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If you’re preparing for a data science interview, you know how overwhelming it can be to research all the available resources online. It’s easy to get lost in the details. That’s why we’re excited to introduce you to a hidden gem resource: The Data Science Interview Book by Dip Ranjan Chatterjee.
This freely available web-based book covers all the important topics you need to know for a data science interview, from statistics and model building to algorithms, neural networks, and business intelligence. But what sets us apart from other resources is our focus on providing only the information you need to prepare for your interview. This makes it a great 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:
- Actual interview questions: This book includes real interview questions from companies like Google, DoorDash, and Airbnb, along with detailed solutions and case studies.
- Updated content: This book is continually updated with new sections, questions, and richer content.
- Cheatsheet and reference materials: This book includes cheat sheets for quick reference guides on various topics, as well as additional reference materials for those who want to learn the topics in more depth.
Don’t panic if you come across a section followed by ??. symbol. This just indicates that these sections are still a work in progress and are subject to change. The main sections covered in this book are:
1. Statistics
This section covers the basics of statistics essential for data analysis and model building. Topics include the fundamentals of probability, probability distributions, central limit theorems, Bayesian and frequentist reasoning, hypothesis testing, and A/B testing.
2. Building the model
This section of the book describes the process of creating a successful model, from data collection to model selection. We also teach essential data preprocessing techniques for data scientists, such as scaling features, handling outliers, handling missing values, and encoding categorical variables. There is also a subsection on hyperparameter optimization and some popular open source tools used for it.
3. Algorithm
Algorithms are the foundation of data science, and understanding them is important to ace data science interviews. This section describes various machine learning algorithms and also provides practical advice on how to choose the right algorithm for your use case. This section begins with the basics of bias and variance tradeoffs and generative and discriminative models. Next, move on to advanced concepts such as regression, classification, clustering, decision trees, random forests, ensemble learning, and boosting. Additionally, this section also covers time series analysis and anomaly detection. It concludes with a comprehensive table on Big O analysis covering the time and space complexities of various machine learning algorithms.
4. Python
Python is a versatile language used for a variety of tasks in data science. This section has the following subsections:
- Theoretical: We’ll cover some basic Python concepts, including mesh grids, statistical techniques, range and xrange, switch cases, and lambda functions.
- Basics: How many common programming techniques do you need to be familiar with, such as lists, tuples, and dictionaries, to solve Python questions during interviews or understand control flow using loops and conditionals? There are.
- Code the algorithm from scratch: Companies often ask candidates to code an algorithm from scratch during a coding demo round. This section describes general steps for coding an algorithm from scratch.
- question: We’ll cover some sample questions related to statistics, data manipulation, and NLP.
5.SQL
SQL queries are often used in data science interviews to assess a candidate’s ability to manipulate data and solve complex problems. This section covers SQL basics, including joins, temporary tables, table variables, CTEs, window functions, time functions, stored procedures, indexing, and performance tuning. The Temporary Tables, Table Variables, and CTEs section explains the differences between these three temporary data structures and when to use each. You will also learn how to create and use stored procedures. The Performance Tuning section describes various tips for optimizing your SQL queries. Overall, it provides a solid foundation in SQL.
6. Analytical thinking
The book has several ongoing sections on Excel, Neural Networks, NLP, Machine Learning Frameworks, Business Intelligence, etc., but I would like to focus on this section in particular. I think it’s unique in that it covers questions related to business scenarios and behavior management, which are becoming increasingly important in data science interviews. Companies are looking for people who not only have technical expertise, but also the ability to think strategically and communicate effectively.
For example, here are the questions Salesforce asked in one of our interviews:
“As a data scientist at Salesforce, you’re talking to a product manager who wants to understand Salesforce’s user base. What approach would you take?”
By answering these scenario-based questions, you will be well prepared for your interview.
7. Cheat Sheet
Instead of spending hours searching for cheat sheets online, get one comprehensive guide on topics like Numpy, Pandas, SQL, Statistics, RegEx, Git, PowerBI, Python Basics, Keras, R Basics, and more. You can easily find it. These guides are perfect for a quick refresher before an interview or to refer to during a coding assignment.
I completely understand the importance of having reliable and comprehensive resources for interview preparation. And I believe this book fits the bill. I’m sure it will help you succeed. I wish you all the best on your data science preparation journey. If you have any questions, please feel free to contact us.
kanwar mereen I am an aspiring software developer with a strong interest in data science and the application of AI in healthcare. Kanwal has been selected as his Google Generation Scholar 2022 for the APAC region. Kanwal loves writing articles and sharing her technical knowledge on trending topics, and she is passionate about improving the representation of women in the technology industry.
