In a data science team using Git with feature branches, what is the recommended approach for integrating experimental analysis without disrupting the main workflow?
Commit directly to main branch with descriptive messages
Create feature branch, conduct experiments, squash commits, and merge via pull request after peer review
Use git stash for all experimental work
Create separate repository for each experiment
A retail analyst wants to find the total sales per store from a Pandas DataFrame df with columns store_id and sales_amount. Which is the most efficient Pandas command?
df['sales_amount'].sum()
df.groupby('store_id')['sales_amount'].sum()
df['sales_amount'].groupby('store_id')
df.pivot_table(index='sales_amount', values='store_id')
A team is troubleshooting why a page loads slowly due to an API call taking a long time. Which Chrome DevTools tab should they use to find the slow request?
Sources
Network
Console
Application
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The IIT Madras BS Tools in Data Science (Tools in Data Science (TDS)) End Term paper sat on 31 Aug 2025, in the May 2025 term, set QIA3: 34 questions for 40 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Tools in Data Science (TDS) End Term 31 Aug 2025 Set QIA3 at a glance |
|---|---|
| Term | May 2025 term |
| Subject | Tools in Data Science |
| Course code | BSSE2002 |
| Questions | 34 |
| Marks | 40 |
| Duration | 180 min |
| MCQ | 33 |
| MSQ | 1 |
| Official paper | IIT M IMPROVEMENT AN EXAM QIA3 31 Aug 2025 |
| Negative marking | No negative marking. |
| Updated |
