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.

This blog covers everything from the basics to advanced topics, SQL queries, and even tricky scenario-based questions. By the end, you’ll feel ready to tackle any question that comes your way. Let’s move straightaway to the questions.

Basic DBMS Questions

Let’s start with some simple and common questions you’ll likely encounter in any DBMS interview. These will build a strong foundation for the more advanced topics later.

1. What is DBMS? How does it differ from RDBMS?

DBMS (Database Management System) is software used to store, manage, and retrieve data efficiently. Examples include file systems and NoSQL databases.

RDBMS (Relational Database Management System), on the other hand, organizes data into tables with rows and columns and supports relationships between tables. Examples include MySQL, PostgreSQL, and Oracle.

Key Difference: RDBMS strictly uses structured formats (tables) and supports relationships, while DBMS may or may not.

2. Explain the ACID properties of a transaction.

ACID properties ensure reliability in database transactions:

• Atomicity: A transaction is all or nothing—either fully completes or doesn’t happen at all.
• Consistency: The database remains valid before and after a transaction.
• Isolation: Concurrent transactions don’t interfere with each other.
• Durability: Once a transaction is committed, it’s permanently saved, even in case of a failure.

3. Define primary key, foreign key, and candidate key.

• Primary Key: A unique identifier for each record in a table (e.g., ID number). It can’t have duplicates or null values.
• Foreign Key: A field in one table that references the primary key in another table, establishing a relationship between the two.
• Candidate Key: All possible columns that can qualify as a unique identifier. One of these becomes the primary key.

4. Differentiate between DELETE and TRUNCATE.

• DELETE: Removes specific rows based on a condition and can be rolled back (uses WHERE clause).
• TRUNCATE: Deletes all rows from a table quickly without conditions and can’t be rolled back.

Example:

DELETE FROM employees WHERE age > 50;

TRUNCATE TABLE employees;

5. What is normalization? Why is it important?

Normalization organizes data to minimize redundancy and improve efficiency. It divides large tables into smaller ones and establishes relationships.

Example: Avoid storing customer addresses in every order record—store them in a separate table and link it with a foreign key.

6. What is denormalization?

Denormalization is the process of combining tables to improve query performance, often by adding redundant data. It’s the opposite of normalization and is used when faster reads are more critical than storage efficiency.

7. Explain the difference between DBMS and file-based systems.

• DBMS: Provides structured data storage, query capabilities, and better data security.
• File-Based Systems: Store data in flat files without any structured query support.

8. What are indexes in a database?

Indexes improve the speed of data retrieval by creating pointers to specific rows in a table. Think of it like an index in a book—it helps you find content quickly.

9. What is a transaction in DBMS?

A transaction is a sequence of operations treated as a single logical unit of work. It must adhere to ACID properties.

10. What are the different types of joins in SQL?

• INNER JOIN: Returns rows with matching values in both tables.
• LEFT JOIN: Returns all rows from the left table and matching rows from the right.
• RIGHT JOIN: Returns all rows from the right table and matching rows from the left.
• FULL OUTER JOIN: Returns all rows when there’s a match in either table.

11. What is a composite key?

A composite key is a combination of two or more columns that uniquely identify a row in a table.

12. What is the difference between UNION and UNION ALL?

• UNION: Combines results of two queries and removes duplicates.
• UNION ALL: Combines results of two queries without removing duplicates.

13. What are constraints in a database?

Constraints are rules enforced on data to ensure accuracy and reliability. Examples include:

• NOT NULL: Ensures a column can’t have null values.
• UNIQUE: Ensures all values in a column are distinct.

14. What is the purpose of a view in DBMS?

A view is a virtual table created from the result of a query. It simplifies complex queries and enhances security by restricting access to specific data.

15. What is a database schema?

A schema is the structure of a database, including its tables, columns, relationships, and constraints.

16. What are triggers in a database?

A trigger is a piece of code automatically executed when a specific event occurs in a database (e.g., INSERT, UPDATE, DELETE).

17. What is the difference between HAVING and WHERE?

• WHERE: Filters rows before grouping.
• HAVING: Filters groups after aggregation.

Example:

SELECT department, COUNT(*)

FROM employees

WHERE age > 30

GROUP BY department

HAVING COUNT(*) > 5;

18. What is data redundancy? How is it managed in DBMS?

Data redundancy is the duplication of data. It’s managed using normalization to reduce storage needs and improve data consistency.

19. What is a foreign key constraint?

A foreign key constraint ensures that a column in one table corresponds to a primary key in another table, maintaining data integrity.

20. Explain the concept of a surrogate key.

A surrogate key is a system-generated unique identifier for a table row, typically a sequential number (e.g., auto-incremented ID).

Advanced DBMS Questions

Let’s level up with some advanced DBMS questions. These cover more profound concepts and are often asked to test your technical expertise in interviews.

1. Explain normalization and its different forms.

Normalization organizes database tables to reduce redundancy and improve data integrity. The forms are:

• 1NF (First Normal Form): No repeating groups; each column contains atomic values.
• 2NF (Second Normal Form): Meets 1NF + no partial dependencies (non-primary key columns depend on the whole primary key).
• 3NF (Third Normal Form): Meets 2NF + no transitive dependencies (non-primary key depends only on the primary key).
• BCNF (Boyce-Codd Normal Form): A stricter version of 3NF, handling anomalies caused by composite keys.

2. What are database indexing and its types?

Indexing improves query performance by creating a pointer to data. Types include:

• Primary Index: Based on the primary key.
• Unique Index: Ensures all values in the indexed column are unique.
• Clustered Index: Data is stored in the same order as the index.
• Non-Clustered Index: Separate structure for indexing, leaving data order unchanged.

3. How do you handle deadlocks in DBMS?

A deadlock occurs when two or more transactions block each other while waiting for resources. To handle it:

• Deadlock Prevention: Use locking strategies like “wait-die” or “wound-wait.”
• Deadlock Detection: Periodically check for deadlocks using a wait-for graph.
• Deadlock Resolution: Abort one or more transactions to break the cycle.

4. Discuss distributed databases and their challenges.

A distributed database stores data across multiple locations. Challenges include:

• Data Consistency: Ensuring all nodes reflect the same data.
• Network Latency: Slower performance due to communication between nodes.
• Fault Tolerance: Handling node failures without data loss.
• Complexity: Managing distributed architecture and queries.

5. What is a clustered vs. non-clustered index?

• Clustered Index: Physically reorders data in the table to match the index. Only one clustered index is allowed per table.
• Non-Clustered Index: Creates a separate structure to store pointers to the actual data. Multiple non-clustered indexes are allowed.

6. Explain the difference between OLTP and OLAP systems.

• OLTP (Online Transaction Processing): Manages real-time transactional data (e.g., banking). Focuses on fast, small-scale operations.
• OLAP (Online Analytical Processing): Handles complex analytical queries for reporting and decision-making (e.g., data warehouses).

7. What is sharding in databases?

Sharding divides a database into smaller, faster subsets called shards, stored on different servers. Each shard contains a portion of the data, improving performance and scalability.

8. What are materialized views?

A materialized view stores the results of a query physically, unlike a regular view. It’s used to improve query performance, especially for complex calculations.

9. Explain the CAP theorem.

The CAP theorem states that a distributed database can achieve only two of the following three:

• Consistency: Every read gets the latest data.
• Availability: Every request gets a response, even if data is not consistent.
• Partition Tolerance: The system functions even if network partitions occur.

10. What is query optimization in DBMS?

Query optimization is the process of improving the efficiency of a query by:

• Using indexes.
• Rewriting queries for better performance.
• Analyzing and adjusting the execution plan.

11. Explain transaction isolation levels.

Isolation levels determine how transactions interact. Common levels include:

• Read Uncommitted: Allows dirty reads.
• Read Committed: Prevents dirty reads but allows non-repeatable reads.
• Repeatable Read: Prevents dirty and non-repeatable reads but allows phantom reads.
• Serializable: Prevents all anomalies but is the slowest.

12. What is data partitioning?

Data partitioning divides a database into smaller, manageable pieces (partitions) to improve performance and scalability. Types include:

• Horizontal Partitioning: Divides rows across tables.
• Vertical Partitioning: Splits columns into separate tables.

13. Explain the difference between star and snowflake schemas.

• Star Schema: A central fact table connects directly to dimension tables. Simpler and faster for queries.
• Snowflake Schema: Dimension tables are normalized into multiple related tables. Reduces redundancy but is more complex.

14. What is a phantom read, and how is it handled?

A phantom read occurs when new rows are added by another transaction during your query. It’s handled using the Serializable isolation level, which locks ranges of data.

15. What is the difference between replication and mirroring?

• Replication: Copies data across multiple servers for high availability and load balancing.
• Mirroring: Creates an exact copy of a database, usually for disaster recovery.

16. What are the types of keys in DBMS?

• Super Key: Any set of attributes that uniquely identifies a row.
• Candidate Key: Minimal super key.
• Primary Key: Chosen candidate key.
• Foreign Key: Key linking one table to another.

17. Explain concurrency control in DBMS.

Concurrency control ensures that multiple transactions occur without conflicts. Techniques include:

• Locks: Shared (read) and exclusive (write).
• Timestamp Ordering: Assigns timestamps to transactions to maintain order.

18. What is a recursive query?

A recursive query refers to itself to process hierarchical or nested data structures. Commonly used in hierarchical databases like employee-manager relationships.

19. What are the advantages of NoSQL databases over RDBMS?

• Scalability: Better for large, unstructured data.
• Flexibility: Supports various data formats (JSON, key-value, etc.).
• Performance: Faster for specific use cases like caching or real-time apps.

20. What is the difference between hot and cold backups?

• Hot Backup: Performed while the database is running, usually in high-availability systems.
• Cold Backup: Done when the database is offline, ensuring a more consistent snapshot.

SQL and Query-Based Questions

Here’s a collection of SQL and query-based questions with easy-to-understand examples. These are practical questions you’ll often encounter in DBMS interviews.

1. Write an SQL query to find the second-highest salary.

SELECT MAX(salary) AS second_highest_salary

FROM employees

WHERE salary < (SELECT MAX(salary) FROM employees);

2. What is the difference between JOIN and UNION?

• JOIN: Combines data from multiple tables based on a related column.
• UNION: Combines the results of two queries into a single result set.

JOIN Example:

SELECT employees.name, departments.department_name

FROM employees

JOIN departments ON employees.department_id = departments.id;

UNION Example:

SELECT name FROM employees

UNION

SELECT name FROM contractors;

3. What is the difference between WHERE and HAVING clauses?

• WHERE: Filters rows before grouping.
• HAVING: Filters groups after aggregation.

Example:

SELECT department, COUNT(*) AS employee_count

FROM employees

WHERE age > 30

GROUP BY department

HAVING COUNT(*) > 5;

4. Write an SQL query to find duplicate records in a table.

SELECT name, COUNT(*) AS count

FROM employees

GROUP BY name

HAVING COUNT(*) > 1;

5. Write an SQL query to fetch employees whose names start with ‘A’.

SELECT *

FROM employees

WHERE name LIKE ‘A%’;

6. Write an SQL query to fetch the current date and time.

SELECT NOW();

7. What are aggregate functions? Give examples.

Aggregate functions perform calculations on a group of rows:

• SUM(): Calculates the total.
• AVG(): Finds the average.
• MAX()/MIN(): Finds the maximum or minimum.
• COUNT(): Counts rows.

