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If you’re preparing for data science interviews, it can be overwhelming to sift through the numerous available resources online. This is why I am excited to introduce you to a hidden gem of a resource: “The Data Science Interview Book” by Dip Ranjan Chatterjee.
This freely available web-based book covers essential data science interview topics, including statistics, model building, algorithms, neural networks, and business intelligence. What sets it apart is its emphasis on providing only the relevant information to prepare for the interview. This makes it the perfect resource for busy data scientists who need to quickly brush up on a wide range of concepts. There are a few unique features that make this book stand out:
- Real-world interview questions: This book includes real-world interview questions from companies like Google, DoorDash, and Airbnb, along with detailed solutions and case studies.
- Updated content: The book is continually updated with new sections, questions, and richer content.
- Cheatsheets and references: The book includes cheatsheets for quick reference guides for various topics, as well as additional references for those who want to study topics more deeply.
Don’t panic if you encounter a section followed by a ?? symbol. This indicates that these sections are still being worked on and are subject to change. Here are the major sections covered in this book:
1. Statistics
This section covers the fundamentals of statistics, essential for data analysis and model building. Topics include probability basics, probability distributions, central limit theorem, Bayesian vs. frequentist reasoning, hypothesis testing, and A/B testing.
2. Model Building
This section will guide you through creating a successful model, from data gathering to model selection, and covers essential data preprocessing techniques such as feature scaling, handling outliers, dealing with missing values, and encoding categorical variables. It also includes a subsection on hyperparameter optimization and famous open-source tools for it.
3. Algorithms
This section covers various machine-learning algorithms, provides practical advice on choosing the right algorithm, and discusses advanced concepts of regression, classification, clustering, decision trees, random forests, ensemble learning, boosting, time series analysis, anomaly detection, and Big O analysis.
4. Python
This section covers fundamental Python concepts, common programming techniques, coding algorithms from scratch, and sample questions related to statistics, data manipulation, and NLP.
5. SQL
It covers the basics of SQL and includes topics such as joins, temp tables vs table variables vs CTE, window functions, time functions, stored procedures, indexing, and performance tuning.
6. Analytical Thinking
This section focuses on business scenarios and behavioral management-related questions, providing scenario-based questions to prepare candidates. It aims to prepare candidates for strategic thinking and effective communication.
7. Cheatsheets
Instead of spending hours searching for cheatsheets online, quick and comprehensive guides are available for topics such as Numpy, Pandas, SQL, statistics, RegEx, Git, PowerBI, Python basics, Keras, and R basics all in one place.
I completely understand the importance of having a reliable and comprehensive resource to prepare for interviews, and I believe that this book fits the bill. I am sure it will help you succeed. I wish you all the best for your data science preparation journey! If you have any questions, feel free to reach out to me.
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine.