesis thinking)
• SQL interview queries (joins, group by metrics, window functions)
• Python basics for data handling (Pandas operations and cleaning logic)
• Machine learning fundamentals (overfitting, evaluation, feature engineering, model selection)
• Case studies (how you would approach a business problem using data)
• Project walkthroughs (most important): problem, dataset, steps, results, limitations, next steps
  

Entry-level roles you can realistically target as a beginner:

• Data Analyst (strong SQL + Python + dashboard thinking)
• Junior Data Scientist (projects + ML foundations + clear communication)
• Data Science Intern or ML Intern (portfolio + fundamentals)
• Business Analyst (analytics-heavy path, then transition into DS)

How to present your projects during interviews?

• Explain the problem in one line
• Explain what data you used and what was messy about it
• Explain the steps you followed (cleaning, EDA, modeling)
• Share results and why they matter
• Mention limitations and what you would do next if you had more time

Outcome you should aim for: you should be able to explain each of your 4 projects in a clear 5–7 minute story, without depending on your notebook.

Suggested Data Science Roadmap 2026

If you want a simple timeline to follow, this 6-month plan gives you a realistic path. It is structured so that you build foundations first, then move into machine learning, and finally focus on projects and interviews.

Month 1: Python basics for data work + statistics fundamentals

• Focus on setting up your environment, learning Pandas basics, and understanding core statistics like mean, variance, distributions, and correlation. Do small daily practice using simple datasets so you become comfortable with data handling early.

Month 2: SQL + stronger data cleaning and transformation

• Build SQL skills alongside Pandas. Practice joins, group by, and writing queries for business-style questions. By the end of this month, you should be able to pull data using SQL and clean it confidently in Python.

Month 3: EDA + visualization + insights writing

• Pick one real dataset and do a complete EDA project. Create charts, explain patterns in words, and write a short insights summary. This is where you start building your portfolio properly.

Month 4: Machine learning foundations + first ML project (regression)

• Learn model basics, evaluation metrics, and regression algorithms. Build one complete regression project and include error analysis and improvements, not only model training.

Month 5: Classification + feature engineering + second ML project

• Learn classification models, imbalanced data handling, and feature engineering. Build a classification project that uses the right metrics and shows clear model improvement steps.

Month 6: End-to-end project + interview preparation + job applications

• Build one strong end-to-end project that is well-documented and looks job-ready. In parallel, revise SQL, statistics, and ML fundamentals, prepare project walkthroughs, update your resume, and start applying consistently.

Common Mistakes Beginners Should Avoid

Many beginners work hard but still feel stuck because they follow an unstructured approach. Avoiding these mistakes will save you months of effort and help you progress faster.

• Starting with deep learning or advanced AI too early can create confusion, as you may not understand why models behave the way they do. Build strong fundamentals first, then go deeper.
• Watching too many tutorials without building projects: Data Science is a skill learned through practice. If you are only watching content, you will feel confident during the video but struggle when you work on your own.
• Ignoring SQL and focusing only on Python: In real jobs, SQL is used daily. If you avoid SQL, your job readiness drops sharply even if your ML knowledge is decent.
• Treating EDA as only charts, not thinking: EDA is about understanding the data and forming reasoning. Charts without interpretation do not show Data Science ability.
• Using accuracy as the only metric: Many real problems have imbalanced classes. In such cases, accuracy can look high even when the model is poor. Learn precision, recall, and F1 early.
• Copy-pasting code without understanding: This creates “portfolio projects” that you cannot explain. In interviews, that becomes a serious weakness.
• Not documenting work properly: A project without a clear README, problem statement, and results summary looks incomplete, even if the code is good.
• Trying to learn everything at the same time: Data Science has many subfields. You will progress faster if you follow the right sequence and stick to one roadmap.
• Not revising basics regularly: Stats, SQL, and ML concepts fade if you do not revise. Short revision cycles make you interview-ready faster.

Top 5 resources to Learn Data Science in 2026

Python for Data Analysis (Book) by Wes McKinney

• This is one of the most practical resources for learning Pandas and real-world data handling. It is especially useful once you have learned basic Python and want to become confident in cleaning, transforming, and analysing datasets.

Kaggle (Learn + Notebooks + Datasets)

• Kaggle is one of the best places to practice because you get free datasets, short beginner lessons, and public notebooks that show how other people solve problems. It is also a strong place to publish your work and build visibility.

SQLBolt (for SQL fundamentals)

• If you want a beginner-friendly way to learn SQL through interactive exercises, SQLBolt is a clean starting point. It helps you build query-writing confidence quickly before you move to harder SQL practice sets.

Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (Book) by Aurélien Géron

• This is a strong resource for machine learning fundamentals with practical implementation. As a beginner, you can focus first on the Scikit-Learn parts for classical ML, and use the deep learning sections later.

Google Machine Learning Crash Course (Free)

• This is a structured beginner course that explains core ML ideas in a simple way, with exercises. It is useful for building intuition on concepts like loss functions, training, and evaluation without becoming overly theoretical.

Expert Corner

Learning Data Science in 2026 becomes much easier when you follow the right sequence and focus on practice, not only theory. Start by building a foundation in statistics, Python, and SQL, then move into EDA and visualization so you learn how to understand real datasets and communicate insights clearly. Once that base is strong, machine learning will feel logical instead of confusing, and you will be able to build models, evaluate them correctly, and improve them with feature engineering.

