A Tour of Data Science
Learn R and Python in Parallel
By: Nailong Zhang
A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
AI Overview
"A Tour of Data Science: Learn R and Python in Parallel" by Nailong Zhang is a comprehensive guide to the fundamentals of data science, focusing on programming, statistics, optimization, and machine learning. Here is a detailed overview of the book:
Key Themes
- Programming and Statistics: The book covers the essential programming concepts in both R and Python, along with statistical techniques that are crucial for data science.
- Optimization and Predictive Modeling: It delves into optimization methods and predictive modeling techniques, including linear regression, lasso, ridge regression, logistic regression, and gradient boosting trees.
- Data Manipulation Tools: The book includes popular data manipulation tools such as
data.tablein R andpandasin Python. - Machine Learning Algorithms: It provides a hands-on approach to implementing machine learning algorithms from scratch, making it accessible for beginners.
Plot Summary
The book is structured to provide a concise and accessible presentation of data science concepts. It does not aim to cover every aspect of data science but rather focuses on the key concepts and topics. The content is designed to be parallel, allowing readers to learn both R and Python simultaneously, which is a unique feature of the book.
Critical Reception
While detailed reviews are scarce, the book's appeal to data scientists, statisticians, and quantitative analysts is evident. Here are some points from the available sources:
- Accessibility: The book is praised for its concise and accessible presentation, making it suitable for readers with varying levels of programming experience.
- Comprehensive Coverage: It covers a wide range of topics in data science, including programming, statistics, optimization, and machine learning.
- Hands-on Approach: The inclusion of machine learning algorithms implemented from scratch is a significant strength, as it allows readers to understand the underlying mechanics of these algorithms.
Additional Information
- Publication Details: The book was published on November 12, 2020, by Chapman & Hall/CRC as part of the Chapman & Hall/CRC Data Science Series. It has 216 pages and is available in both print and eBook formats.
- Author Background: Nailong Zhang has written two books on Goodreads, with "A Tour of Data Science" being his most popular work.
Overall, "A Tour of Data Science: Learn R and Python in Parallel" is a valuable resource for those looking to learn the fundamentals of data science using both R and Python. Its concise and accessible approach makes it an excellent choice for beginners and intermediate learners alike.