Example:

SELECT department, SUM(salary) AS total_salary

FROM employees

GROUP BY department;

8. Write an SQL query to fetch the top 3 salaries.

SELECT salary

FROM employees

ORDER BY salary DESC

LIMIT 3;

9. Write an SQL query to delete duplicate rows.

DELETE FROM employees

WHERE id NOT IN (

    SELECT MIN(id)

    FROM employees

    GROUP BY name

);

10. Write an SQL query to retrieve all employees with a salary greater than the department average.

SELECT name

FROM employees e

WHERE salary > (

    SELECT AVG(salary)

    FROM employees

    WHERE e.department_id = department_id

);

11. What is the difference between DELETE and TRUNCATE?

• DELETE: Removes specific rows with conditions; can be rolled back.
• TRUNCATE: Removes all rows quickly; cannot be rolled back.

DELETE Example:

DELETE FROM employees WHERE age > 60;

TRUNCATE Example:

TRUNCATE TABLE employees;

12. Write an SQL query to count the total number of employees in each department.

SELECT department, COUNT(*) AS employee_count

FROM employees

GROUP BY department;

13. What is a self-join? Write an example.

A self-join joins a table with itself.

Example:

SELECT e1.name AS employee, e2.name AS manager

FROM employees e1

JOIN employees e2 ON e1.manager_id = e2.id;

14. Write an SQL query to fetch all employees who joined in the last 30 days.

SELECT *

FROM employees

WHERE join_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);

15. What is the difference between INNER JOIN and OUTER JOIN?

• INNER JOIN: Returns rows with matching values in both tables.
• OUTER JOIN: Returns all rows from one table, with NULLs for unmatched rows.

Example:

SELECT e.name, d.department_name

FROM employees e

LEFT JOIN departments d ON e.department_id = d.id;

16. Write an SQL query to calculate the total and average salary of employees.

SELECT SUM(salary) AS total_salary, AVG(salary) AS average_salary

FROM employees;

17. Write an SQL query to fetch employees in a specific age range (25 to 35).

SELECT *

FROM employees

WHERE age BETWEEN 25 AND 35;

18. Write an SQL query to display employee names along with the number of projects they are assigned.

SELECT e.name, COUNT(p.id) AS project_count

FROM employees e

JOIN projects p ON e.id = p.employee_id

GROUP BY e.name;

19. Write an SQL query to fetch the maximum and minimum salary in each department.

SELECT department, MAX(salary) AS max_salary, MIN(salary) AS min_salary

FROM employees

GROUP BY department;

20. What is a CTE (Common Table Expression)? Write an example.

A CTE is a temporary result set used within a query.

Example:

WITH DepartmentSalary AS (

    SELECT department, AVG(salary) AS average_salary

    FROM employees

    GROUP BY department

)

SELECT *

FROM DepartmentSalary

WHERE average_salary > 50000;

DBMS Tools and Technologies

In this section, we’ll explore popular DBMS tools, cloud-based platforms, and how DBMS integrates with modern big data technologies. These questions are perfect for showcasing your knowledge of current tools and trends.

1. What is Oracle DB, and why is it widely used?

Oracle DB is a relational database management system known for its scalability, robustness, and advanced features like:

• High availability with RAC (Real Application Clusters).
• Strong data security.
• PL/SQL for custom stored procedures.

2. What are the key features of MySQL?

MySQL is an open-source RDBMS popular for:

• High performance.
• Ease of use with tools like phpMyAdmin.
• Scalability for web-based applications (e.g., WordPress).
• Support for replication and clustering.

3. What is PostgreSQL, and how is it different from MySQL?

PostgreSQL is an advanced, open-source RDBMS with:

• Support for complex queries and JSON/JSONB data types.
• ACID compliance and better handling of concurrency.
• Extensibility with user-defined data types and functions.

Difference: PostgreSQL is better for complex analytics, while MySQL is often faster for simple read-heavy applications.

4. What is MongoDB, and why is it popular?

MongoDB is a NoSQL database known for:

• Storing unstructured or semi-structured data in JSON-like documents.
• Horizontal scalability via sharding.
• Flexibility for use cases like content management, IoT, and real-time analytics.

5. What are the use cases for cloud-based DBMS platforms like AWS RDS?

AWS RDS (Relational Database Service) supports multiple DB engines (MySQL, PostgreSQL, Oracle, etc.). Key benefits:

• Fully managed (no manual backups or updates).
• Scalability and high availability.
• Use cases include e-commerce platforms, mobile apps, and data warehouses.

6. What is DynamoDB, and how does it differ from traditional RDBMS?

DynamoDB is a NoSQL database by AWS designed for low-latency, high-scale applications. It uses key-value and document-based storage, unlike relational databases that use tables and rows.

7. How does DBMS integrate with big data tools like Hadoop or Spark?

• Hadoop: DBMS can export data to Hadoop Distributed File System (HDFS) for processing large datasets.
• Spark: Connectors like JDBC allow DBMS systems to integrate with Spark for real-time analytics.

8. What are the advantages of Google BigQuery?

Google BigQuery is a serverless, cloud-based data warehouse known for:

• Fast SQL querying of large datasets.
• Built-in ML capabilities for predictive analytics.
• Integration with Google Cloud services and tools.

9. How is MySQL different from MariaDB?

MariaDB is a fork of MySQL, created after Oracle acquired MySQL. Differences include:

• Enhanced performance and security features.
• Open-source development (no proprietary features like in Oracle’s MySQL).
• Compatibility with newer SQL standards.

10. What is CockroachDB?

CockroachDB is a distributed SQL database known for:

• Horizontal scaling and resilience.
• Automatic replication and fault tolerance.
• Strongly consistent ACID transactions across nodes.

11. Explain the benefits of using SQLite.

SQLite is a lightweight, self-contained DBMS. Advantages include:

• No server setup (runs on disk files).
• Portability across platforms.
• Ideal for embedded systems like mobile apps and IoT devices.

12. What are the features of IBM Db2?

IBM Db2 is an enterprise-grade RDBMS offering:

• Built-in AI for query optimization.
• Strong data security and compliance tools.
• Hybrid cloud support for flexible deployment.

13. What is the role of Redis in DBMS?

Redis is an in-memory key-value database, often used for:

• Caching to speed up application performance.
• Real-time analytics.
• Session management and message brokering.

14. How does Snowflake differ from traditional databases?

Snowflake is a cloud-native data platform known for:

• Separating storage and compute, allowing independent scaling.
• Zero maintenance (no hardware or software to manage).
• Advanced data sharing features across organizations.

15. What are graph databases? Provide examples.

Graph databases store data as nodes and relationships (edges). Examples include:

• Neo4j: Popular for social networks, fraud detection, and recommendation engines.
• Amazon Neptune: Fully managed graph DB for complex relationships.

16. How is Elasticsearch different from traditional databases?

Elasticsearch is a search engine that stores and retrieves data as JSON documents, ideal for:

• Full-text search capabilities.
• Real-time data exploration.
• Use cases like log analysis and e-commerce product searches.

17. What is the importance of ETL tools in DBMS?

ETL (Extract, Transform, Load) tools like Talend and Informatica help:

• Extract data from various sources.
• Transform it into a usable format.
• Load it into a DBMS or data warehouse for analysis.

18. How does Azure SQL Database support scalability?

Azure SQL Database provides:

• Elastic pools for scaling multiple databases dynamically.
• Automatic tuning and high availability.
• Integration with Microsoft’s ecosystem (e.g., Power BI).

19. What are time-series databases?

Time-series databases are optimized for timestamped data, like sensor readings or stock prices. Examples include:

• InfluxDB: Popular for IoT applications.
• TimescaleDB: Built on PostgreSQL with extensions for time-series data.

20. What are the advantages of using Amazon Aurora?

Amazon Aurora is a cloud-based relational DBMS offering:

• Compatibility with MySQL and PostgreSQL.
• High performance (up to 5x faster than MySQL).
• Automatic scaling and replication for resilience.

Scenario-Based Questions

Scenario-based questions test how well you can apply your DBMS knowledge to real-world situations. These questions are practical, so take a hands-on approach while preparing.

1. How would you design a database for an e-commerce platform?

• Tables:
  • Users: Stores customer information.
  • Products: Contains product details.
  • Orders: Tracks orders placed by users.
  • OrderDetails: Tracks product quantities and prices for each order.
  • Payments: Manages payment transactions.

Example Schema:

CREATE TABLE Users (id INT PRIMARY KEY, name VARCHAR(100), email VARCHAR(100));

CREATE TABLE Products (id INT PRIMARY KEY, name VARCHAR(100), price DECIMAL);

CREATE TABLE Orders (id INT PRIMARY KEY, user_id INT, order_date DATE, FOREIGN KEY (user_id) REFERENCES Users(id));

CREATE TABLE OrderDetails (order_id INT, product_id INT, quantity INT, FOREIGN KEY (order_id) REFERENCES Orders(id), FOREIGN KEY (product_id) REFERENCES Products(id));

2. Resolve a situation where a query takes too long to execute.

• Steps:
  • Check for missing indexes on frequently queried columns.
  • Use EXPLAIN or EXPLAIN PLAN to analyze the query execution path.
  • Rewrite the query to reduce complexity (e.g., avoid nested subqueries).
  • Optimize joins by ensuring indexed keys are used.

Example:

— Original query:

SELECT * FROM Orders WHERE user_id = 123 AND order_date > ‘2024-01-01’;

— Optimized with an index:

CREATE INDEX idx_user_order_date ON Orders (user_id, order_date);

3. Troubleshooting data redundancy issues.

• Solution: Normalize the database to eliminate duplicate data.
  • Identify repeating groups or redundant data.
  • Break the data into separate tables with proper relationships.

Example: If customer addresses are repeated in an Orders table, move them to a separate Users table.

4. How would you secure sensitive customer data in a database?

• Solutions:
  • Encrypt sensitive fields like credit card numbers or passwords.
  • Use role-based access control (RBAC) to limit who can access data.
  • Mask data for non-administrative users.

Example:

UPDATE Users SET email = AES_ENCRYPT(email, ‘encryption_key’);

5. A database has grown too large and affects performance. What do you do?

• Partition the database:
  • Horizontal partitioning: Split rows across multiple tables or servers.
  • Vertical partitioning: Split columns into separate tables.
• Archive old data to reduce the table size.

6. How would you handle frequent deadlocks in transactions?

• Solutions:
  • Set a proper locking hierarchy to avoid circular waits.
  • Use a retry mechanism for transactions.
  • Reduce the time locks are held by optimizing queries.

7. Design a database for a library management system.

• Tables:
  • Books: Book details (e.g., title, author, ISBN).
  • Members: Library members.
  • Loans: Tracks which books are borrowed by whom.

Schema Example:

CREATE TABLE Books (id INT PRIMARY KEY, title VARCHAR(100), author VARCHAR(100));

CREATE TABLE Members (id INT PRIMARY KEY, name VARCHAR(100));

CREATE TABLE Loans (book_id INT, member_id INT, loan_date DATE, FOREIGN KEY (book_id) REFERENCES Books(id), FOREIGN KEY (member_id) REFERENCES Members(id));

8. A query returns incorrect results due to inconsistent data. How do you resolve it?

• Use constraints like NOT NULL, UNIQUE, and FOREIGN KEYS to ensure data consistency.
• Apply triggers to enforce rules during data modifications.

9. How would you handle high availability in a database?

• Use replication (e.g., Master-Slave replication) to create redundant copies.
• Implement failover mechanisms with tools like AWS RDS or MySQL Cluster.

10. How would you manage schema changes in a production database?

• Create a migration plan and test changes in a staging environment.
• Use tools like Flyway or Liquibase for versioned schema changes.
• Apply changes incrementally to minimize downtime.

11. Design a database for a school management system.

• Tables:
  • Students: Stores student details.
  • Classes: Tracks class schedules and teachers.
  • Enrollments: Links students to classes.

Schema Example:

CREATE TABLE Students (id INT PRIMARY KEY, name VARCHAR(100));

CREATE TABLE Classes (id INT PRIMARY KEY, subject VARCHAR(100), teacher VARCHAR(100));

CREATE TABLE Enrollments (student_id INT, class_id INT, FOREIGN KEY (student_id) REFERENCES Students(id), FOREIGN KEY (class_id) REFERENCES Classes(id));

12. A report generation query is slow. What steps do you take?

• Pre-aggregate data into a summary table.
• Use indexes and materialized views for faster querying.
• Avoid fetching unnecessary columns.

13. How would you design a system to handle time-series data?

• Use a time-series database like TimescaleDB or InfluxDB.
• Partition data by time intervals (e.g., daily partitions).
• Optimize storage with compression.

14. You need to migrate data from one database to another. How would you do it?

• Export data using tools like mysqldump or database-specific utilities.
• Use ETL tools like Talend or Informatica for complex transformations.
• Validate data integrity post-migration.

15. How do you design a schema for user roles and permissions?

• Tables:
  • Users: Stores user details.
  • Roles: Lists available roles.
  • UserRoles: Maps users to roles.
  • Permissions: Maps roles to specific actions.

16. Resolve a situation where two databases need to sync in real-time.

• Use replication tools like Debezium or CDC (Change Data Capture).
• Implement event-driven architectures with tools like Kafka.

17. How would you optimize database performance under heavy load?

• Optimize indexes and queries.
• Use caching (e.g., Redis) to reduce database load.
• Implement database sharding.

18. You find duplicate entries in a table. How do you fix it?

• Identify duplicates using GROUP BY and HAVING clauses.
• Delete duplicates:

DELETE FROM employees

WHERE id NOT IN (

    SELECT MIN(id) FROM employees GROUP BY name, age

);

19. How would you store hierarchical data, like an organizational chart?

• Use the Adjacency List Model or the Nested Set Model.
• Example:

CREATE TABLE Employees (

    id INT PRIMARY KEY,

    name VARCHAR(100),

    manager_id INT,

    FOREIGN KEY (manager_id) REFERENCES Employees(id)

);

20. How would you ensure data security in a multi-tenant application?

• Use separate schemas or databases for each tenant.
• Apply row-level security policies for data access control.

Tips for Cracking DBMS Interviews

Here’s a quick checklist to ace your DBMS interview in 2025:

• Understand the Company’s Database Stack: Research the tools they use (e.g., MySQL, MongoDB) and highlight relevant experience.
• Master SQL Optimization: Practice writing efficient queries, using indexes, and analyzing query plans.
• Showcase Real-World Use Cases: Share examples of database design, performance tuning, or solving deadlocks.
• Know Cloud DBMS Platforms: Familiarize yourself with AWS RDS, Google BigQuery, or Azure SQL Database.
• Practice Database Design: Be ready to design schemas for common use cases (e.g., e-commerce or IoT).
• Stay Updated on Trends: Mention new tools like Snowflake or CockroachDB and certifications you’ve earned.
• Prepare for Scenarios: Solve practical problems like query timeouts, data redundancy, or securing sensitive data.
• Brush Up on DBMS Fundamentals: Know normalization, ACID properties, and differences between RDBMS and NoSQL.
• Practice Live SQL: Get comfortable with coding SQL queries during interviews on platforms like SQLZoo or LeetCode.
• Ask Smart Questions: Show curiosity by asking about the company’s database challenges or tech stack.

Conclusion

Preparing for a DBMS interview might seem overwhelming, but you can tackle it confidently with the right strategy and consistent practice. Focus on understanding fundamental concepts, mastering SQL queries, and applying your knowledge to real-world scenarios. Stay updated with the latest tools and trends, and don’t forget to showcase your problem-solving skills and enthusiasm for learning. With this guide and your dedication, you’re all set to ace your DBMS interview in 2025.

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

Trendy terms like Big data, deep learning, Machine learning, Data mining and Artificial intelligence are also linked with Data Science. According to Glassdoor, the national average salary for a Data Scientist is ₹10,00,000 in India. This article talks about a few top jobs for Data Scientists in the city of Mumbai.

The list of companies with Data Science job vacancies in Mumbai are

• Unilever
• Spotify
• VISA
• Michael Page
• Deutsche Bank
• Colgate – Palmolive company
• London Stock Exchange Group(LSEG)
• UBS
• Vodafone Idea
• Abbott
• BCG
• JP Morgan Chase & Co

Unilever PLC is a British multinational corporation that manufactures and supplies fast-moving consumer goods including foods, cleaning agents, beverages, home care products, vitamins, minerals and personal care products. The company has a business presence worldwide and owns more than 400 brands including 11 billion-dollar brands.

As per Glassdoor, the typical Unilever Data Scientist salary is ₹13,18,285 per year. Data Scientist salaries at Unilever can range from ₹10,23,026 to ₹21,86,076 per year.

Unilever Job Openings

Steps to apply for a Data science job role at Unilever, Mumbai

Step 1: Visit the Unilever careers page and access the job opening

Step 2: Click on “Apply now” and create an account

Step 3: Click on “Apply manually”

Step 4: Fill in the details and submit

Skills required for Data Science job at Unilever

• Strong ability to solve analytical problems using statistical and quantitative approaches
• Good understanding of data science techniques like LGB, LSTMs and XGBoost
• Experience in designing end to end architectures in Azure
• Ability to apply an agile analytic approach
• Comfortable working with large datasets and ability to manipulate and analyse them

• Good communication skills

Spotify is one of the world’s largest commercial music streaming service providers. It provides restricted digital content from a range of record labels and artists. The firm monetises its users through a paid subscription model and an ad-based model. The platform has over 150 million listeners worldwide.

As per AmbitionBox, the Data Scientist’s salary in Spotify ranges from ₹ 25.2 Lakhs to ₹ 32.2 Lakhs.

Spotify Job Openings

Steps to apply for Data Science Job at Spotify, Mumbai

Step 1: Visit the company careers page and access the job opening

Step 2: Click on “Apply now”. Fill in the details and submit

Skills required for Data Science job at Spotify

• Experience working with mixed data science skills and approaches
• Good analytical skills including analysis of Big data, A/B testing and statistics etc
• Expertise in SQL and advanced languages like Python or R
• Understanding of modelling techniques like forecasting, regression models, clustering etc
• Proactive and creative with good communication skills

Visa is a global payment company. Visa acts as an intermediary in facilitating consumers, businesses and governments. It facilitates electronic fund transfers throughout the world mainly through its credit cards and debit cards.

As per Glassdoor, the typical Visa Inc. Data Scientist salary is ₹3,27,159. Data Scientist salaries at Visa Inc. can range from ₹2,35,883 to ₹26,68,243.

VISA Job Openings

Steps to apply for Data Science Job at VISA, Mumbai

Step 1: Visit the careers page and access the job role

Step 2: Click on “I’m interested” and fill in the details

Skills required for Data Science Job at VISA, Mumbai

• Good experience in data exploration and feature engineering
• Proficiency in statistics and other data science tools
• Good with modelling software
• Experience with Python, Hadoop, impala and Hive
• Understanding how the algorithms work
• Ability to work with multiple projects
• Experience in building and applying ML tools like linear models, deep learning, gradient boosting etc
• Good communication skills

Michael Page is one of the world’s leading professional recruitment consultancies, specialising in the placement of candidates in temporary, permanent, contract and interim positions with clients worldwide. The firm provides specialised recruitment services for professionals at middle management to leadership levels. In India, the firm specialises in roles across functional disciplines like Analytics, legal, marketing, sales, HR, IT etc.

Michael Page Job Openings

Steps to apply for Data Science Job at Michael Page

Step 1: Visit the careers section and access the job

Step 2: Click on “Apply”

Step 3: If you choose to apply with a CV, fill in your details

Step 4: Fill in the details to apply through LinkedIn

Skills required for Data Science Job at Micheal Page

• Ability to program in at least two languages
• Good theoretical and practical knowledge of statistical algorithms including ML Techniques
• Experience working in analytics
• Strong leadership skills to be able to handle a team
• Good business understanding
• Worked in 3-4 domains for developing analytics solutions
• Good communication skills

Deutsche Bank AG is a German-based multinational investment bank and financial services company. The firm specialises in asset management, private wealth management, private banking, retail banking and transaction banking. The bank caters to financial institutions, media and telecom, real estate, technology sectors and healthcare etc. The bank also offers debt, foreign exchange, repo, loans, trade finance, advice on M&A etc.

As per Glassdoor, Data Scientist’s salaries at Deutsche Bank can range from ₹14,96,397 to ₹16,00,448. 

Deutsche Bank Job Openings

Steps to apply for Data Science Job at Deutsche Bank

Step 1: Visit the careers page and access the job

Step 2: Scroll down and click on “Apply for this job”

Step 3: Click on “Apply manually” and fill in the needed details

Skills required for Data Science Job at Deutsche Bank

• Experience in developing and deploying analytical solutions using ML
• Proficiency in one or more languages like Python/Java/SAS/R
• Ability to learn new machine learning and deep learning techniques
• Applying the theoretical knowledge to real-life situations
• Good written and verbal communication skills
• Organised, disciplined and detail-oriented

Colgate Palmolive Company is a global provider of consumer products headquartered in New York. The company operates through two segments, Oral, Personal and home care; and Pet Nutrition. The former segment offers toothpaste, liquid hand soaps, shampoos, detergents etc. The brands that sell these are Colgate, hello, AXION, Cuddly etc. The Pet Nutrition segment offers pet nutrition products for everyday nutritional needs under Hill’s prescription Diet brand.

As per Glassdoor, Data Scientist’s salaries at Colgate-Palmolive can range from ₹7,02,039 to ₹7,63,990.

Colgate-Palmolive Company Job Openings

Steps to apply for Data Science job at Colgate-Palmolive Company

Step 1: Visit the company careers page and access the job role

Step 2: Click on “Apply now” and create an account

Step 3: Fill in your details and submit

Skills required for Data Science job at Colgate-Palmolive company

• Experience in building data models and driving insights
• Experience in developing statistical models such as LASO, random forest and regression etc
• Working knowledge of Python, SQL, R, AI/ML
• Working knowledge of tools like Domo Power BI and Tableau
• Knowledge of Google products BigQuery, data studio, coda etc
• Good project management and communication skills

LSEG plc is a British stock exchange and financial information company covering the financial market value chain from primary and secondary markets across multiple asset classes over the index, data and analytics down to the clearing and post-trading reporting.

As per AmbitionBox, the Average London Stock Exchange Group Data Scientist salary in India is ₹ 15.6 Lakhs for 2 to 11 years of experience. Data Scientist salary at London Stock Exchange Group ranges from ₹ 11.6 Lakhs to ₹ 17 Lakhs.

LSEG Job Openings

Steps to apply for Data Science role at LSEG, Mumbai

Step 1: Visit the company careers site and access the job opening

Step 2: Click on “Apply”

Step 3: Click on “Apply manually” and create an account

Step 4: Fill in the information and submit

Skills required for Data Science job at LSEG

• Proficiency in using analytical software like Matlab and Excel
• Proficiency with programming languages like VBA, Python etc
• Good academic records
• Analytical and structured yet creative approach to problem-solving
• Ability to take initiative and collaborative attitude
• Good written and verbal communication skills
• Team spirit

UBS is a multinational diversified financial services company headquartered in Zurich and Basel. UBS provides financial advice and solutions to institutional, private and corporate clients worldwide. UBS is also involved in virtually all major financial activities, including commercial and retail banking, investment banking and wealth management. The company has offices in over 50 countries and employs more than 69,000 people.

As per Glassdoor, The typical UBS Data Scientist’s salary is ₹11,07,017. Data Scientist salaries at UBS can range from ₹9,47,987 to ₹15,50,000.

UBS Job Openings

Steps to apply for Data Science Job at UBS, Mumbai

Step 1: Visit the UBS careers page and find the job opening

Step 2: Click on “Apply now” and create an account

Step 3: Fill in the necessary details and submit

Skills required for Data Science job at UBS

• Expertise with Power BI, Tableau and other BI tools
• Knowledge of data science concepts and data crunching
• Experience with Python, Pyspark and scala coding language
• Knowledge of databases like SQL Server and Oracle
• Good written and verbal communication skills
• Knowledge of procurement and sourcing data analytics

Vodafone Idea Limited is a telecommunication company born out of the Aditya Birla Group and Vodafone Group partnership. The company provides mobile services and operates under Mobility services. The company owns telecommunications infrastructure, such as fibre networks and mobile towers. The company provides pan India voice and Data services across 2G,3G and 4G platforms.

As per Glassdoor, the typical Vodafone Idea Data Scientist salary is ₹19,13,885 per year. Data Scientist salaries at Vodafone Idea can range from ₹5,40,044 to ₹24,92,038 per year.

Vodafone Idea Job Openings

Steps to apply for Data Science Jobs at Vodafone Idea

Step 1: Visit the careers page and access the job posting

Step 2: Click on “Apply now” and create an account

Step 3: Fill in the necessary details and submit

Skills required for Data Science Jobs at Vodafone Idea

• Experience in Python and cloud computing – AWS
• Knowledge of ML tools like XGBoost, Keras, CNN, LSTM etc
• Knowledge of deep learning tools like Keras, Tensorflow, Caffe, torch etc
• Knowledge of
• Expertise in AI, ML and cloud technologies
• Working knowledge of Big Data technologies like Hive, Spark, Kafka, Hadoop MR, NoSQL etc
• Understanding of statistical modelling of data using concepts like ANOVA, Significant analysis, hypothesis testing and probabilistic estimations
• Excellent communication skills – both written and verbal

Abbott Laboratories is a Chicago based medical products company. It is known for its diagnostics, medical devices, branded generic pharmaceuticals and nutrition products. Some of Abbott’s well-known brands are Sililac, Glucerna and Ensure. Abbott markets its products worldwide through affiliates and distributors.

As per Glassdoor, Data Scientist’s salaries at Abbott can range from ₹9,56,992 to ₹11,69,739.

Abbott Job Openings

Steps to apply for Data Science Jobs at Abbott

Step 1: Visit the careers page and access the job opening

Step 2: Click on “Apply now” and fill in the details

Skills required for Data Science Jobs at Abbott

• Ability to work with large datasets
• Perform integrative analyses of clinical and device data
• Knowledge of Data modelling, predictive algorithms and data analytics
• Ability to research and develop Deep Learning and AI algorithms
• Creating dashboards and visualisations of processed data
• Good communication and presentation skills

BCG is a global management consulting firm that partners with businesses to provide solutions to various problems. It is one of the top consulting firms in the world and deals with everything from operations, HR, Legal, and product management to technology and engineering. 

As per Glassdoor, the typical Boston Consulting Group Data Scientist salary is ₹20,64,713 per year. Data Scientist salaries at Boston Consulting Group can range from ₹8,80,390 to ₹25,80,891 per year.

BCG Job Openings

Steps to apply for Data Science Jobs at BCG

Step 1: Visit the BCG careers page and find the job opening

Step 2: Click on “Apply now” and fill in the necessary details

Skills needed for Data Science jobs at BCG

• Strong technical and analytical skills
• Knowledge of data science tools
• Good communication skills – both written and spoken
• Leadership qualities
• Understanding of statistical concepts

JP Morgan Chase & Co is a leading global financial services firm that provides financial solutions for businesses and governments around the world. It specialises in investment banking, commercial banking, asset management and financial transaction processing.

As per Glassdoor, the typical J.P. Morgan Data Scientist salary is ₹19,72,080 per year. Data Scientist salaries at J.P. Morgan can range from ₹6,66,523 to ₹22,46,815 per year. 

JP Morgan Chase & Co Job Openings

Steps to apply for Data Science Jobs at JP Morgan Chase & Co

Step 1: Visit the careers page and access the job opening

Step 2: Click on “Apply now” and enter your email address

Step 3: Fill in your details and submit

Skills required for Data Science jobs at JP Morgan Chase & Co

• Proficiency in Microsoft Office suite
• Awareness of Big Data technologies and their applications
• Knowledge of Tableau and Power BI
• Ability to work in cross-functional teams
• Strong analytical and problem-solving skills
• Good communication and presentation skills
• Team player
• Ability to prioritise work and follow tight deadlines

Vskills Data Science Certification Program

Since the demand for Data Science professionals has been constantly rising in recent times, we can expect high competition among individuals for Data Science Jobs. To stand out, it is advisable to gain as much expertise as possible. One of the easiest ways to achieve this is by enrolling in a certification course that will upskill you by giving you both theoretical and practical exposure to Data Science. One such certification course is the Vskills Certified Data Science with Python professional certification. It will help you gain the necessary skills and stand out.

Vskills Data Science Practice Tests

To be certified and be able to crack Data Science Jobs in the market, one will have to be well prepared to crack the final exam after the course. This requires practice. To get the best practice you can take help from Vskills Free Data Science Practice Tests which will help you point out your lacunas and give you a sense of your progress before giving your final exam.

Vskills Data Science Interview Cheat Sheet

Along with being certified, it is also necessary to be prepared for the interviews since they will be an important factor/step in getting hired. Do check out Vskills Free Data Science Interview Resources. This will help you to prepare the basic and advanced questions that are asked during interviews. These questions and answers are prepared by experts in the field and are inspired by real-life interviews. Hence, they will be helpful for you to make you better prepared for Data Science Jobs.

Happy Learning !! Cheers!! To get Data Science certified with Vskills click here.

The post Top Data Science Jobs in Mumbai appeared first on Vskills Blog.

This sector consists of vast opportunities, ranging from data integration and preparation to specialized databases, to data mining and artificial intelligence algorithms. This sector is at the growing stage and entering such a field will give you grow your skills and enhance your chances to achieve your goals. Moreover, career growth, job security, and reputation make it an attractive career option for beginners.

Therefore, understanding the importance and demand of this sector we’ve listed the top 10 highest paying big data jobs in India in the year 2022. This list is updated and reviewed by our professionals. Irrespective of your work level, beginner/entry-level, or middle level, this blog will help you decide the right direction you want to go in next. Now, let’s begin with the blog.

1. Big Data Engineer

A big data engineer is one of the highly demanding jobs in the present time. According to statistics, in India, there is a need for 200,000 Data Scientists and Data Engineers in the coming 5 years. From a salary perspective, both positions are equally paid. Moreover, these professionals are responsible for designing, building, testing, and maintaining complicated data processing systems that work with large data sets. However, to become a big data engineer you are expected to have two to three years of experience in the domain with various technical skills like knowledge of programming, communication skills, etc.

Roles and Responsibilities

• Working with large datasets to provide insights for decisions within an organization.
• Retrieve, aggregate, interpret, analyze, and report on business data.
• They also look after building and maintaining the organization’s software and hardware architectures.

Average Salary: INR 8.6 Lakhs per annum

Click here to get Certified

2. Big Data Architect

Big data architects are termed as one of the highest-paying jobs in this sector. These professionals work in close relation with the customers and solutions architects to translate their customer’s requirements into big data. Moreover, you are expected to have a bachelor’s level degree in computer science or related fields. These professionals develop, maintain, test, and construct complex database systems. So, we would definitely recommend you to choose this as your career path.

Roles and Responsibilities

• Creating strategies for different areas of the enterprise data model.
• Communicating the plans, progress, and issues to company executives.
• Designing frameworks.

Average Salary: INR 20 Lakhs per annum

Click here to get Certified

3. Data Warehouse Manager

Data warehouse managers design, implement and manage database management projects. They make sure that the projects remain on duration and within the given budget. Some of their other duties include monitoring databases to ensure they function properly and security protections are up to date. Moreover, the extreme and increasing demand makes database experts one of the highest salary big data jobs in India.

Roles and Responsibilities

• Evaluating data using performance and usage metrics.
• Analyzing data load.
• Store and analyse functions in data facilities.
• Monitoring job usage. 

Average Salary: INR 12 Lakhs per annum

Click here to get Certified

4. Database Developer

A career as a database developer is a highly-technical, highly-progressive career path that is in significant demand in the workforce. With great demand comes significant competition among peers. Therefore, is a very competitive field but a rewarding one. These professionals specialize in designing and implementing computer databases. Moreover, you’re expected to have a good coding background with significant working experience. Overall, this is the best career option you’ll ever choose.

Roles and Responsibilities

• Analyzing database performance.
• Understanding the scope of modernization.
• Developing new databases, if required.
• Eliminate inefficient coding and troubleshoot any issues that may arise. 

Average Salary: INR 4.5 Lakhs per annum

Click here to get Certified

5. Database Administrator

These professionals work to ensure that the data analysts and other users can easily use databases to find the information they need. They basically use the Structured query language (SQL) to analyze data and are in high demand. Moreover, their work is engaging and demanding, therefore it can be stressful. DBAs manage an organization’s data using the database language SQL. Moreover, you are expected to have good technical skills to take up this job.

Roles and Responsibilities

• Data administrators monitor and optimize databases while coordinating with the IT team to ensure data security.
• Monitoring database operation and providing support.
• Migration, performance monitoring, security, and troubleshooting.

Average Salary: INR 5 Lakhs per annum

Click here to get Certified

6. Database Manager

Becoming a database manager is a good career option that will offer you good monetary benefits that will completely depend upon the candidate’s skills and the company’s paying capacity. Now, these professionals work to store the data in an organized manner for the companies. They also supervise the daily activities of the database teams and assess companies’ needs regarding data storage. Moreover, this is a good career option and you should definitely have a look at it.

Roles and Responsibilities

• They work with developers to remedy database issues.
• They guide the design and implementation phases of storage hardware and their maintenance.
• Reviewing business requests for data and usage is also one of their key result areas.

Average Salary: INR 14 Lakhs per annum

Click here to get Certified

7. Data Scientist

This is the most in-demand job profile and is one of the best big data jobs in India. These are highly skilled professionals who use their analytical skills in both technology and social science to find trends and manage data. This field is filled with tremendous opportunities in almost every sector be it information technology, medical, etc. Moreover, you are expected to have a master’s degree for the data scientist position.