The biggest differentiator for beginners is not how many courses you finish, but how many strong, well-documented projects you build. If you complete at least four portfolio projects and learn to explain your workflow and decisions clearly, you will be in a strong position for internships and entry-level roles. Use the roadmap as your guide, stay consistent, and keep your learning project-based, and you will gradually become confident and job-ready in Data Science.

The post How to learn Data Science? | Complete Roadmap for Beginners 2026 appeared first on Vskills Blog.

In this blog, we’ll explore the top companies hiring data scientists in 2025, what they look for in candidates, and how you can align your skills to land a role in these high-impact organizations.

Responsibilities of a Data Scientist

A data scientist is a professional who collects, analyzes, and interprets large amounts of data to discover trends and patterns. Their role is crucial in deriving actionable insights that drive business decisions.

Key responsibilities of a data scientist include:

• Data Collection and Preparation: Gathering data from various sources, cleaning, and transforming it into a suitable format for analysis.
• Data Exploration and Analysis: Applying statistical methods and machine learning algorithms to uncover patterns, trends, and correlations within the data.
• Model Building: Developing predictive models to forecast future trends or outcomes based on historical data.
• Data Visualization: Creating clear and informative visualizations to communicate findings effectively to stakeholders.
• Problem Solving: Utilizing data-driven insights to address business challenges and identify opportunities.
• Collaboration: Working closely with cross-functional teams to understand business requirements and translate them into data-driven solutions.
• Staying Updated: Keeping abreast of the latest advancements in data science and technology.

In essence, a data scientist acts as a bridge between data and business strategy, providing valuable insights that contribute to organizational success. Let’s now look at the top 10 Companies hiring Data Analysts.

Best companies hiring Data Scientist

The demand for data scientists has skyrocketed as organizations increasingly recognize the power of data-driven insights. These ten companies are the best companies to work as data scientists, at the forefront of data science recruitment, offering exciting opportunities for professionals in the field.

Tech Industry Powerhouses

• Google: Renowned for its data-centric culture, Google employs data scientists to develop cutting-edge algorithms, enhance search capabilities, and drive product innovation.
• Meta: With a massive user base, Meta leverages data science to understand user behavior, personalize content, and optimize advertising campaigns.
• Amazon: As a global e-commerce and cloud computing leader, Amazon relies on data scientists to optimize operations, improve customer experience, and inform strategic decisions.
• Microsoft: A diverse technology conglomerate, Microsoft offers data scientists opportunities across various domains, from product development to customer support.
• Apple: Known for its focus on user experience, Apple employs data scientists to analyze customer behavior, inform product design, and drive marketing strategies.

Industry Innovators

• Netflix: As a streaming giant, Netflix utilizes data science to understand viewer preferences, recommend content, and improve platform performance.
• Uber: This ride-sharing company relies on data science to optimize operations, set pricing, and enhance the rider experience.
• Airbnb: A global hospitality platform, Airbnb employs data scientists to analyze booking trends, optimize pricing, and personalize guest experiences.
• Salesforce: A leading CRM platform, Salesforce utilizes data science to help businesses understand customer behavior, improve sales, and enhance marketing efforts.
• LinkedIn: As a professional networking platform, LinkedIn employs data scientists to analyze user behavior, provide personalized recommendations, and inform talent acquisition strategies.

Tech giants like Google, Meta (formerly Facebook), and Amazon consistently top the list of highest-paying companies for data scientists. These companies invest heavily in data-driven initiatives, leading to competitive salaries and lucrative compensation packages. Financial institutions, particularly those involved in trading and investment banking, also offer substantial rewards for data science talent due to the critical role data plays in their operations.

Beyond the tech industry, companies in healthcare, e-commerce, and other data-intensive sectors are increasingly recognizing the value of data scientists and are willing to pay premium salaries to attract top talent.

Let’s now look at some of the skills that can help you land your dream job in your dream company.

Skills Required to Get into Top Companies for Data Scientist

To excel as a data scientist, a strong foundation in both technical and soft skills is essential.

Technical Skills:

• Programming languages: Python and R are the most commonly used languages for data manipulation, analysis, and modeling.
• Statistics and probability: Understanding statistical concepts is crucial for data interpretation and model building.
• Machine learning: Proficiency in algorithms and techniques to build predictive models.
• Data mining: Extracting valuable insights from large datasets.
• Data visualization: Creating clear and informative visual representations of data.
• Database management: Handling and querying large datasets efficiently.
• Cloud computing: Utilizing cloud platforms for data storage and processing.

Soft Skills:

• Problem-solving: Identifying and addressing complex data-related challenges.
• Communication: Effectively conveying insights to both technical and non-technical audiences.
• Critical thinking: Analyzing data objectively and drawing meaningful conclusions.
• Business acumen: Understanding the business context and aligning data analysis with organizational goals.
• Collaboration: Working effectively with teams from different departments.

A combination of these skills will equip you to thrive in the dynamic field of data science and secure a job in one of the top data scientist companies.

Tips for Landing a Data Science Job

Landing a data science job requires combining technical skills, soft skills, and strategic job hunting. Here are some tips to increase your chances of success:

Build a Strong Foundation

• Master the fundamentals: Ensure a solid grasp of statistics, probability, and programming languages like Python and R.
• Develop a strong portfolio: Showcase your skills through personal projects or data competitions.
• Continuous learning: Stay updated with the latest trends and technologies in the field.

