Crack Data Science Interviews
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Interview Questions
These are currently most commonly asked interview questions.
Questions can be removed if they are no longer popular in interview circles and added as new question banks are released.
- đ Flashcards
- DSA (Data Structures & Algorithms)
- System Design
- Natural Language Processing (NLP)
- Probability
- A/B Testing
- SQL
- ML-Algorithms
- Python
- Pandas
- NumPy
- Scikit-Learn
- LangChain
- LangGraph
- Interview Question Resources - Community-curated sources for all topics
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Cheat Sheets
Distilled down important concepts for your quick reference
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ML Algorithms
From scratch implementation and documentation of all ML algorithms
- ARIMA
- Activation functions
- Collaborative Filtering
- Confusion Matrix
- DBSCAN
- Decision Trees
- Gradient Boosting
- K-means clustering
- Linear Regression
- Logistic Regression
- Loss Function MAE, RMSE
- Neural Networks
- Normal Distribution
- Normalization Regularisation
- Overfitting, Underfitting
- PCA
- Random Forest
- Support Vector Machines
- Unbalanced, Skewed data
- kNN
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Online Resources
Most popular and commonly referred online resources
This is a completely open-source platform for maintaining curated list of interview questions and answers for people looking and preparing for data science opportunities.
Not only this, the platform will also serve as one point destination for all your needs like tutorials, online materials, etc.
This platform is maintained by you! đ¤ You can help us by answering/ improving existing questions as well as by sharing any new questions that you faced during your interviews. You can also improve topics and articles.
Current Platform Status
Every core section is live and actively maintained:
- Interview Questions â DSA, Machine Learning, System Design, NLP, Probability, A/B Testing, SQL, Python, Pandas, NumPy, Scikit-Learn, LangChain, LangGraph, Transformers
- Cheat Sheets â Django, Flask, Hypothesis Tests, Keras, LangChain & LangGraph, NumPy, Pandas, PySpark, PyTorch, Python, RegEx, Scikit-Learn, SQL, TensorFlow
- ML Topics â 20 from-scratch guides incl. Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, PCA, Neural Networks, and more
- More â Flashcards, Online Study Material, Popular Blogs, Interview Question Resources
This is a living, community-driven project. On the roadmap:
- More interview questions across every topic
- Additional cheat sheets and worked examples
- Deeper coverage of ML topics
The pace is set by the community â new content is added as contributors submit it.
Contributions are always welcome! đ¤
- âī¸ Add a question you were asked in a real interview
- đ Improve an answer or expand a topic
- đ Report an issue or a broken link
You can submit content in .py, .md, .txt, or .ipynb â we'll take care of the formatting. See the Contribute page to get started.