Interview Preparation¶
This section covers everything you need to clear data science interviews — from screening rounds to final loops.
How to Use This Section¶
- Pick a subject folder below.
- For each question, think through your answer first before opening the collapsible block.
- If your answer misses key points, note the gap and revisit it in 24 hours.
- Once you can answer every question in a file fluently, move to the next.
Saying an answer out loud is not optional — it is the skill being tested.
Coverage¶
| Folder | Topics | Status |
|---|---|---|
| 01-Python | Python basics, data structures, OOP, functional programming, libraries | ✅ Ready |
| 02-SQL | SQL basics, joins, window functions, subqueries, query optimization | ✅ Ready |
| 03-Statistics | Descriptive stats, probability, distributions, hypothesis testing, A/B testing, Bayesian thinking | ✅ Ready |
| 04-Machine-Learning | Fundamentals, regression, classification, clustering, ensembles, feature engineering, evaluation, regularization | ✅ Ready |
| 05-Deep-Learning | Neural networks, training, CNNs, RNNs, transformers | ✅ Ready |
| 06-NLP | Text preprocessing, classical NLP, embeddings, language models, applications | ✅ Ready |
| 07-System-Design | ML system design, data pipelines, model serving, monitoring | ✅ Ready |
| 08-Case-Studies | Product metrics, experiment design, ML problem framing, business cases | ✅ Ready |
| 09-Behavioral | Telling your story, project deep dives, conflict, STAR method | ✅ Ready |
Difficulty Guide¶
Questions within each file move from foundational to nuanced. The first few are expected in any screening. The later ones separate candidates in final rounds.