Tailor Your Job Search

• Identify target companies: Research companies that align with your career goals and industry interests.
• Understand job descriptions: Carefully analyze job descriptions to tailor your resume and cover letter accordingly.
• Networking: Build relationships within the data science community through conferences, online forums, and social media.

Ace the Interview Process

• Technical skills assessment: Prepare for coding challenges, statistical problem-solving, and machine learning questions.
• Behavioral questions: Practice answering questions about your problem-solving abilities, teamwork, and project management.
• Data storytelling: Be prepared to present your findings and insights in a clear and compelling manner.

Additional Tips

• Online presence: Create a professional LinkedIn profile and showcase your work on platforms like GitHub.
• Data science certifications: Consider obtaining relevant certifications to enhance your credibility.
• Practice data storytelling: Develop the ability to communicate complex data insights to non-technical audiences.

By following these tips and consistently honing your skills, you’ll increase your chances of landing your dream data science job.

How Vskills Certification Can Help You Become a Data Scientist

Breaking into data science can feel overwhelming—especially when you’re trying to build skills across multiple domains like statistics, programming, and machine learning. That’s where Vskills Data Science Certification steps in as your launchpad.

Structured Learning for Core Skills

Vskills offers a well-curated certification that covers the key building blocks of data science, including Python programming, data wrangling, data visualization, machine learning algorithms, and more. The content is beginner-friendly yet comprehensive—perfect for those transitioning from non-technical fields.

Recognized by Employers

Vskills is a Government of India initiative, and its certifications are respected by recruiters and companies across India. Listing a Vskills Data Science Certification on your résumé helps signal your commitment, credibility, and readiness to take on real-world data challenges.

Learn at Your Own Pace

Whether you’re a student, a working professional, or switching careers, the self-paced nature of the certification gives you the flexibility to upskill without putting your life on hold.

Boost Your Interview Confidence

The certification not only equips you with theoretical knowledge but also includes practice questions and assessment tests to prepare you for job interviews and technical screenings.

Step Into Real Roles

Many certified learners have gone on to pursue roles like Data Analyst, Junior Data Scientist, Business Analyst, and even AI/ML Engineer after completing the certification. It’s an ideal stepping stone before diving into advanced tools or specialized domains.

Conclusion

The demand for skilled data scientists continues to soar, making it a competitive field. By understanding the top companies hiring data scientists, their specific roles, and essential skills, you’ve taken a significant step towards a successful career in this exciting domain. Remember, continuous learning, a strong portfolio, and effective job search strategies are vital for landing your dream data science position. As the data-driven world evolves, so too will the opportunities for those equipped with the right skills and knowledge.

The post Top Companies Hiring Data Scientist 2025 appeared first on Vskills Blog.

In this blog, we’ve put together the top 100 Power BI interview questions to help you ace your interview in 2025. We’ll cover the basics, get into some more intermediate questions, and even tackle advanced and scenario-based questions so you’re ready for anything they throw your way.

Why Prepare for Power BI Interview Questions in 2025?

In 2025, Power BI will continue to be a key tool in data analytics, helping companies turn complex data into clear insights. As businesses rely more on data to make decisions, the need for skilled Power BI professionals is growing fast. Preparing for Power BI interview questions not only helps you review important concepts but also keeps you updated on the latest trends, new features, and best practices.

Understanding advanced Power BI features like DAX formulas, data modeling, and custom visuals gives you a competitive edge, showing employers that you can handle complex data tasks. Staying up-to-date with Power BI’s latest updates means you’ll be ready to work smarter and make the most of new functionalities that could make a real impact.

Basic Power BI Interview Questions

Here are some fundamental Power BI questions to get you started, along with quick, easy-to-understand answers.

1. What is Power BI?

Power BI is a Microsoft tool used for data visualization and business intelligence. It helps users connect to various data sources, create interactive dashboards, and share insights.

2. What are the key components of Power BI?

The main components are Power BI Desktop, Power BI Service, Power BI Mobile, Power Query, and Power BI Report Server.

3. How is Power BI different from Excel?

Power BI focuses more on data visualization and advanced analytics, while Excel is primarily for data manipulation and basic charting.

4. What is Power BI Desktop?

Power BI Desktop is a Windows application where users create and design reports and visuals. It’s the main workspace for building Power BI dashboards.

5. What is the Power BI Service?

Power BI Service is the online version of Power BI where users can publish, share, and collaborate on dashboards and reports.

6. What types of data sources can Power BI connect to?

Power BI connects to a wide range of data sources, including Excel, SQL databases, cloud services (like Azure), and online services (like Google Analytics).

7. What is DAX in Power BI?

DAX (Data Analysis Expressions) is a formula language used in Power BI for creating custom calculations and measures.

8. What is a dashboard in Power BI?

A dashboard is a single-page summary of data created using visuals from one or multiple reports. It provides a high-level view of key metrics.

9. What is a report in Power BI?

A report is a collection of multiple pages of visuals and data insights that users can interact with to explore data more deeply.

10. What are visuals in Power BI?

Visuals are graphical representations of data, such as charts, maps, and tables, that help users interpret information easily.

11. What is Power Query?

Power Query is a data connection tool within Power BI used to connect to, transform, and clean data before loading it into the report.

