Free Data Science Interview Book to Land Your Dream Job

If you're preparing for data science interviews, you know how overwhelming it can be to sift through all the resources available online. One can easily get lost in the details. That's why I'm excited to introduce you to a hidden gem of a resource: “The Data Science Interview Book” by Dip Ranjan Chatterjee.

This book available for free on the web covers all the essential topics you need to know for data science interviews, from statistics and model building to algorithms, neural networks, and business intelligence. But what sets it apart from other resources is that it focuses on providing only the relevant information to prepare you 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. Here are some things that I think make this book unique:

1. 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.
2. Updated content: The book is continually updated with new sections, questions and richer content.
3. Cheat Sheets and References: The book includes cheat sheets for quick reference guides for various topics, as well as additional references for those who want to study topics in more depth.

Don't panic if you find a section followed by a ?? symbol. This simply indicates that those sections are still being worked on and are subject to change. These are the main sections covered in this book:

1. Statistics

This section covers the fundamentals of statistics, which are essential for data analysis and model building. Topics include basic probability concepts, probability distributions, central limit theorem, Bayesian versus frequentist reasoning, hypothesis testing, and A/B testing.

2. Model construction

This section of the book will guide you through the process of creating a successful model, from data collection to model selection. He also teaches you the data preprocessing techniques essential for any data scientist, including feature scaling, handling outliers, handling missing values, and coding categorical variables. He also has a subsection on hyperparameter optimization and some famous open source tools used for it.

3. Algorithms

Algorithms are fundamental to data science and understanding them is crucial to succeeding in a data science interview. This section covers various machine learning algorithms and also gives you practical advice on how to choose the right algorithm for your use case. This section begins with the basics of bias-variance trade-off and generative versus discriminative models. Then you move on to advanced concepts of regression, classification, clustering, decision trees, random forests, ensemble learning, and boosting. Additionally, the section also discusses time series analysis and anomaly detection. Finally, it concludes with a comprehensive table on Big O analysis, covering the temporal and spatial complexities of different machine learning algorithms.

4. Python

Python is a versatile language used in data science for various tasks. This section has the following subsections:

1. Theoretical: It covers some fundamental concepts in Python, such as mesh grid, statistical methods, range vs x range, change case, and lambda functions.
2. The essential: There are some common programming techniques that you should be familiar with to solve Python questions during an interview, such as lists, tuples, and dictionaries, and understand the flow of control using loops and conditionals.
3. Encoding algorithms from scratch: Companies often ask candidates to code algorithms from scratch during a coding demo round. The general steps for coding an algorithm from scratch are discussed here.
4. Questions: It covers some sample questions related to statistics, data manipulation and NLP.

5.SQL

In data science interviews, SQL queries are often used to assess a candidate's ability to work with data and solve complex problems. This section covers the basics of SQL, including joins, temporary tables, table and CTE variables, window functions, timing functions, stored procedures, indexing, and performance tuning. The Temporal Table, Table Variable, and CTE section explains the differences between these three temporal data structures and when to use each. You will also learn how to create and use stored procedures. The Performance Tuning section covers several tips for optimizing your SQL queries. Overall, it will provide you with a solid foundation in SQL.

6. Analytical thinking

While the book includes several continuous sections such as Excel, Neural Networks, NLP, Machine Learning Frameworks, Business Intelligence, etc., I would like to highlight this one specifically. I think it's unique because it covers business scenarios and behavioral management-related questions, which are becoming increasingly important in data science interviews. Companies are not only looking for technical experience, but also candidates who can think strategically and communicate effectively.

For example, here is a question Salesforce asked in one of their interviews:

“As a data scientist at Salesforce, you're talking to a product manager who wants to understand Salesforce's user base. What would your approach be?”

By reviewing these scenario-based questions, you will be well prepared for your interviews.

7. Cheat Sheets

Instead of spending hours searching for cheat sheets online, you can find quick and comprehensive guides on topics like Numpy, Pandas, SQL, statistics, RegEx, Git, PowerBI, Python basics, Keras, and R basics, all in one only place. These guides are perfect for a quick refresher before an interview or for reference during a coding challenge.

I completely understand the importance of having a reliable and comprehensive resource for preparing for interviews, and I think this book fits the bill. I'm sure it will help you succeed. I wish you all the best in your data science preparation journey! If you have any questions, please do not hesitate to contact me.

Kanwal Mehreen is an aspiring software developer with a strong interest in data science and ai applications in medicine. Kanwal was selected as a Google Generation Scholar 2022 for the APAC region. Kanwal loves sharing technical knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.