Roles and Responsibilities

• Data modelling, data mining, and production.
• Conducting data studies and experiments, developing prototypes, algorithms, and performing custom analysis.
• Collaborate with cross-functional IT teams to create statistical models.
• Interpret complex information for corporations.

Average Salary: INR 8 Lakhs per annum

Click here to get Certified

8. Business Intelligence Analyst

Business intelligence analysts, also known as BI analysts, are professionals who use data and additional information to assist organizations to make good business decisions. According to statistics, the demand for business intelligence analysts is expected to grow by 21% by 2024. Therefore, it is a very good career choice to make. These specialists are in demand in diverse industries and grow to earn above-average salaries. It is a good career path if you’re interested in data analytics and project management.

Roles and Responsibilities

• Taking care of the management’s specific requests.
• Furnish data and insights.
• Assisting the executives in their decision-making duties.

Average Salary: INR 5.7 Lakhs per annum

Click here to get Certified

9. Data Modeler

A data modeler basically designs, implements, and records data architecture and data modeling solutions. These solutions sustain enterprise information management, business intelligence, machine learning, data science, and other business interests. It is a booming career that will face no career opportunity shortage in the coming future. Moreover, most of the data modelers begin as an analyst, then move up the ladder as they acquire experience.

Roles and Responsibilities

• Uncover micro and macro trends for business reports.
• Extracting out information from large datasets.
• Reducing data redundancy and improving its movement across systems.
• Collaborating with architects to design objective-oriented databases.

Average Salary: INR 10 Lakhs per annum

Click here to get Certified

10. Data Analyst

Data analysis is one of the most in-demand careers in the whole world. These professionals basically purify the data and study data sets to help solve problems. They work in almost every sector be it business, finance, criminal justice, science, medicine, and government. Moreover, you’re expected to have a technical graduate degree along with the knowledge of Microsoft Excel, SharePoint, and SQL databases. 

Roles and Responsibilities

• Designing and implementing large-scale surveys.
• Compiling and interpreting the data.
• Relaying the findings in the form of reports.

Average Salary: INR 4.2 Lakhs per annum

Click here to get Certified

We are pleased that you’ve reached the end of this blog and happy to know that you’ve gathered all the required information. We hope you found your best course and start your journey to become a Big data professional and will complete it successfully with flying colors. Moreover, we wish you all the very best for your future. Happy Learning!

The post Top 10 Highest Paying Big Data Jobs in India 2022 appeared first on Vskills Blog.

Discussing the 9 Best Predictive Analytics Certification and online courses to Learn in 2022

Recognizing the importance and the need to choose the right learning path to become a Predictive Analyst. It is surely not an easy track to take but it is neither impossible. The important point to focus on is whether you choose the correct path or not. The internet is filled with several courses but choosing the right one which satisfies your requirements is a difficult task. Therefore, we along with our experts have researched and tried to present you with our best knowledge regarding the 9 Best Predictive Analytics Certification and online courses to Learn in 2022. So without wasting more time below are some of the best and latest certification courses that you desire.

Now. let’s discuss the 9 Best Predictive Analytics Certification and online courses to Learn in 2022!

1. Vskills Certified Predictive Analytics Professional

This certification course is specially meant for professionals and graduates who want to understand the key concepts of predictive analytics and modelling concepts, common tools and algorithms, and the most significant business applications of predictive analytics. This certificate is recognized by many reputed multinational companies as well as the Government of India. Moreover, this course is software-independent and will be using one or several software tools like Excel, Minitab, R, and RapidMiner. This course is one of the best on the internet and has received many positive reviews and learners have shared their experiences that they have felt an extraordinary growth in their career and are very satisfied with this course. So, I would suggest you give this course a definite try.

Course Outline:

• Predictive Analytics & Big Data
• Process & Application
• Key Statistical Concepts
• Correlation & Regression
• Data Collection & Exploration
• Data Mining, Data Distributions, & Hypothesis Testing
• Clustering Techniques
• Data Preprocessing
• Data Reduction & Exploratory Data Analysis (EDA)
• A/B Testing, Bayesian Networks, and Support Vector Machine
• Artificial Neural Networks
• Linear and Logical Regression
• Text Mining & Social Network Analysis
• Time Series Modeling
• Machine Learning, Propensity Score, & Segmentation Modeling
• Random Forests & Uplift Models
• Model Life Cycle Management
• Model Development, Validation, & Evaluation

Reasons to take this course:

• Vskills is a Government Certified body that adds value to your resume itself.
• The Certification is valid throughout your life.
• No prior knowledge required.
• Get access to free Practice Tests.
• Both Professionals as well as begineers can take this course without any second thought.
• Candidates get tagged as ‘Vskills Certified’ On Monsterindia.com and  ‘Vskills Certified’ On Shine Shine.com for better job opportunities.
• This course is available online including online learning material and hard copy at your door steps.
• You can take your exam at any time of your convenience.
• 20 fruitfull Learning hours.

More Details:

Duration of course: 20 hours

Rating: 5 out of 5

Click here to Sign up

2. Predictive Analytics Nanodegree for Business by Alteryx / Tableau (Udacity)

This nano degree program is offered by Udacity, in collaboration with Alteryx. This is a three-month-long course that will help aspirants who aspire to become Data scientists and Business Analysts. This course will indulge you in solving business problems with large datasets using predictive models that will help you brainstorm your practical knowledge. Moreover, you’ll be working with two leading software packages namely Alteryx and Tableau. Later in this course, you will develop skills to apply predictive analytics and business intelligence to solve real-world business problems.

Course Outline:

• Problem Solving with Advanced Analytics
• Data Wrangling
• Classification Models
• A/B Testing
• Time Series Forecasting
• Segmentation and Clustering

Reasons to take this course:

• Get to take real-world projects from industry experts.
• Learn at your own pace.
• Get the Course Completion Certificate Powered by Alteryx.
• No prior experience requirement to enrol for the program.

More Details:

Duration of course: 3 months

Rating: 4.6 out of 5

Click here to Sign up

3. Python Data Products for Predictive Analytics by UC San Diego (Coursera)

This course is prepared by the professionals at UC San Diego, in collaboration with the famous online study platform Coursera. This is an intermediate-level course that is meant for learners who are proficient with the basics of Python. This course claims that learners will learn and build statistical models, devise data-driven workflows, and learn how to make meaningful predictions for a wide range of business and research purposes. By the end of this course, you’ll be mastered one of the technology industries’ most in-demand skills.

Course Outline:

• Basic Data Processing and Visualization
• Design Thinking and Predictive Analytics for Data Products
• Meaningful Predictive Modeling
• Deploying Machine Learning Models

Reasons to take this course:

• Financial aid to students who cannot afford the cost of the course.
• Learn to achieve specialization in the field.
• Learn from the top universities in the world.
• Comes with brand name of UC San Diego.

More Details:

Duration of course: Self-paced

Rating: 4.2 out of 5

Click here to Sign up

4. Predictive Analytics using Python by University of Edinburgh (edX)

This Micromaster program is prepared by the world-famous University of Edinburgh in collaboration with edX. This is a very advanced program that will help aspirants attain higher career goals like becoming a data scientist or a business analyst. This course will teach you data for predictive modelling, data mining, and advanced analytics using a range of statistical and machine learning methodologies on real-life datasets. Moreover, this program is designed for students at the undergraduate level, or equivalent professional experience/background in mathematics, statistics, or programming (Java, C, Python, Visual Basic).

Course Outline:

• Introduction to Predictive Analytics using Python
• Successfully Evaluating Predictive Modelling
• Statistical Predictive Modelling and Applications
• Predictive Analytics using Machine Learning
• Predictive Analytics Final Project

Reasons to take this course:

• End the program with a capstone project.
• Beginner to advance level course.
• Practice by interpreting predictive models
• Learn at your own pace.

More Details:

Duration of course: 8 months

Rating: 4.6 out of 5

Click here to Sign up

5. Predictive Analytics: Gaining Insights from Big Data (FutureLearn)

This is a self-guided course that can be covered by you at your own pace. However, prior knowledge in SQL and Unix will be beneficial to start this course. This course claims that you will learn to use predictive analytics tools and HPE Vertica Analytics to gain insights from big data. Moreover, this course will show you how predictive analytics tools can help you gain information, knowledge and insights from big data. Overall, this is a good course to start with your career as a Predictive analyst.

Course Outline:

• Estimation and prediction using linear regression
• Modelling, estimation and prediction
• Estimation and prediction using logistic regression
• Estimation and prediction using k-means clustering

Reasons to take this course:

• Each topic is covered in an elaborate manner.
• Perform estimation and prediction using linear regression.
• Free online course.
• Learn at your own pace.

More Details:

Duration of course: 4 weeks

Rating: 4.4 out of 5

Click here to Sign up

6. The Essential Elements of Predictive Analytics and Data Mining (LinkedIn Learning)

LinkedIn Learning is probably the best place to find a wide variety of courses all over the world. This is a short course that will help you get an overview of Predictive analysis. This course claims that you will learn about predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. However, this course can be taken by anyone. Also, LinkedIn provides you with the special facility to try its 30-days free trial so you might as well take the advantage of the opportunity as much as you can.

Course Outline:

• Introduction
• What Is Data Mining and Predictive Analytics?
• Problem Definition
• Data Requirements
• Resources You’ll Need
• Problems You’ll Face
• Finding the Solution
• Putting the Solution to Work
• CRISP-DM and the Nine Laws

Reasons to take this course:

• Designed and created by some of the best instructors of LinkedIn Learning.
• Learn at your own pace.
• Variety of exercises to practice the concepts covered in the lectures.
• Free access to courses for the first 30 days.
• Learn writing skills from the very basics to advance level.

More Details:

Duration of course: 1.5 hours

Rating: 4.8 out of 5

Click here to Sign up

7. Introduction to Time Series Analysis and Forecasting in R (Udemy)

Udemy is one of the most famous online education platforms where you can find masses of courses from the best instructors all over the world. This course claims that you will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Moreover, you’ll also learn about different ways in how you can handle date and time data in R. You will learn how to visualize, clean, and prepare your data. Further in this course, you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity, and unit root tests.

Course Outline:

• Introduction
• Working with dates and time in R
• Time series Data Pre-processing and Visualization
• Statistical background for time series analysis and forecasting
• Time series analysis and forecasting
• ARIMA Models
• Multivariate time series analysis
• Neural networks in time series analysis

Reasons to take this course:

• Get lifetime access to the video lectures.
• Learn from the best instructors.
• Get the courses at a very nominal price.
• Learn at your own pace.
• Get assessment test to validate your skills.

More Details:

Duration of course: 8.5 hours

Rating: 4.5 out of 5

Click here to Sign up

8. Introduction to Predictive Analytics in Python (Datacamp)

This course is prepared by Nele Verbiest, a successful data scientist, in collaboration with Datacamp. In this course, you’ll learn how to use and present logistic regression models for making predictions. Moreover, you will learn how to use models to make predictions and how to present them and their performance to business stakeholders. Overall, this is a good course for beginners and for professionals who want to refresh their knowledge and skills.

Course Outline:

• Building Logistic Regression Models
• Forward stepwise variable selection for logistic regression
• Explaining model performance to business
• Interpreting and explaining models

Reasons to take this course:

• Learn to use and present logistic regression models for making predictions.
• Complete Free course.
• Get 52 exercises to practice.