12. What is the Power BI mobile app?

Power BI mobile app allows users to view and interact with their dashboards and reports on mobile devices.

13. Can Power BI be used for real-time data analysis?

Yes, Power BI supports real-time data updates through streaming datasets and live data sources.

14. What is the difference between Power BI Free and Power BI Pro?

Power BI Free allows individual users to create and view reports, while Power BI Pro enables sharing, collaboration, and additional features.

15. What is the Power BI Gateway?

The Power BI Gateway allows Power BI Service to securely access on-premises data for refreshing reports and dashboards.

16. What is the use of Power BI Report Server?

Power BI Report Server is an on-premises server for storing, managing, and sharing Power BI reports without using the cloud.

17. What are custom visuals in Power BI?

Custom visuals are additional visualization types created by developers or available in the Power BI marketplace to enhance reports.

18. What is Row-Level Security (RLS) in Power BI?

RLS restricts data access for specific users, allowing only authorized users to see certain parts of the data in a report.

19. What is a measure in Power BI?

A measure is a calculation created using DAX to aggregate data, such as sum, average, or count.

20. What is a calculated column in Power BI?

A calculated column is a custom column created using DAX that performs calculations on rows of data within a table.

21. How can you share reports in Power BI?

You can share reports via Power BI Service by sharing links, embedding reports, or creating dashboards that are accessible to others.

22. What are filters in Power BI?

Filters limit the data shown in visuals, pages, or reports, helping users focus on specific data points.

23. What are slicers in Power BI?

Slicers are a type of visual that lets users interactively filter data on a report page.

24. Can you embed Power BI reports into other applications?

Yes, using Power BI Embedded or iframe codes, you can embed reports into web applications, SharePoint, or other platforms.

25. What is drill-through in Power BI?

Drill-through allows users to click on data points in a visual to see related details on a separate, focused report page.

Intermediate Power BI Interview Questions

These questions cover more advanced functionalities, data modeling, and visualization techniques in Power BI, providing a deeper look into what’s needed for effective report building and data analysis.

1. What is DAX, and why is it important in Power BI?

DAX (Data Analysis Expressions) is a formula language used to perform calculations and build custom measures and columns, making it essential for creating complex data models.

2. Explain the use of Power Query in Power BI.

Power Query is used for data transformation and preparation, allowing users to clean, reshape, and combine data from various sources before loading it into Power BI.

3. How can you optimize data refresh times in Power BI?

To optimize refresh times, you can reduce data volume by removing unnecessary columns/rows, use query folding, apply incremental refresh, and ensure efficient data modeling.

4. What is query folding, and why is it important?

Query folding allows Power BI to offload transformations to the data source, which speeds up data refresh and processing by leveraging the source’s capabilities.

5. How does Power BI handle large datasets?

Power BI can handle large datasets using features like incremental refresh, aggregations, and DirectQuery mode, which connect directly to data sources without importing.

6. What is DirectQuery, and when would you use it?

DirectQuery allows Power BI to query data directly from the source without importing it. It’s used for large datasets where real-time data is needed or data volumes exceed Power BI’s limits.

7. What is a calculated table in Power BI, and why would you use it?

A calculated table is created using DAX and can be used when you need a table based on DAX calculations, especially helpful in complex data models.

8. Explain relationships in Power BI.

Relationships in Power BI link tables within a data model, allowing users to create reports with data from multiple sources. They can be one-to-many, many-to-one, or many-to-many.

9. What is the purpose of cross-filtering?

Cross-filtering controls how filters apply across relationships in the data model, determining the flow of data between related tables.

10. How can you manage performance in Power BI models?

Performance can be managed by reducing data size, optimizing DAX formulas, using query folding, limiting visuals, and using the Performance Analyzer tool.

11. What is the Performance Analyzer tool in Power BI?

Performance Analyzer is a built-in tool that helps identify slow-performing visuals and DAX queries, allowing you to optimize report performance.

12. What are aggregations, and how do they improve performance?

Aggregations store summary data, which reduces query complexity and improves performance, especially for large datasets.

13. How do you use DAX functions like CALCULATE?

CALCULATE is a DAX function that applies filters to modify existing calculations, enabling more complex data analysis.

14. What is the difference between SUM and SUMX in DAX?

SUM aggregates a single column, while SUMX iterates over a table, allowing calculations across rows based on specific conditions.

15. What is data modeling in Power BI, and why is it important?

Data modeling organizes tables and relationships to create a cohesive structure for data analysis, essential for creating accurate reports and insights.

16. How can you implement Row-Level Security (RLS) in Power BI?

RLS restricts data access by defining roles with DAX filters, limiting what data users can view based on their permissions.

17. What is incremental refresh, and why would you use it?

Incremental refresh only updates new or modified data rather than reloading the entire dataset, improving performance and reducing refresh time.

18. Explain the concept of measures in Power BI.

Measures are dynamic calculations created using DAX, which adjust based on filters and slicers, making them key to interactive reports.

19. What are hierarchies in Power BI, and how are they used?

Hierarchies organize data into levels (e.g., Year > Quarter > Month) for easy drilling down in visuals, providing deeper analysis.

20. How do you use bookmarks in Power BI?

Bookmarks save the current state of visuals and filters, allowing users to switch between different views within a report.

21. What are parameters in Power BI, and when would you use them?

Parameters let users input values to customize data loading, such as dynamically changing data sources or filtering data during load.

