Name: Cracking the Data Science Interview: 101+ Data Science Questions & Solutions (en Inglés)
SKU: 52741599
Price: 26.29 USD
Availability: InStock
Author: Maverick Lin
ISBN: 9781710680133

portada Cracking the Data Science Interview: 101+ Data Science Questions & Solutions (en Inglés)

Cracking the Data Science Interview: 101+ Data Science Questions & Solutions (en Inglés)

Formato

Libro Físico

Editorial

Independently Published

Autor

Maverick Lin

Año

2019

Idioma

Inglés

N° páginas

119

Encuadernación

Tapa Blanda

ISBN13

9781710680133

Categorías

Diseño y teoría de bases de datos
Arquitectura de la información

Cracking the Data Science Interview: 101+ Data Science Questions & Solutions (en Inglés)

Maverick Lin (Autor) · Independently Published · Tapa Blanda

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Reseña del libro "Cracking the Data Science Interview: 101+ Data Science Questions & Solutions (en Inglés)"

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.