This exercise helps you understand how strong the job demand is for data analysis skills and what employers actually expect. The goal is to convert job listings into a clear skill checklist that you can practise and use in your resume.
Step 1 is choosing 2 to 3 target job roles. For example, Data Analyst, Business Analyst (Data), Research Analyst, or Reporting Analyst. Decide the level you are targeting, such as fresher or 0 to 2 years.
Step 2 is collecting job listings. Find at least 15 to 20 job descriptions from platforms like LinkedIn, Naukri, Indeed, and company career pages. Save the links or copy the text into one document so you can review them together.
Step 3 is extracting skills and tools. For each job, note the required tools and skills. Create a list of repeated keywords such as Python, Pandas, Excel, SQL, dashboards, Power BI/Tableau, data cleaning, EDA, statistics, communication, and domain knowledge. Also note common responsibilities such as reporting, KPI tracking, stakeholder communication, and automation.
Step 4 is grouping the findings into categories. For example:
- core tools: Python, SQL, Excel
- analysis skills: cleaning, EDA, visualisation, reporting
- business skills: problem framing, communication, documentation
- preferred add-ons: Power BI/Tableau, cloud, basic ML
Step 5 is identifying the top 10 most common requirements. Mark which ones appear in more than half of the job listings. These become your priority skills.
Step 6 is creating an action plan. For each priority skill, write one small practice task. For example, for Pandas groupby, create a summary table; for SQL, write 10 queries; for visualisation, create 4 charts and explain them. At the end, write a short conclusion on which skills are most demanded and what you will practise next.