22. How can you create custom tooltips in Power BI?

Custom tooltips provide additional information when hovering over visuals by using custom pages designed to show relevant data.

23. What is the purpose of the Q&A visual in Power BI?

The Q&A visual allows users to ask questions in natural language, generating visuals based on the data model’s content.

24. What is the significance of the Common Data Model (CDM) in Power BI?

CDM standardizes data structure, making it easier to integrate and analyze data from different systems consistently.

25. What is Power BI Embedded?

Power BI Embedded allows developers to embed Power BI reports and dashboards into applications, providing insights to users within other platforms.

26. How do you handle null or missing values in Power BI?

Missing values can be handled by replacing them with default values, filtering them out, or using DAX functions like IF and COALESCE.

27. What is R integration in Power BI?

R integration allows you to use R scripts for advanced analytics and data visualization, expanding Power BI’s capabilities with statistical analysis.

28. How does Python integration work in Power BI?

Python scripts can be used for data manipulation and visualization within Power BI, enabling more complex data processing.

29. What are themes in Power BI, and how are they applied?

Themes apply custom colors, fonts, and styling across a report, ensuring consistent visual design.

30. What is composite modeling in Power BI?

Composite modeling allows combining DirectQuery and import modes in a single report, offering flexibility with large datasets.

31. How can you use KPI visuals in Power BI?

KPI visuals track progress toward specific goals, making it easy to see whether performance metrics are being met.

32. What is a dataflow in Power BI?

A dataflow is a collection of queries saved in Power BI Service, used for reusable and centralized data preparation.

33. Explain drill-down and drill-through in Power BI.

Drill-down shows lower-level details within the same visual, while drill-through navigates to a detailed report page based on a selection.

34. What is the Quick Insights feature in Power BI?

Quick Insights automatically identifies patterns and trends in data, providing quick analytical insights with a single click.

35. How does conditional formatting work in Power BI?

Conditional formatting changes visual appearance based on data values, making it easy to highlight trends or outliers.

Advanced Power BI Interview Questions

Here are advanced Power BI questions that dive into complex DAX functions, data architecture, performance optimization, and integrations, helping you showcase in-depth knowledge of the tool.

1. How do you use CALCULATE in DAX for advanced filtering?

CALCULATE modifies filters in DAX expressions, allowing complex calculations by applying multiple filter conditions or ignoring specific filters.

2. Describe how to optimize large datasets in Power BI.

To optimize large datasets, use techniques like aggregations, incremental refresh, data reduction, efficient DAX measures, and DirectQuery for real-time access.

3. What are the steps to set up row-level security (RLS) in Power BI?

Set up RLS by defining roles, writing DAX filter expressions for each role, and assigning users to roles in Power BI Service to restrict access to data.

4. What is the purpose of the ALLEXCEPT function in DAX?

ALLEXCEPT removes filters from all columns in a table except specified ones, useful in scenarios requiring certain filters to remain active.

5. Explain the concept of dynamic RLS in Power BI.

Dynamic RLS uses DAX functions and user information (such as USERNAME()) to dynamically filter data based on the logged-in user’s identity.

6. What is the benefit of using aggregate tables in Power BI?

Aggregate tables store summarized data, which improves query performance, especially for large datasets where detailed level data isn’t always needed.

7. How does Power BI handle circular dependencies in DAX?

Power BI prevents circular dependencies by restricting the model, as circular references in DAX calculations can cause errors and slow performance.

8. Explain the use of context transition in DAX.

Context transition occurs when row context is converted to filter context, especially in CALCULATE and other functions that change row-based calculations.

9. What are the best practices for creating efficient data models in Power BI?

Use a star schema, minimize relationships, avoid calculated columns when possible, and simplify the model to improve performance and manageability.

10. How do you use the ALLSELECTED function in DAX?

ALLSELECTED returns all values selected in the filter context, enabling analysis of trends within specific subsets of filtered data.

11. What is the difference between EARLIER and EARLIEST functions in DAX?

EARLIER retrieves values from a previous row context, useful in nested calculations; EARLIEST extends it to handle multiple row contexts.

12. How do you implement custom time intelligence in Power BI?

Create custom time intelligence by defining calculated columns or measures with DAX functions like DATEADD, SAMEPERIODLASTYEAR, or TOTALYTD.

13. Explain the use of Variables in DAX.

Variables in DAX store values for reuse within expressions, improving readability and potentially optimizing performance by reducing repeated calculations.

14. How can you optimize performance when using DirectQuery in Power BI?

Optimize DirectQuery by limiting complex calculations, minimizing visuals on a single page, reducing data loads, and using indexed columns in the source.

15. What is the TREATAS function in DAX, and when would you use it?

TREATAS applies the values of one table’s columns as filters on another table, helpful in scenarios without direct relationships.

16. How does aggregating data affect performance in Power BI?

Aggregating data reduces the volume of data processed, improving performance by summarizing detailed information where full granularity is unnecessary.

17. What are calculation groups, and how do they enhance DAX modeling?

Calculation groups allow predefined calculations (like time intelligence) to be reused across measures, simplifying the model and reducing duplication.

18. How do you configure Power BI Embedded for application integration?

Use Power BI Embedded to integrate reports within apps by setting up workspace collections, authentication, and managing permissions with the Power BI API.

19. How can you use the RANKX function in DAX for custom rankings?

RANKX ranks values based on criteria in the table, useful for ranking sales, performance metrics, or other measures within a specific context.

20. What is query folding, and why is it crucial for performance in Power BI?

Query folding sends transformations back to the data source, optimizing performance by handling filtering, joins, and aggregations at the source level.

21. How does the SUMMARIZE function in DAX work, and when would you use it?

SUMMARIZE creates summary tables, which are useful for aggregating data on specific columns and performing further analysis or calculations.

22. What is XMLA endpoint, and how does it enhance Power BI?

XMLA endpoint allows third-party tools to connect directly to Power BI data models, supporting enterprise-grade data management and external data processing.

23. Explain how to create a data lake integration in Power BI.

Integrate a data lake by connecting Power BI to Azure Data Lake or similar, enabling storage and analysis of large datasets outside of Power BI limits.

24. What is Composite Model, and how does it improve data modeling in Power BI?

Composite Model allows combining import and DirectQuery tables in one report, enabling flexibility in data handling and performance optimization.

25. What are AI visuals in Power BI, and how do they work?

AI visuals like Key Influencers and Decomposition Tree use AI algorithms to provide insights on data patterns, making advanced analytics more accessible.

26. How does incremental refresh work in Power BI Premium?

Incremental refresh updates only new or changed data, improving performance for large datasets by reducing load times on each refresh.

27. What is the purpose of the GENERATE function in DAX?

GENERATE combines tables by iterating over each row, useful for creating detailed tables in data modeling and simulations.

28. How does setting up dataflows benefit an organization in Power BI?

Dataflows centralize and automate data preparation, making cleaned data reusable across multiple reports, enhancing data consistency and efficiency.

29. How can you handle many-to-many relationships effectively in Power BI?

Use bridge tables or composite models to manage many-to-many relationships, ensuring accurate filtering without duplicating data.

30. What are paginated reports, and when are they useful?

Paginated reports are detailed reports formatted for printing, ideal for creating large, structured reports with precise control over layout.

31. How can you leverage Power Automate with Power BI?

Power Automate integrates with Power BI to trigger workflows based on data events, such as sending alerts or updating records when thresholds are met.

32. What is the USE relationship function in DAX?

USERELATIONSHIP activates inactive relationships temporarily within DAX expressions, allowing selective use of multiple relationships.

33. How do you use Drillthrough filters in Power BI for better reporting?

Drillthrough filters let users navigate from summary visuals to detailed report pages based on selected data points, adding depth to reports.

34. Explain the role of custom connectors in Power BI.

Custom connectors allow Power BI to access data from sources not natively supported, expanding data integration options.

35. What is the purpose of aggregations in large datasets, and how do they work in Power BI?

Aggregations create summary tables that reduce data processing for large datasets, improving performance by using summary data in queries.

Power BI Scenario-Based Questions

These questions are designed to assess how you apply your Power BI knowledge to real-world situations, showing problem-solving skills and practical experience.

1. How would you handle a data refresh issue in Power BI Service?

First, check the data source connection and credentials. If they’re correct, verify the gateway configuration for on-premises data. Next, review the refresh settings and logs in Power BI Service for error details. If the data model is too large, consider optimizing it by reducing data volume or enabling incremental refresh.

2. Describe a time when you used DAX to solve a business problem.

Share a specific example, like using DAX to calculate year-over-year growth, identifying customer retention rates, or building a dynamic KPI to track performance, explaining the DAX functions and logic applied to get the needed insights.

3. If a report is running slow, how would you approach troubleshooting?

Start by using the Performance Analyzer to identify slow visuals and DAX queries. Simplify complex visuals, reduce data volume, optimize DAX calculations, and check for efficient data modeling practices. Consider aggregations, filters, or DirectQuery if appropriate.

4. How would you implement Row-Level Security for a report where users should only see their regional data?

Set up a role in Power BI Desktop with DAX filters on the region column to limit data by user region. Use USERNAME() or USERPRINCIPALNAME() in DAX to match each user’s region and assign roles in Power BI Service.

5. Imagine a scenario where you need to combine data from multiple sources. How would you approach it?

Use Power Query to connect to each data source and perform transformations as needed. Merge or append the data based on relationships, ensuring consistent field types, column naming, and granularity. Then, load the data model into Power BI and set up relationships as required.

6. How would you handle a request to add a new data source to an existing Power BI report?

Connect to the new data source in Power Query, clean and transform the data if needed, then load it into the model. Update relationships and refresh the report. Test to ensure the new data integrates seamlessly without disrupting existing visuals.

7. You’ve been asked to create a dashboard with KPIs, targets, and conditional alerts. How would you do it?

Create measures for KPIs using DAX, set up conditional formatting to highlight performance vs. targets, and add gauge or KPI visuals. Use Power Automate to trigger alerts if certain conditions are met, like reaching or missing a target.

8. What would you do if your manager requested a report that should only show data for the past 6 months but retain the full dataset for analysis?

Use Power Query or DAX filters to limit the data displayed to the last 6 months in visuals but keep the complete dataset in the model. This approach keeps the visuals lightweight while retaining data for drill-down or historical analysis if needed.

9. How would you approach integrating Power BI reports into a company’s internal application?

Use Power BI Embedded to integrate reports, requiring setup of a Power BI workspace and API authentication. Ensure permissions are properly managed, and test to confirm that embedded reports display securely within the internal application.

