Intermediate 4 days · 50 tasks

Course Drop-off and Mentor Load

Paid learners are quitting a 12-week online course halfway, and completion has fallen sharply since July. Use weekly activity and module data to find where and why learners drop off, from mentor load and unanswered doubts to hard modules and a discount sale.

Kaushal Edge Academy · SQL · Python (pandas, seaborn) · solved in your browser

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The problem

The Chief Academic Officer needs to know how much completion has fallen in the April to August 2026 cohorts, when in the course learners drop off, and how much is explained by mentor load and unanswered doubts, the discount sale, hard modules and early engagement, using enrollment, activity and module data up to 31 October 2026, when the last cohort had finished.

How it works

Start the project — the 4-day timer begins and the workspace opens with every table loaded.
Solve 50 tasks in SQL or Python (30 basic, 15 intermediate, 5 hard). Each is checked instantly.
Write the final report in the workspace and add charts from your notebook.
Score = 70% tasks + 30% report review. 60 or more earns a certificate.

What you will deliver

Answers to the 50 analysis tasks (30 basic, 15 intermediate, 5 hard), solved with SQL or Python in the JobPrax workspace.
A final report written in the workspace: executive summary, data quality, findings and recommendations.
Two to four charts from your Python notebook, added to the report.