Data Science Using Python and R

Data Science Using Python and R is a book that teaches data science by doing data science. It covers topics such as data preparation, exploratory data analysis, modeling, and error costs, and provides over 500 exercises for readers to test their skills.

\n Format: Hardback
\n Length: 256 pages
\n Publication date: 14 May 2019
\n Publisher: John Wiley and Sons Ltd
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Data science is a rapidly growing field that has been recognized as one of the "hottest jobs in America" by Bloomberg. It involves the use of advanced techniques and tools to analyze and interpret large amounts of data, enabling organizations to make informed decisions and gain a competitive advantage. Python and R are two of the most widely used open-source platforms for data science, and this book aims to provide a comprehensive guide for beginners who want to learn data science using these tools.

The book is written for the general reader with no previous analytics or programming experience. It begins by introducing the basics of Python and R, including their syntax, data types, and basic programming concepts. An entire chapter is dedicated to learning the basics of Python and R, covering topics such as variables, loops, functions, and data structures.

Each chapter of the book presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.

Exciting new topics such as random forests and general linear models are also included in the book. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars.

To reinforce the learning process, the book includes over 500 exercises at the end of every chapter. These exercises cover a wide range of topics and allow readers to test their newfound data science skills and expertise. The Hands-on Analysis exercises challenge readers to apply the concepts learned in the chapters to real-world data sets, providing a hands-on experience that is essential for developing practical data science skills.

In conclusion, Data Science Using Python and R is a comprehensive and practical guide for beginners who want to learn data science using Python and R. It covers a wide range of topics and provides step-by-step instructions and walkthroughs for solving real-world business problems. The book emphasizes data-driven error costs to enhance profitability, and includes over 500 exercises to reinforce the learning process. Whether you are a business professional, a student, or a researcher, this book will help you gain the skills and knowledge needed to become a successful data scientist.

\n Weight: 534g\n
Dimension: 234 x 156 x 18 (mm)\n
ISBN-13: 9781119526810\n \n