10 Best Books for Data Science Interview

Prepare for the interview if you’re serious about getting a job in data science. Reading books intended specifically for data science interviews is one efficient method to get ready. To help you ace the interview and distinguish yourself from the competition, we will examine a variety of popular data science interview books in this guide.

Best Books for Data Science Interview

Preparing for a data science interview can be challenging given the broad technical concepts and business cases to master. The right resources are key to standing out among candidates. This list provides of books covers key topics including statistics, machine learning, data engineering, Python, and business analytics. For data scientists, these are essential reads to help you brush up on skills, tackle common interview questions, and land your dream job in this highly competitive field. Read these books to build a solid preparation plan and impress hiring managers with your technical expertise and communication abilities.

1- Ace the Data Science Interview: 201 Real Interview Questions

A comprehensive resource created especially for data science interviews is “Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street”. This book, which was co-written by former Facebook employees. It provides in-depth answers to 201 interview questions on a variety of subjects, including probability, statistics, machine learning, SQL, Python, and more.

Although machine learning review in “Ace the Data Science Interview” may be viewed as superficial by candidates interviewing for machine learning-intensive professions, it does offer concrete advice to assist candidates secure interviews and offers helpful strategies for creating resumes and portfolios.

2- Be the Outlier: How to Ace Data Science Interviews

Be the Outlier: How to Ace Data Science Interviews by Shrilata Murthy takes an innovative approach by emphasizing the importance of establishing oneself as an outlier to stand out during data science interviews. The book offers a concept review, advice on preparing a resume, and insights into various interview formats, such as take-homes, presentations, and case studies.

While “Be the Outlier” provides a good selection of sample questions and helpful explanations. It primarily targets early-stage data scientists or those newly graduated.

3- Designing Data-Intensive Applications

Data scientists need to have a solid understanding of how data is ingested, stored, and processed, especially in smaller companies and startups. For hybrid roles that involve both data science and data engineering, it’s essential to be prepared for system design interview questions. One book that can be incredibly helpful in this regard is “Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems” by Martin Kleppmann.

“Designing Data-Intensive Applications” covers important topics such as data modeling, data storage, distributed systems, and data processing pipelines. By reading this book, data scientists can develop the valuable skill of balancing performance, scalability, and reliability in designing data-intensive applications. Understanding how large-scale software systems work is crucial for data scientists to excel in areas like DataOps and MLOps.

4- Cracking the Coding Interview

Coding skills are an essential aspect of data science interviews, particularly at top tech companies like Google and Amazon. “Cracking the Coding Interview: 189 Programming Questions and Solutions” by Gayle Laakmann McDowell is a popular resource for coding interview preparation. Although it is not specifically made for data science interviews, it covers Python data structures and algorithms extensively, which are commonly tested in data science and machine learning interviews.

“Cracking the Coding Interview” provides a solid foundation for coding interview questions. Data scientists who want to go through deeper into computer science topics like linked lists, graphs, and trees should consider supplementing their preparation with additional resources.

5- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

“Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications” by Chip Huyen is a comprehensive guide for data scientists interviewing for ML-heavy positions or working closely with machine learning engineers. This book covers essential topics like machine learning metrics, model evaluation and deployment, collaboration with machine learning engineers and DevOps, and architecting ML platforms for multiple use cases.

By reading “Designing Machine Learning Systems,” data scientists can gain a deeper understanding of the open-ended ML interview questions they may encounter and develop the necessary skills to excel in the field of machine learning engineering.

6- Data Science and Machine Learning Interview Questions Using Python

This book is a comprehensive guide intended for those preparing for data science machine learning interviews. This book is expertly compiled to cover not only core Python, Numpy, Pandas, Scipy, and Sklearn but also advanced statistics and even Excel related aspects.

The author, used his two decades of industry experience, has thoughtfully curated the content in a way that makes it easy to comprehend even complex concepts. The simplicity of language and clarity of explanations turns this book into an easily navigable knowledge repository. The organization of the book into seven chapters, starting from the basics of data science and progressing towards more complex topics, allows for a smooth and gradual build-up of understanding.

What sets this book apart is its ability to provide practical, interview-focused questions and answers, making it a perfect last-minute revision tool. The examples given are relevant and realistic, aiding in better retention of the concepts. Moreover, the author’s decision to include a section dedicated to statistics, acknowledging its crucial role in data science, is commendable.

7- Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

“Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning” is not a dedicated interview book but offers valuable information for data scientists. Written by Alex Gutman and Jordan Goldmeier, award-winning data scientists, this book focuses on critical data thinking skills and helps readers avoid common data interpretation mistakes.

While “Becoming a Data Head” may not provide specific interview preparation materials, it can help data scientists develop a mindset of asking the right questions and gain a deeper understanding of real-world data science applications.

8- The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

“The Data Science Handbook” provides a collection of 25 interviews with established data scientists. It provides insights into their career trajectories and perspectives in the field. However, it does not cover interview techniques or specific topics, it can be beneficial for understanding different paths to success in data science.

Reading this book can be helpful for brainstorming answers to culture-fit and career-oriented questions. However, it’s important to note that the book was published in 2015, so some of the information may be outdated in terms of current industry standards.

9- Data Science Interview: Prep for SQL, Panda, Python, R Language, Machine Learning, DBMS and RDBMS – And More – The Full Data Scientist Interview Handbook

The information, statistics, and insights provided by the author are both informative and enlightening. With data science job postings witnessing a 31% surge in the recent past, and salaries averaging at a handsome $98,230, the importance of preparing comprehensively for interviews cannot be stressed enough.

The brilliance of this book lies in its practicality. It’s not just a theoretical guide. Instead, it’s a compendium of actual questions sourced from real data scientists employed at industry giants such as Google, Facebook, Amazon, and even NASA. This ensures that readers are exposed to the kind of questions they might face in real-life scenarios.

The variety of topics covered, including SQL, Panda, Python, R Language, and Machine Learning, ensures that aspirants are well-versed across the board, leaving no room for unexpected surprises during interviews. Furthermore, the guide’s clear structure and organization make it both a great preparatory tool for upcoming interviews and a quick brush-up resource for seasoned professionals.

10- Effective Data Science Infrastructure: How to make data scientists productive

The author’s real-world experience as a designer of Netflix’s full-stack data science framework, Metaflow, adds a layer of practicality and credibility to the text. The book skillfully simplifies the complex topic of data science infrastructure, demonstrating how to design systems that enhance productivity and streamline the path from prototype to production.

Tuulos provides a comprehensive overview of how to deploy machine learning to production, manage performance, and monitor results. He also presents efficient ways to amalgamate cloud-based tools into a unified data science environment.

The book also takes a commendable social stand, with the author committing to donate proceeds from the book to charities that support women and underrepresented groups in data science.

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