This exercise helps you understand which data skills tend to be linked with higher salaries and how you can prioritise your learning based on pay signals. The goal is to compare skills across job listings and identify which skills appear more often in higher-paying roles.
Step 1 is selecting a target region and job level. Choose a geography you care about (for example India) and a level such as fresher, 0–2 years, or 2–5 years. Keep the level consistent, otherwise pay comparisons will not be fair.
Step 2 is collecting job data. Find at least 25 to 30 job listings that mention salary ranges. Use roles like Data Analyst, Business Analyst (Analytics), Reporting Analyst, and Junior Data Scientist if relevant. Record job title, company, salary range, location, and required skills. Save this in a simple table in Excel or Google Sheets.
Step 3 is creating a skill list. Create a list of common skills such as Python, SQL, Excel, Power BI, Tableau, statistics, A/B testing, data modelling, ETL, cloud (AWS/GCP/Azure), machine learning basics, and communication. For each job listing, mark which skills are mentioned.
Step 4 is grouping jobs by salary. Create 3 buckets such as low, mid, and high based on the salary ranges you collected. Then calculate how often each skill appears in each bucket. This shows which skills are more common in higher-paying roles.
Step 5 is analysing patterns. Identify skills that increase strongly from low to high salary buckets. Also note skill combinations that often appear together in higher-paying jobs, such as SQL + dashboards + stakeholder management, or Python + statistics + automation.
Step 6 is converting results into a learning plan. Choose the top 5 skills linked with higher pay and write one practical project task for each. End the exercise by writing a short conclusion: which skills are worth prioritising first, and what projects you will build to prove them.
