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.
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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 reffered 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! 🤗 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
- Cheat-Sheets/Django
- Cheat-Sheets/Flask
- Cheat-Sheets/Hypothesis-Tests
- Cheat-Sheets/Keras
- Cheat-Sheets/NumPy
- Cheat-Sheets/Pandas
- Cheat-Sheets/PySpark
- Cheat-Sheets/PyTorch
- Cheat-Sheets/Python
- Cheat-Sheets/RegEx
- Cheat-Sheets/Sk-learn
- Cheat-Sheets/SQL
- Cheat-Sheets/tensorflow
- Interview-Questions/DSA
- Interview-Questions/System-Design
- Interview-Questions/Natural-Language-Processing
- Interview-Questions/Probability
- Interview-Questions/ML-Algorithms
Learn about How to contribute?
You can pick anyone, write in .py, .md, .txt or .ipynb; I will format it!
- Machine-Learning/ARIMA
- Machine-Learning/Activation-Functions
- Machine-Learning/Collaborative-Filtering
- Machine-Learning/Confusion-Matrix
- Machine-Learning/DBSCAN
- Machine-Learning/Decision-Trees
- Machine-Learning/Gradient-Boosting
- Machine-Learning/K-means-Clustering
- Machine-Learning/Linear-Regression
- Machine-Learning/Logistic-Regression
- Machine-Learning/Loss-Function-MAE-RMSE
- Machine-Learning/Neural-Networks
- Machine-Learning/Normal-Distribution
- Machine-Learning/Normalization-Regularisation
- Machine-Learning/Overfitting-Underfitting
- Machine-Learning/PCA
- Machine-Learning/Random-Forest
- Machine-Learning/Support-Vector-Machines
- Machine-Learning/Unbalanced-Skewed-Data
- Machine-Learning/kNN
- Online-Material/Online-Material-for-Learning
- Online-Material/Popular-Blogs
Useful Commands
mkdocs serve- Start the live-reloading docs server.mkdocs build- Build the documentation site.mkdocs -h- Print help message and exit.mkdocs gh-deploy- Usemkdocs gh-deploy --helpto get a full list of options available for thegh-deploycommand. Be aware that you will not be able to review the built site before it is pushed to GitHub. Therefore, you may want to verify any changes you make to the docs beforehand by using thebuildorservecommands and reviewing the built files locally.mkdocs new [dir-name]- Create a new project.
Useful Documents
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📑 MkDocs:
- GitHub: https://github.com/mkdocs/mkdocs
- Documentation: https://www.mkdocs.org/
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🎨 Theme: