Cracking the Data Science Interview: 101+ Data Science Questions & Solutions - Paperback
$16.18
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- 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.
Cracking the Data Science Interview: 101+ Data Science Questions & Solutions - Paperback
$16.18
by Maverick Lin (Author)
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.
Number of Pages: 120
Dimensions: 0.28 x 8.5 x 5.51 IN
Publication Date: December 17, 2019
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