More Details:

Duration of course: 4 hours

Rating: 4.4 out of 5

Click here to Sign up

9. Machine Learning for Predictive Analytics (Experfy)

This is a beginners-level course you will understand what exactly machine learning and predictive analytics are, what are its limits and their potential risks, and why it may benefit your organization. You will also learn about some real-world case studies and many other examples of current and potential future. Moreover, this is a complete package of in-depth understanding of predictive analytics and machine learning and obtaining a robust knowledge of business analysis.

Course Outline:

• An Overview of Predictive Analytics
• Some Pitfalls to Avoid in Creating Analytics Initiatives
• Important Machine Learning Concepts
• Types of Learning Styles
• Supervised Algorithms
• Unsupervised Algorithms
• Other Algorithm Types
• A Visual Algorithm Similarity Map, and so on

Reasons to take this course:

• There are no prerequisites
• This course is for beginners 
• Instructor has 25 years of experience

More Details:

Duration of course: 4.5 hours

Rating: 4.6 out of 5

Click here to Sign up

We have now arrived at the end of this blog. Hopefully, you must’ve decided on your best course and started working on the next steps. We wish you all the very best and good wishes to ace in your career exceptionally and with flying colors. Happy learning!

The post 9 Best Predictive Analytics Certification and online courses to Learn in 2022 appeared first on Vskills Blog.

So, if you are someone who wishes to build a career in Big data Hadoop administration, then stay with us till the end as here you’ll find the 10 Best Big Data Hadoop Certification and Online courses in 2022. Let’s begin with knowing the skills required for the interested candidates.

Recommended skills

To begin with, let’s discuss the basic skills that one is recommended have before going for a certification course in Big data Hadoop-

• Tech savvy
• Data visualization ability
• Data mining skills
• Programming knowledge
• Problem-solving skills
• Analytical skills
• Basics of SQL

Top 10 Market Research Certification and Courses in 2022

Choosing a certification course that suits you best is not an easy task as there are various online platforms offering numerous courses to fulfill your needs. Therefore, here we are to help you out by discussing the 10 Best Big Data Hadoop Certification and Online courses in 2022 that would give you an idea about which course you should go for. So, without any further waste of time let’s look at what we are here for-

1. Certified Big Data and Apache Hadoop Developer by Vskills

This government certification program provides you with basic to the advanced level knowledge of Big Data Hadoop and helps you become a successful Hadoop administrator, developer, data scientist professional, etc in the field of Big Data. It covers broad data science projects and the methods of data transformation, acquisition, and predictive analytics that help in solving real-world problems. Thus, it helps in validating your knowledge on several areas in Big Data and Apache Hadoop. Following are some important details and the course outline that you should know before enrolling in this course.

Course Details

• Government certified
• Learning hours- 22 hrs
• Certification validity- Lifetime
• E-learning access- Lifelong

Course Outline

1. Big Data

2. Apache Hadoop

3. HDFS

4. MapReduce

5. YARN

6. Pig

7. HBase

8. Sqoop and Flume

9. Hive

10. Workflow

11. Hadoop Cluster Management

12. Administration

13. Security

14. NextGen Hadoop

Apply for the course

2. Hadoop Platform and Application Framework by Coursera

This course is beneficial for web developers or business people who wish to understand the core tools used in wrangling and analyzing big data. It provides you with the opportunity to walk through hands-on examples with Hadoop and Spark frameworks without even having experience in the field. So, by the end of the program, you’ll be able to explain the specific components as well as basic processes of the Hadoop architecture, execution environment, and software stack. So, it is a good option for you to choose but before applying keep note of the following important details.

Course Details

• Level- Beginner
• Duration- 26 hours
• Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Spanish
• Self-paced

Course Outline

• Hadoop basics
• Introduction to the Hadoop Stack
• Introduction to Hadoop Distributed File System (HDFS)
• Introduction to Map/Reduce
• Spark

Apply for this course

3. The Ultimate Hands-On Hadoop: Tame your Big Data! by Udemy

This comprehensive course covers over 25 different technologies in around 14 hours of video lectures. It’s not just about theory but is filled with hands-on activities and exercises that help you get some real experience in using Hadoop. Thus, you’ll find a range of activities in this course for people at the beginner level. So, this training program is definitely a good option for candidates willing to start their careers as Hadoop professionals.

Course Details

• Duration- 14.5 hrs
• Full lifetime access
• Fees- Rs 360
• 7 articles and 2 downloadable resources

Course Outline

• Learn all the buzzwords! And install all Hortonworks Data Platform Sandbox
• Using Hadoop’s Core: HDFS and MapReduce
• Programming Hadoop with Pig
• Programming Hadoop with Spark
• Using relational database stores with Hadoop
• Using non-relational database stores with Hadoop
• Querying your data interactively
• Managing your Cluster
• Feeding data to your Cluster
• Analyzing streams of data
• Designing real-world systems
• Learning more

Apply for the course

4. Big Data Hadoop Certification Training Course by edureka

This comprehensive course on Big Data curated by 10+ years of experienced industry experts covers in-depth knowledge on Big Data as well as Hadoop ecosystem tools like HDFS, MapReduce, YARN, Hive, and Pig. Thus, this online instructor-led live Big Data Hadoop certification training allows you to work on real-life industry use cases in retail, aviation, social media, tourism, and finance domains with the use of Edureka’s Cloud Lab. Moreover, some other important details of this course are as follows-

Course Details

• Price- Rs 16,995
• Live classes
• 60 days of Cloud Lab access

Course Outline

• Understanding Big Data and Hadoop
• Hadoop Architecture and HDFS
• Hadoop MapReduce Framework
• Advanced Hadoop MapReduce
• Apache Pig
• Apache Hive
• Advanced Apache Hive and HBase
• Advanced Apache HBase
• Processing Distributed Data with Apache Spark
• Oozie and Hadoop Project
• Certification Project

Apply for the course

5. Big Data Engineer course by Simplilearn

This program provides you with job critical skills such as Big Data and Hadoop frameworks and leverages the functionality of AWS services. It helps you in learning the use of database management tools and MongoDB through interactive live sessions, and industry projects. Moreover, some other important details of this program are as follows-

Course Details

• Standard Fee- ₹44990
• Rating- 4.5
• Provider- IBM
• Course type- Online

Course Modules

• Big Data for Data Engineering
• Big Data Hadoop and Spark Developer
• PySpark Training Course
• Apache Kafka
• MongoDB Developer and Administrator
• AWS Big Data Certification Training
• Big Data Capstone

Apply for the course

6. Introduction to Hadoop by GreatLearning

This introductory level training on Big Data Hadoop gives an overview of the MapReduce programming model with the use of a simple word counting mechanism with existing tools which highlights the challenges around processing data at a big scale. Thus, this will help you learn the use of Hadoop in different real-life situations to gain a deeper appreciation of its simplicity. Following are some other important details for you to know before applying for the program.

Course Details

• Duration- 4.5hrs
• Rating- 4.48
• 1 quiz included
• Level- Beginner

Course Outline

• Introduction to Big Data / Hadoop
• Hadoop distributed file system (HDFS)Intro to ETL
• Distributed computing
• Map-Reduce abstraction
• Programming MapReduce jobs
• Introduction to Oozie and HDFS processing
• Hadoop cluster and eco system
• Input/Output formats and conversion between different formats
• MapReduce features
• Troubleshooting MapReduce jobs
• YARN (Hadoop2.0)

Apply for the course

7. Certification Course on Big Data Engineering with Hadoop and Spark by Cloud x Lab

This specialization helps you learn Hadoop and Spark to drive better business decisions and solve problems in real-life. Also, it will help you apply your skills to a distributed cluster to solve real-world problems. Thus, it is a good course that gives you basic to advanced knowledge about Big Data engineering with Hadoop and Spark. Following are some important details and the course outline that you should know before enrolling in this course.

Course Details

• Duration- 60+hrs
• 90 days of labs
• Projects-7

Course Outline

1. Introduction

2. Foundation & Environment

3. Zookeeper

4. HDFS

5. YARN

6. MapReduce Basics

7. MapReduce Advanced

8. Analyzing Data with Pig

9. Processing Data with Hive

10. NoSQL and HBase

11. Importing Data and with Sqoop and Flume, Oozie

Apply for the course

8. A Program To Master Big Data Technologies by Trendytech

This program is open for learners from different backgrounds with and without any prior experience in Bigdata. Moreover, it is entirely designed and developed on the basis of industry experts’ expertise and experience. Some other important details of this course are as follows-

Course Outline

• Hadoop core components and its ecosystem
• Spark with Scala (Performance optimisation)
• Structured Streaming with Kafka
• Big Data on AWS cloud
• Airflow & CICD pipeline

Apply for the course

9. Certified Big Data and Hadoop Training Course by Data Flair

This course offers in-depth theoretical knowledge on Hadoop as well as strong practical skills through the implementation of real-time Hadoop projects. Thus, it gives you a headstart and enables you to bag good Hadoop jobs in the industry of Big Data. So, this training program is definitely a good option for candidates with or without experience as it’ll help in upskilling your current level of knowledge.

Course Outline

1. The big picture of Big Data

2. Demystifying Hadoop

3. Setup and Installation of Hadoop

4. HDFS – The Storage Layer

5. A Deep Dive into MapReduce

6. MapReduce – Advanced Concepts

7. Hive – Data Analysis Tool

8. Pig – Data Analysis Tool

9. NoSQL Database – HBase

10. Data Collection using Sqoop

11. Data Collection using Flume

12. Apache YARN & advanced concepts in the latest version

13. Processing data with Apache Spark

14. Real-Life Project on Big Data

Apply for the course

10. Big Data Hadoop Certification Training Course by Intellipaat

This training program helps its participants master Big Data Hadoop and Spark to master Hadoop administration. In this course, you get to master MapReduce, Pig, Hive, Sqoop, Flume, and Oozie and work with Amazon EC2 for cluster setup, Scala and Spark SQL, Spark framework, and RDD, Machine learning using Spark and Spark streaming, etc. So, it is a good option for you to choose but before applying keep note of the following important details.

Course Details

• Minimum cut-off- 55%
• Level- Beginner
• Certification fee- USD 530

Course Outline

• Hadoop Installation and Setup
• Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
• Deep Dive in MapReduce
• Introduction to Hive
• Advanced Hive and Impala
• Introduction to Pig
• Flume, Sqoop and HBase
• Writing Spark Applications Using Scala
• Use Case Bobsrockets Package
• Introduction to Spark
• Spark Basics
• Working with RDDs in Spark
• Aggregating Data with Pair RDDs
• Writing and Deploying Spark Applications
• Project Solution Discussion and Cloudera Certification Tips and Tricks
• Parallel Processing
• Spark RDD Persistence
• Spark MLlib
• Integrating Apache Flume and Apache Kafka
• Spark Streaming
• Improving Spark Performance
• Spark SQL and Data Frames
• Scheduling/Partitioning
• Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2
• Hadoop Administration – Cluster Configuration
• Hadoop Administration – Maintenance, Monitoring and Troubleshooting
• ETL Connectivity with Hadoop Ecosystem (Self-Paced)
• Hadoop Application Testing
• Roles and Responsibilities of Hadoop Testing Professional
• Framework Called MRUnit for Testing of MapReduce Programs
• Unit Testing
• Test Execution
• Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Apply for this course

We hope this blog will turn out to be insightful and will help you choose the best course for you. So, stop waiting and start learning; enroll in any of the above courses and kick-start your career in Big Data Hadoop.