10. Describe a scenario where you used Power BI to automate a repetitive task.

Share an example, such as setting up a dataflow to automate data preparation or using Power Automate to update a Power BI dataset on a schedule. Explain how this approach saved time, reduced manual effort, or improved data accuracy.

Tips for Cracking Power BI Interviews in 2025

If you’re preparing for a Power BI interview, here are some practical tips to help you stand out:

• Practice with Real-World Datasets
  • Familiarize yourself with datasets beyond simple examples. Use publicly available datasets (like those from Kaggle or data.gov) to practice creating reports, dashboards, and applying real-world scenarios. This will help you handle complex data models and improve your problem-solving skills.
• Stay Updated on Power BI Features
  • Power BI regularly releases new features and updates, so staying up-to-date is essential. Check the Power BI blog, subscribe to newsletters, or join the Power BI Insider program to stay informed. Understanding new features like Dataflows, Paginated Reports, or the latest AI visuals can give you an edge.
• Master DAX and Power Query
  • DAX and Power Query are core to Power BI’s functionality. Practice using advanced DAX functions and get comfortable with Power Query transformations. Interviewers often test your knowledge of these areas, especially in intermediate to advanced interviews.
• Engage with the Power BI Community
  • The Power BI Community, including forums like Stack Overflow, Reddit, and LinkedIn groups, is invaluable. By engaging, you can ask questions, learn from others’ solutions, and keep up with best practices. Community forums also help you understand real-world challenges and solutions in Power BI.
• Work on Performance Optimization
  • Performance can be a big focus in interviews, especially for large datasets. Practice optimizing data models, DAX formulas, and visuals. Learn how to use tools like the Performance Analyzer and understand techniques like query folding, aggregations, and incremental refresh to handle large datasets efficiently.
• Learn How to Explain Your Reports
  • It’s important not only to build reports but also to communicate your insights clearly. Practice explaining your visuals, design choices, and insights to non-technical audiences. Interviewers often look for candidates who can translate technical reports into business value.
• Get Comfortable with Scenario-Based Questions
  • Many interviewers ask scenario-based questions to test your problem-solving skills. Review common scenarios, such as troubleshooting data refresh issues, implementing Row-Level Security, or handling complex calculations. Practicing these will help you think on your feet during the interview.
• Create a Portfolio of Your Work
  • If possible, create a portfolio of Power BI projects, either from previous jobs or personal projects. Having a portfolio demonstrates your experience, creativity, and ability to produce professional reports. It’s also a great way to visually showcase your skills in an interview.
• Understand Integrations and APIs
  • Power BI often integrates with other tools like Azure, SharePoint, and Power Automate. Familiarize yourself with Power BI’s integration capabilities and API functionalities, as interviewers may ask about automating tasks or embedding Power BI in other applications.
• Prepare for Questions About Business Impact
  • Be ready to discuss how your Power BI reports can help solve business problems. Practice connecting your technical skills to real business outcomes, such as improving decision-making, optimizing processes, or identifying trends. This approach shows your understanding of Power BI’s impact beyond the technical level.

These tips can help you become a well-rounded candidate prepared to answer technical and business-related questions, setting you up for success in your Power BI interviews 2025.

Final Words

Preparing for a Power BI interview can feel like a big task, but with the right practice and resources, you can go in confidently. Power BI is a powerful tool that businesses rely on more and more, so taking the time to master the basics, dive into advanced features, and learn how to solve real-world scenarios will set you apart from other candidates.

Whether you’re just starting or looking to step up your Power BI game, these questions and tips will help you show off your skills, highlight your problem-solving abilities, and demonstrate your value to potential employers. Remember, stay curious, keep practicing, and don’t be afraid to explore new features as Power BI evolves. Good luck, and go ace that interview!

The post Top 100 Power BI Interview Questions 2025 appeared first on Vskills Blog.

In today’s data-driven economy, data analysts are the architects of success. They empower businesses to understand their customers better, optimise operations, identify new opportunities, and mitigate risks. By providing data-backed recommendations, data analysts contribute significantly to a company’s bottom line.

What is a data analyst?

A data analyst is a skilled professional who transforms raw data into actionable insights. They employ statistical methods such as regression analysis, hypothesis testing, and machine learning algorithms, and various software tools like Excel, R, and Python to uncover patterns, trends, and correlations within vast datasets. These insights are then communicated effectively to inform strategic decision-making.

Data Analyst Job Description

So, what does a Data Analyst do? Being a Data Analyst, you would be required to carry out these responsibilities –

1. Data Collection

A significant part of a data analyst’s role involves gathering data from diverse sources. This can include:

• Collecting data from internal databases: Extracting information from company records, sales figures, customer data, and operational metrics.
• Utilising external data sources: Accessing publicly available datasets, industry reports, economic indicators, and social media analytics.
• Conducting surveys and polls: Gathering primary data to address specific research questions.

2. Data Cleaning and Preparation

Raw data often contains inconsistencies, errors, and missing values. Data analysts spend considerable time cleaning and preparing data for analysis:

• Handling missing data: Imputing missing values or excluding irrelevant data points.
• Identifying and correcting errors: Ensuring data accuracy and reliability.
• Formatting and standardising data: Preparing data for analysis and visualisation.
• Creating data dictionaries: Documenting data definitions and structures.

