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Cracking the Data Science Interview: 101+ Data Science Questions & Solutions Paperback – Dec 17 2019
by
Maverick Lin
(Author)
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Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview.
Topics include:
• Necessary Prerequisites (statistics, probability, linear algebra, and computer science)
• 18 Big Ideas in Data Science (such as Occam’s Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality)
• Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization)
• Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more)
• Reinforcement Learning (Q-Learning and Deep Q-Learning)
• Non-Machine Learning Tools (graph theory, ARIMA, linear programming)
• Case Studies (a look at what data science means at companies like Amazon and Uber)
Maverick holds a bachelor’s degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics.
Topics include:
• Necessary Prerequisites (statistics, probability, linear algebra, and computer science)
• 18 Big Ideas in Data Science (such as Occam’s Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality)
• Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization)
• Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more)
• Reinforcement Learning (Q-Learning and Deep Q-Learning)
• Non-Machine Learning Tools (graph theory, ARIMA, linear programming)
• Case Studies (a look at what data science means at companies like Amazon and Uber)
Maverick holds a bachelor’s degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics.
- ISBN-10171068013X
- ISBN-13978-1710680133
- Publication dateDec 17 2019
- LanguageEnglish
- Dimensions13.97 x 0.76 x 21.59 cm
- Print length119 pages
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Product details
- Publisher : Independently published (Dec 17 2019)
- Language : English
- Paperback : 119 pages
- ISBN-10 : 171068013X
- ISBN-13 : 978-1710680133
- Item weight : 159 g
- Dimensions : 13.97 x 0.76 x 21.59 cm
- Best Sellers Rank: #336,757 in Books (See Top 100 in Books)
- #163 in Data Mining
- #204 in Computer Science Modelling & Simulation
- #225 in Database Storage & Design Textbooks
- Customer Reviews:
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Maverick holds a bachelor’s degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics.
Customer reviews
3.9 out of 5 stars
3.9 out of 5
48 global ratings
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Top reviews from other countries

Sabrina
4.0 out of 5 stars
Little gem of Interview book
Reviewed in the United Kingdom 🇬🇧 on September 25, 2021Verified Purchase
I bought the book after reading the cheatsheet on Github, 111 pages included necessary concepts and answers for questions. It will give you idea how to tackle Data Science, it is a big subject for next 20 years.
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kimi
1.0 out of 5 stars
Can not read on Kindle. Amazon has no way to return an ebook. Do not buy the Kindle version
Reviewed in Japan 🇯🇵 on February 2, 2021Verified Purchase
I bought this book as Kindle version, but my Kindle said this book is not a compatible content.
I tried to return the order but Amazon has no way to return an ebook.
I just lost 8USD for nothing.
I tried to return the order but Amazon has no way to return an ebook.
I just lost 8USD for nothing.

Sri
1.0 out of 5 stars
It is a Kindle Book - I cannot download it to my Kindle Device or read on my PC via cloud
Reviewed in the United States 🇺🇸 on June 19, 2020Verified Purchase
No reply from the author. I can not read the book in my Kindle Paper White or via the Cloud
The listing said it is a Kindle Book. I bought. I am not able to download/read in my Kindle Book Reader. Please help.
Amazon should do quality control. I can't read this book in the Kindle. Can't download. I would like to download and print. The author does not reply. Very poor user experience.
The listing said it is a Kindle Book. I bought. I am not able to download/read in my Kindle Book Reader. Please help.
Amazon should do quality control. I can't read this book in the Kindle. Can't download. I would like to download and print. The author does not reply. Very poor user experience.
5 people found this helpful
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Bineta
5.0 out of 5 stars
A Great Introductory Book & Interview Preparation
Reviewed in the United States 🇺🇸 on February 13, 2021Verified Purchase
I really enjoy this book. Data Science is so vast and so much topics you have to understand. This book really covers 80%+ concepts. Granted you need other books to supplement this book to get a deep understanding of everything. But this boom truly goes above and beyond. If you’re just starting out and have no idea where to start— start here. And build up.


Bineta
Reviewed in the United States 🇺🇸 on February 13, 2021
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One person found this helpful
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Malik Hassan Qayyum
2.0 out of 5 stars
Don't Buy
Reviewed in the United States 🇺🇸 on March 3, 2020Verified Purchase
Terrible waste of money
2 people found this helpful
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