HAPPY LEARNING!!

The post 10 Best Big Data Hadoop Certification and Online courses in 2022 appeared first on Vskills Blog.

Data Science is continuously increasing at an astronomical rate. Using data has become more and more important in creating new business opportunities and growth. Companies are still discovering the potential of utilizing data, the importance of monetizing that data in some form to benefit the business. Here’s are the top trends in data science and analytics.

1. Talent Trends in the Data Analytics Industry

The growth of the data science industry has led to new employment trends. These reports a growth in number of IT professionals are moving into the data analytics industry. As the number of jobs available for data analytics continues to grow more and more. People with strong IT skills will be taking advantage of opportunities to develop their data analysis skills.

2. Data Science in Healthcare Industries

Data science has already been invaluable in improving the outcomes of epidemics and predicting patient behaviour. Healthcare companies are using data science to examine breast cancer and help patients avoid biopsies. Data science in the healthcare to grow further as healthcare professionals look forward to improve day-to-day needs and save lives. With the rise of electronic healthcare records, the amount of data at our disposal is at an all-time high. The massive amounts of data have its benefits and drawbacks in which data scientists are looking it.

3. Embedded Analytics

Embedded analytics is the fastest growing area of Business Intelligence (BI). It consists of any consumer-facing BI and analytics tools that are integrated into software applications, operating as a component of the native application itself, rather than a separate platform. Embedded analytics allows users to utilize higher quality and improved data that pinpoint insights quicker and time is not wasted requesting reports from external agents.

4. In-Memory Analytics

This technology querying data loaded into RAM, which can significantly speed up analytical performance by decreasing or even eliminating disk I/O, this results in high performance. With big data, it is the availability of terabyte systems and massively parallel processing that makes in-memory more interesting. At this stage the role of data analytics is important. Running iterations to see correlations between data points doesn’t happen without milliseconds of latency, multiplied by millions/billions of iterations.

5. Mobile Analytics

With increased penetration of mobile, companies are taking a Mobile First approach to engaging consumers, leading to progress in Mobile Analytics. With the location and motion sensing capabilities, significant progress was made in data collection in a privacy to determine what, when, where, and why of the activities that consumers engage in. The app developers are using app analytics to understand the users’ profile and transaction behavior to fine tune their product features and offering to increase user experience and engagement. This data is enabling will improve the consumer experience.

6. Self-Service Analytics

Technological advancement has led to tools for Self-Service Business Intelligence or Self Analytics. This approach meets the needs of data producers and consumers alike, adding speed and agility to the process while protecting organizational data and the overall system. There has been a paradigm shift in deriving business value from analytics owing to the exponential growth in the volume, variety, and complexity of data. Today’s competitive business environment asks for the democratization of analytics with self-service capabilities to meet the time-to-insight demands.

7. Analytics of Governance Platforms and focus on ROI

With the increasing use of analytical models in business decisions, consolidating them in a single technology console, monitoring the health of analytics implementations and model performance is becoming a crucial need for business leadership and regulators. This will help track the analytics performance, demonstrated ROI and avoid the risk of incorrect decisions made because of analytics. ‘Analytics governance platforms’ gained a lot prominence across all large enterprises and become mainstream to monitor analytics deployment through workflows. The ROI of the analytics governance platforms will be seen in the long-term and would reap the benefits of governing complex analytics environments in a single platform with more visibility to the analytics ROI.

8. Real-time Automated Decision Systems

The Enterprises are increasingly enabling real-time automated decision systems whether to streamline operations or mitigate risk. The older rule-based systems are now being replaced by a new generation of systems powered by online machine learning and artificial intelligence – these are self-learning systems that can recalibrate in an automated manner and can be deployed on a large-scale.

9. Cloud Services

Hybrid and public cloud services continue to rise in popularity. The key to big data success is in running the Hadoop platform. The data storage and analytics, resulting in new smarter storage systems that will be optimized for storing, managing and sorting massive petabytes of data sets. Companies wanted a platform that allows them to scale, something that cannot be delivered through a heavy investment in a data center. There is a big focus on APIs to unlock data and abilities in a reusable way, with many companies.

Cloud Certification courses you can look for:-

Certified Cloud Computing Professional

Certified Cloudstack Professional

10. Apache Spark

The popular Apache Spark project provides Spark Streaming to handle processing in the real time through an in-memory, micro-batching approach. Spark will provide dramatically increase data processing speed as compared to Hadoop and provides an efficient, general-purpose framework for parallel execution.

11. Deep Learning and AI

The algorithmic business has the potential to change society and saw a significant increase in the development of algorithms. Artificial general intelligence is becoming possible because of deep learning. Deep learning is a subfield of machine learning and is inspired by the neural networks in our brain. The objective is to create artificial neural networks that can find patterns in vast amounts of data. Deep learning is now available widely, As a result of increased computing power & large data sets are available to scientists around the globe. So, we will see many new deep learning applications that could significantly impact our lives.

12. IoT-Related Data Breaches

The Internet of Things continuously to growing. The 2016 IoT DDoS attack, which disrupted the internet on the USA East Coast, was made possible because of the lack of security with connected devices. Thousands of connected devices are infected via botnet such as smart cameras, routers. Botnet used these to launch a DDoS attack that brought down the internet for millions of people. Unfortunately, we will probably see many more of such IoT-related attacks because of the lack of data security or vulnerability in IoT connected devices.

13. Mixed Reality Improve Data Visualisations and Decision-Making

There are already many examples of some form of Mixed Reality being used in the manufacturing industry to enable better repairs, faster product development or improved inventory management. In addition, Mixed Reality will help decision makers with the understanding of complex data sets, which will enable them to make better decisions. We will see an increase in products and applications developed for Mixed Reality, which organizations can use to improve their decision-making or deliver a better product or service.

14. Prediction analytics needs to the cloud

Many companies are moving their data and applications to the cloud. This process is driven by increased collaboration and flexibility, as well as reducing the complexity of administration and configuration of computing resources. And most the top cloud providers developed their own offering of Machine Learning services in a cloud. This allows organizations to leverage machine learning technology, without making huge investments. They dosen’t required to employ large data science teams. So, now we are observing consumerization of predictive analytics technologies, which is supported by those cloud prediction services.

15. Chatbots

It appears that new possibilities in the conversational interfaces (CI) and chatbots will require a lot of new skills from developers. It will be a huge challenge for UX designers, as it is very different from current user interfaces. But, many programmers and designers are aiming to improve their skills in design and development of chatbots. Chatbot technology has already been integrated into many consumer apps.

16. Automated Machine Learning Dominate the Data Science

Automated Machine Learning dominates the data science industry. The reason for this is that Data Science and ML are linked. After all, machines can only learn based on the data they must work with, and these two fields cannot really exist without each other.

17. Traditional Business Intelligence Replaced by the Internet of Things

Traditional methods of gathering business intelligence will go the way of the Dodo as the Internet of Things (IoT) makes it easier than ever to gather this sort of information. Essentially, as the rise of sensor-driven devices engulfs all facets of society, about 50 % of business intelligence platforms will capitalize on event data streams to find meaningful trends.

18. Threat intelligence

It varies from hashes of known malicious files used by adversaries, IP addresses of the command and control servers of botnets, or even user agent strings used by persistent threats. Threat intelligence feeds have long been used by the security community as point-in-time checks for security monitoring, but the argue that the data science community should leverage them with the behavioral detection systems.

Scope of Data Science in India

Information in total is developing so quick that everything about how we consider information now will change fundamentally in the following ten years. One Exabyte or one million terabytes is the amount of data that is generated in the world every year.  This includes data from social sites, blogs, online transactions, news, pictures, mails, commercial websites, etc. Then comes scientific data, scientific journals, financial data, and so on. So, there is need of data scientist that performs researches and analyses data that help flourish this large amount of data. They take this huge data and use their skills to clean and organize the data.

But what did data scientist really do?

A data scientist must be able to know what he/she is analyzing in big data and what could be the output for the same. He/she also needs to have a clearly defined plan on how the output can be achieved with the available resources and time. To achieve all the above, a data scientist may be required to perform research on the messy data available and frame questions that needs to be answered by his analysis on the data collected collect huge data from multiple sources and make use of high-end analytics programmes such as machine learning and statistical methods to organise data into a predictive model to clean the huge volume of data and discard irrelevant information.

Data science affects many domains of academic and applied research that also include machine translation, speech recognition, robotics, search engines, digital economy etc. It also heavily influences economics, business and finance. From the business perspective, data science is an integral part of competitive intelligence. Their analysis combined with industrial knowledge helps to uncover hidden solutions to business challenges and predicting growth, trends and business insights as well.

As we know that the web is full of “data-driven apps.” Almost any e-commerce application is a data-driven application such as of flipkart, snapdeal and amazon. There’s a database behind a web front end, and middleware that talks to a number of other databases and data services such as credit card processing companies, banks, and so on. Data science also plays role in consultancy services as well, according to the article, Data Science.

A new era for the consulting industry which is published by TCS (Tata Consultancy Services) said that “Leveraging data science will bring about two crucial changes in the consulting industry. Commoditization or disaggregation of certain parts of the existing value chain, and technology-driven business transformation.” Data science is quickly becoming a staple within organizations that harvest data online such as Google, Facebook, and LinkedIn all have their own data science teams to use their available data. Data science enables the creation of data products in India, currently has the highest concentration of analytics globally.

The scarcity of data analytics talent is particularly acute and demand for talent is expected to be higher as more global organizations are outsourcing their work. McKinsey & Company predicts that by 2018 a shortage of people who can fit the data scientist role will occur.  Even if we don’t aspire to become a data scientist, data science skills can be a great addition to once achievements. That is also the reason why Harvard Business Review has called Data Scientist as the sexiest job of the 21st century.

“Garbage in, garbage out” of the huge volume of data, Data science will be as good as the data generated on the following years and thus scope is very bright in India.

If you want to move ahead in your career progression, take some online certification course, there are lots of vendors are available you can choose accordingly.

Written By 
Akhil Joshi - Vskills Intern
(SRM University)

The post Top trends in Data Science and Analytics appeared first on Vskills Blog.

Big Data has emerged as the next frontier in information technology to address the need for managing huge data sets and get meaningful insights. Skills in big data has emerged as the new age skill, having huge demand across businesses. Due to increased use of mobile and online for consumption, making meaning of the data is getting more critical for companies.

Mr. Jasneet Singh from Vskills, interviewed Hardeep Singh, SDE at Right Relevance to update us on the right Big data skills. The interview also lists how to develp skills for beginners, to have opportunities in Data science, machine learning, Hadoop, Mapreduce and big data analytics.

Please tell our readers something about yourself

B-Tech CSE from IIT Ropar, I am working as a full stack development engineer at Right Relevance. In past I’ve worked mostly on Frontend Technologies like Ember Angular etc. My career around Big Data has started in recent past.

What inspired you to choose a career in Big Data ?

With the increasing rate of adaption of Technology the Digital Data is increasing exponentially. There are many problems we couldn’t solve earlier just because we didn’t have enough data to create solutions that work, but today we can and the results are phenomenal e.g major machine learning problems are solved using Big Data, self driving cars, amazon recommendations, facebook friend suggestions etc.

