Statistics for Data Scientists: An Introduction to Probability, Statistics, and Data Analysis

This book is a comprehensive undergraduate introduction to data analysis, combining hands-on approach with probability and statistical principles, and focusing on large data sets and streaming data.

Format: Paperback / softback
Length: 321 pages
Publication date: 03 February 2022
Publisher: Springer Nature Switzerland AG

This comprehensive textbook serves as an essential guide for undergraduate students pursuing studies in data science, computer science, and quantitative social science. It seamlessly blends a hands-on approach to data analysis, backed by ample real-world data examples and readily reusable [R] code, with a rigorous exploration of probability and statistical principles. In contrast to contemporary undergraduate textbooks in probability theory or statistics, which often overlook applications and an introductory exploration of modern methods such as bootstrapping and Bayesian inference, this book offers a well-rounded introduction to data analysis, employing statistical techniques that seamlessly combine these two perspectives. Moreover, the book prioritizes methods for handling large data sets and real-time streaming data, making it an ideal single-course introduction to statistical methods for data science.

The text begins by providing a foundational overview of data analysis, covering topics such as data types, descriptive statistics, and basic inference. It then delves into more advanced topics, including hypothesis testing, regression analysis, and Bayesian inference. Throughout the book, real-world data examples are used to illustrate the practical applications of statistical methods, making the content accessible and engaging for students.

One of the key strengths of the textbook is its emphasis on computational tools and programming languages. It provides detailed instructions on using [R] for data analysis, including data manipulation, visualization, and statistical modeling. This hands-on approach allows students to apply the theoretical concepts learned in the text directly to their own data projects, fostering a deeper understanding of the subject matter.

Furthermore, the book recognizes the importance of dealing with large data sets and real-time streaming data in today's data-driven world. It discusses various methods for managing and analyzing these data, such as distributed computing, data compression, and machine learning algorithms. These topics are crucial for students who aspire to work in data science and computer science fields, where the analysis of large and complex data sets is a common challenge.

In conclusion, this textbook is an excellent resource for undergraduate students seeking to develop a strong foundation in data analysis. Its combination of hands-on approach, rigorous theoretical treatment, and emphasis on computational tools and programming languages makes it an invaluable tool for anyone interested in pursuing careers in data science, computer science, or quantitative social science. Whether you are a novice or an experienced student, this book will provide you with the knowledge and skills necessary to succeed in your academic and professional endeavors.

Weight: 540g
Dimension: 155 x 234 x 23 (mm)
ISBN-13: 9783030105303
Edition number: 1st ed. 2022