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Cracking the Data Science Interview: 101+ Data Science Questions & Solutions
| AUTHOR | Lin, Maverick |
| PUBLISHER | Independently Published (12/17/2019) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
- 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.
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Product Format
Product Details
ISBN-13:
9781710680133
ISBN-10:
171068013X
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
120
Carton Quantity:
58
Product Dimensions:
5.51 x 0.28 x 8.50 inches
Weight:
0.35 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Data Science - Data Modeling & Design
Descriptions, Reviews, Etc.
publisher marketing
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.
- 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.
Show More