3. Data Analysis

The core of a data analyst’s job is to extract meaningful insights from data:

• Exploratory data analysis (EDA): Summarizing data characteristics and uncovering patterns.
• Statistical analysis: Applying statistical methods to test hypotheses and draw inferences.
• Data mining: Discovering hidden patterns and relationships within large datasets.
• Predictive modelling: Building models to forecast future trends and outcomes.

4. Data Visualization

Communicating insights effectively is crucial:

• Creating visualisations: Developing charts, graphs, and dashboards to represent data visually.
• Storytelling with data: Translating complex findings into understandable narratives.
• Interactive dashboards: Building dynamic visualisations for exploratory analysis.

5. Collaboration and Communication

Data analysts often work closely with stakeholders:

• Understanding business requirements: Collaborating with business users to define analysis goals.
• Presenting findings: Communicating insights to technical and non-technical audiences.
• Providing recommendations: Offering data-driven solutions to business challenges.

Skills Required for a Data Analyst

A data analyst needs a blend of technical and soft skills to excel in their role.

1. Technical Skills

• Statistical Knowledge: A strong foundation in statistics is essential for analyzing data, testing hypotheses, and drawing meaningful conclusions.
• Programming Proficiency: Languages like Python, R, or SQL are commonly used for data manipulation, analysis, and modeling.
• Data Modeling: The ability to create and manipulate data structures to support analysis.
• Data Mining: Skills in extracting hidden patterns and knowledge from large datasets.
• Data Visualization: Proficiency in using tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) to create compelling visuals.
• Database Management: Understanding of database structures (relational, NoSQL) and SQL for data retrieval and manipulation.

2. Soft Skills

• Problem Solving: The ability to break down complex problems into smaller, manageable components.
• Critical Thinking: Evaluating information objectively and making sound judgments.
• Communication: Effectively conveying technical insights to both technical and non-technical audiences.
• Business Acumen: Understanding how data can be used to drive business decisions.
• Attention to Detail: Ensuring data accuracy and consistency.
• Curiosity: A passion for exploring data and uncovering new insights.

Moving on, let’s look at the data analyst career opportunities.

Career Path and Opportunities for Data Analysts

The field of data analytics offers a dynamic and rewarding career path with numerous growth opportunities.

1. Entry-Level Roles

• Junior Data Analyst: Responsible for data cleaning, preparation, and basic analysis tasks. Focuses on developing foundational data analysis skills and understanding business requirements.
• Data Analyst Intern: Provides hands-on experience in a data-driven environment, working on real-world projects under the guidance of experienced analysts.

2. Career Progression

With experience and skill development, data analysts can progress to more senior roles and specialised areas:

• Senior Data Analyst: Takes on complex projects, mentors junior analysts, and provides strategic insights. Requires strong analytical skills, problem-solving abilities, and effective communication.
• Data Analyst Team Lead: Oversees a team of analysts, manages projects, and ensures data quality and consistency. Leadership and project management skills are essential.
• Business Analyst: Bridges the gap between business needs and data analysis. Focuses on understanding business requirements and translating them into data-driven solutions.
• Data Scientist: Gets deeper into advanced statistical modeling, machine learning, and predictive analytics. Requires strong programming skills and a solid understanding of statistical concepts.
• Data Engineer: Focuses on building and maintaining data infrastructure, ensuring data quality and accessibility. Requires expertise in database management, data warehousing, and ETL processes.

3. Industries Hiring Data Analysts

The demand for skilled data analysts spans across various industries:

• Technology: Software development, e-commerce, internet companies
• Finance: Banking, insurance, investments, fintech
• Healthcare: Pharmaceuticals, hospitals, healthcare IT, medical research
• Marketing: Customer analytics, market research, digital marketing
• Retail: Sales analysis, inventory management, customer segmentation
• Government: Policy analysis, public administration, data-driven governance
• Consulting: Data-driven consulting services for various industries

4. Salary and Job Outlook

The data analyst role has experienced significant growth, reflecting the increasing reliance on data-driven decision-making.

Salary:

The median annual wage for data analysts in the United States was $73,010 in May 2021, according to the Bureau of Labor Statistics (BLS).

Job Outlook:

The demand for data analysts is expected to grow 22% from 2021 to 2031, much faster than the average for all occupations, according to the BLS. This growth is driven by the increasing volume and complexity of data generated by businesses and organisations.

Let’s look at a typical day in the life of a Data analyst.

A Typical Day in the Life of a Data Analyst

Alex, a data analyst at a bustling e-commerce company, starts her day with a strong coffee. Her first task is to dive into the latest sales figures. She wrangles with messy datasets, cleaning and organising the numbers to make sense of the chaos. Once the data is in shape, she employs statistical wizardry to unearth hidden patterns. A sudden spike in sales of blue widgets?

Alex is on the case! She transforms her findings into colourful charts and graphs, crafting a compelling story to share with the marketing team. The afternoon is filled with meetings, discussing potential marketing campaigns and answering questions about customer behavior. It’s a whirlwind of numbers, insights, and collaboration. As the day winds down, Alex reflects on the day’s discoveries, eager to see how her work will impact the company’s success.

Conclusion

In essence, data analysts are the storytellers of the digital age. They transform raw data into compelling narratives that drive business decisions. With a blend of technical expertise and business acumen, data analysts are crucial assets to organisations seeking to unlock the full potential of their data.

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