What were your professional qualifications and how did it help to get a job in Big Data ?

I’ve worked at multiple companies solving problems with frontend architectures and implementing some proprietary Microsoft protocols, which lead me to Right Relevance. Right Relevance is an analytics company deeply involved in Big Data.

What courses do you recommend to students and professionals who want to pursue Big Data as a profession ?

Big Data Specialisation course from Coursera will teach you everything about Big Data, initial understanding of Machines, Clusters, Networks would be a plus, generally a B-Tech/ MCA degree would work as a pre-requisite.

Is studying at a top institution essential to make a career in Big Data ?

No, it’s not related, I think if you can get good ranks solving some known online problems on Big Data, that would be a major plus, ultimately showing your solutions to problems would lead to a better impression than your college degree.

What are the books / websites / online courses that you would recommend to freshers in your profession ?

For Books I would recommend

1) Hadoop The Definite Guide

2) Introduction to Apache Hive, Apache Spark, Apache Kafka.

And Big Data Specialisation Course from Coursera includes everything needed to kick start a Big Data Project.

According to you, which area of specialization in Big Data has the most promising future ?

Big data is transforming lives everywhere, every big or small company today is using Big Data, to improve user experience, to recommend better products. Big Data is everywhere, but personally I feel Machine Learning is going to be most promising future for Big Data analytics.

In your experience, what are the common qualities and skills that you have identified in successful professionals in Big Data ?

From my experience skills of a successful professions in Big Data professional includes Java language, AWS or Azure knowledge, understanding of operating systems, understanding of hardware and clusters. The most important quality is the great sense of debugging these systems because these systems don’t get built in one iteration, they take multiple iterations to get right and perfect.

Other qualities include

1) Performance Measurement

2) Agility

3) End to End System Testing.

What challenges have you faced in the course of your career and how did you overcome them ?

The number of technologies are huge, you have to find out what you want to use. Then, you will have to figure out their configurations because the hardware requirements vary according to use of Technology. These hardware requirements are generally too high to manage finances. Keeping these things in mind, building a technology stack is I guess includes major challenges in my course career. I had to read a lot, sometimes multiple times to understand each configuration and how at the core one technology is different from other, this has given me a lot of clarity and now all that hard work helps me a lot to build resilient systems.

What is the most satisfying and enjoyable aspect of your job ?

Seeing your analytics solutions solving everyday problems of a lot of people and hence saving a lot of time of a lot of people, is I think the most satisfying aspect. The unexpected challenges that occur while building the big data solution stack is the most enjoyable aspect.

What is the dividing line between an average professional in Big Data and an excellent one ?

The Excellent One will build a “Resilient System” really fast growing from a prototype, but an average one would try to build a “Solution” slowly keeping every possibility in mind and solving problems that might not occur, which is generally not needed and is a major cost to company.

What is your advice to aspiring professionals who want to build career in Big Data ?

The Big Data Ecosystem is huge and it is evolving at a very fast pace, you don’t need to learn everything but you should know what is happening everyday in this ecosystem, what technologies are used to solve which type of problems. These are basic things generally asked in Job Interviews.

-Team Vskills

Test your Big data skills at – http://www.vskills.in/practice/skills/Big-Data

The post Interview with Big Data analytics expert – Hardeep Singh appeared first on Vskills Blog.

The team in control of NASA’s Mars Rover spacecraft now have Big Data driven analytical engines at their fingertips.

The same open source ElasticSearch technology used by the likes of Netflix and Goldman Sachs is now being put to use planning the actions of the Curiosity rover, which landed on the red planet in 2012. And the next mission, due to launch in 2020 with the primary mission of finding evidence of ancient life on the planet, is being built around the technology from the ground up.

NASA’s Jet Propulsion Labs, which runs the day-to-day mission planning, has rebuilt its analytics systems around ElasticSearch which now processes all of the data transmitted from the Rover during its four daily scheduled uploads.

These uploads cover tens of thousands of data points – every reading taken by Curiosity’s onboard sensors including temperature on the Martian surface, atmospheric composition, and precise data on the rover’s equipment, tools and actions.

All of Curiosity’s operations are planned a day in advance based on data received the previous day. This move to a real time analytics vastly speeds up the time in which decisions can be taken by mission control.

ElasticSearch – which recently achieved its 50 millionth download – means that patterns and anomalies in the datasets can be spotted far more quickly. Correlations which could provide mission-critical insights are more likely to become apparent, leading to a greater rate of scientific discovery and less danger or malfunction or failure.

NASA JPL data scientist Dan Isla told me “This has been very transformational from an operational point of view. We can get very quick insights over multiple days’ worth of data, or even the entire mission, in just a few seconds, without delays. We can actually ask questions and get answers quicker than we can think up new questions.”

One application is anomaly resolutions. When it appears that there is a problem with the spacecraft, precise details of its operations can immediately be analyzed to find out when the last time this situation occurred, and what other elements were in play at the time.

The system is also used for the vital function of managing power generated by the rover’s radioisotope thermoelectric and solar power generators. The trickle charge system only makes around 100 watts of power available to the craft and its instruments at any one time consistently.

This means that power management must be carefully planned in advance. As Isla explains, “using ElasticSearch we can budget this power much more effectively, we can look at the data and see ‘ok, this is what we used yesterday, and this is the state of the batteries, and this is our plan coming up…’ we can build a power model very quickly. “With ElasticSearch everyone can do analytics, ask questions at the same time and get the same results, we’re all working together now. Everyone’s very agile now.”

This isn’t NASA’s only project to use ElasticSearch. It was incorporated into the Mars Rover program after being successfully tested in the Soil Moisture Active Packing mission, launched last year. SMAP uses high powered radar and radiometer to analyze and capture high resolution data on soil moisture across the globe. A full moisture map of the Earth’s surface is generated every three days – data creation an order of magnitude greater than that of the Mars Rover. While the rover has so far generated around 1 billion data points, the SMAP project is already pushing 20 billion.

Perhaps the most exciting aspect of Mars exploration today is the possibility of discovering whether or not the planet was ever home to life. It is thought that ElasticSearch will greatly speed up the answering of this question.

Isla tells me “Curiosity was designed to find out whether Mars was ever a habitable environment. During the course of the mission we’ve been able to confirm that yes, it was. The next mission – in 2020 – we’re taking life detection instruments and we need more capabilities, more instruments and we need to be able to do it faster.

“We can’t be spending hours analyzing the data, so we need to take the ElasticSearch approach that we implemented fairly early into Curiosity and do that from day one. We’re definitely baselining this tech as our operational data system.”

For Big Data certification – http://www.vskills.in/certification/Big-data

The post NASA’s Mars Rover appeared first on Vskills Blog.

Like many businesses, the National Football League is experimenting with big data to help players, fans, and teams alike.

The NFL recently announced a deal with tech firm Zebra to install RFID data sensors in players’ shoulder pads and in all of the NFL’s arenas. The chips collect detailed location data on each player, and from that data, things like player acceleration and speed can be analyzed.

The NFL plans to make the data available to fans and teams, though not during game play. The thought is that statistics-mad fans will jump at the chance to consume more data about their favorite players and teams.

In the future, the data collection might be expanded. In last year’s Pro Bowl, sensors were installed in the footballs to show exactly how far they were thrown.

Of course, this isn’t the NFL’s first foray into big data. In fact, like other statistics-dependent sports leagues, the NFL was crunching big data before the term even existed.

However, in the last few years, the business has embraced the technology side, hiring its first chief information officer, and developing its own platform available to all 32 teams. Individual teams can create their own applications to mine the data to improve scouting, education, and preparation for meeting an opposing team.

It’s also hoped that the data will help coaches make better decisions. They can review real statistics about an opposing team’s plays or how often one of their own plays worked rather than relying solely on instinct. They will also, in the future, be able to use the data on an individual player to determine if he is improving.

Diehard fans can, for a fee, access this same database to build their perfect fantasy football team. Because, at heart, the NFL believes that the best fans are engaged fans. They want to encourage the kind of obsessive statistics-keeping that many sport fans are known for. It’s hard to predict how this flood of new data will impact the game. In 2015, only 14 stadiums and a few teams were outfitted with the sensors. And in 2016, the NFL decided against installing sensors in all footballs after the politics of 2015’s “deflate gate” when the Patriots were accused of under inflating footballs for an advantage. Still, it seems fairly easy to predict that the new data will quickly make its way into TV broadcast booths and instant replays. Broadcasters love to have additional data points to examine between plays and between games.

2016 also marked the 50th year of the Super Bowl, and plenty has changed since the first time teams duked it out for the national title. Sports analysts have been collecting data on football games since the very beginning, but our data collection techniques and abilities have vastly improved. New sensors in stadiums and on NFL players’ pads and helmets help collect real-time position data, show where and how far players have moved, and can even help indicate when a player may have suffered a damaging hit to the head.

In 2013, Microsoft struck a $400 million deal with the NFL to make this data available to coaches and players via their Surface tablets. Coaches use the tablets to demonstrate and review plays on the sidelines, as well as access real-time data from the NFL’s databanks.

Of course, the partnership hasn’t been all smooth. At first, commenters kept referring to the tablets as “iPads,” and had to be reminded many times to call them by their brand name. Then, during the AFC championship game this year, the Patriots briefly had connectivity issues, causing the tablets to stop working.

And, of course, the commenters remembered to call them by their brand name that time just as, almost as unfortunately, the network ran a Microsoft ad during the break.

Still, connectivity issues aside, the advent of real-time data access is potentially changing the way coaches view games and call plays. As data collection has improved, people are also turning more and more to computer algorithms to predict the outcome of the game. Big data, IoT sensors, and new analytics abilities makes even more data available, including real-time distance traveled and position on the field, how weather conditions affect individual plays, and even predicting individual player matchups.

But it’s still a long way from a sure bet.

A company called Varick Media Management has created its own Prediction Machine to predict the outcome of all the games in the season. The site also offers its “Trend Machine” which can analyze many different matchups from more than 30,000 games over 35 years of play.

However, their accuracy is far from perfect; in the 2014-2014 regular season, they boasted a 69% accuracy rating. But, they were the only major predictor to foresee the Seattle Seahawks blowout win over the Denver Broncos in 2014. Facebook tried to use social data to predict the outcome, and predicted the Broncos would take home the trophy. Another way big data could be changing the game is in the realm of advertising.

Everyone knows Super Bowl ads cost millions of dollars — a record $5 million for a 30-second spot this year — but what big data showed marketers last year is that most of the online chatter about Super Bowl XLIX took place after the game.

This could be a boon for advertisers who want to take advantage of the attention on the game, but can’t afford a TV spot. In fact, by using data to strategically target only certain consumers, online advertising could represent a much better investment with better engagement — and at a fraction of the cost.

That could mean that, for well targeted ads, online advertising could have a better return on investment than television, even ads aired during the most-watched event on live TV.

In addition, social listening in years past told us that social media users talked more about the brands, commercials, and halftime show than they did the game. That points to an important social opportunity for brands who can afford to advertise during the game.

As we all know, brands can make or break a big social opportunity like this depending on how well their social media is managed, and how well they’re watching their data. Take the Oreo blackout tweet during the 2013 Super Bowl — many said the Twitter ad “won” the Super Bowl marketing game that year, and they paid mere pennies compared to brands that bought TV ads.

For Big Data certification – http://www.vskills.in/certification/Big-data

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