COMMON MISTAKES TO AVOID
| ❌ Mistake | ✅ What to do Instead |
|---|---|
| Jumping straight to ML models | Start with clarifying questions and EDA |
| Using complex models without justification | "I'd start with a simple baseline, then upgrade with reason" |
| Focusing only on accuracy | Discuss business impact and stakeholder communication |
| Ignoring data quality issues | Always mention data cleaning as a critical step |
| Not mentioning limitations | End with caveats and next steps |
| Speaking only in jargon | Use business language that a CMO would understand |
| Forgetting to quantify impact | "~20% churn reduction = ₹X revenue saved" |
| Presenting problems without solutions | Always end with an actionable recommendation |
HOW TO PRESENT A CASE STUDY
Template Answer Structure:
"Let me walk through how I'd approach this:
First, I'd clarify the business objective and define the problem precisely — [state definition].
Then, I'd gather and understand the data — [mention key datasets and features].
For analysis, I'd start with EDA to understand patterns — [mention specific analyses]. Then build a model — [mention method with justification].
The key insight is [state finding with a number].
My recommendation is [actionable step with expected impact].
One caveat to note is [limitation or assumption]."
🧠 Practice tip: Pick 2-3 case studies from this document, set a 10-minute timer, and present your approach out loud. Record yourself and listen back — you'll catch rambling, missing structure, and uncertain moments. Do this 3 times and you'll nail this round.
Recap — Progressive Difficulty:
Level Case Studies Skills Tested Level 1 (SQL) Employee Analysis, E-Commerce Sales, Retention Analysis SQL queries, JOINs, Window Functions, CTEs, CASE WHEN Level 2 (SQL+Python) RFM Segmentation, Dashboard Design End-to-end pipeline, Pandas, visualization, stakeholder thinking Level 3 (Full ML) Churn Prediction, Demand Forecasting, Marketing Mix Feature engineering, model building, business recommendations