Data Science: A First Introduction uses R programming language in Jupyter notebooks for data manipulation, cleaning, visualizations, and insights. It emphasizes clear, reproducible workflows and includes autograded Jupyter worksheets for interactive learning. The book is designed for learners from all disciplines with minimal prior knowledge.

Format: Paperback / softback
Length: 456 pages
Publication date: 11 April 2022
Publisher: Taylor & Francis Ltd

Data Science: A First Introduction

Data Science: A First Introduction is a comprehensive guide that focuses on utilizing the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data through classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.

Based on educational research and active learning principles, the book adopts a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbias DSCI100: Introduction to Data Science course.

The book is organized into five chapters, each covering a different aspect of data science. The first chapter provides an introduction to the field and discusses the importance of data in modern society. The second chapter introduces the R programming language and its features, including data types, vectors, matrices, and functions. The third chapter focuses on data manipulation and cleaning, including tasks such as importing data, checking for missing values, and transforming data. The fourth chapter introduces effective visualizations, such as plots, charts, and graphs, and demonstrates how to create them using R. The fifth chapter covers classification, regression, clustering, and inference, and demonstrates how to apply these techniques to analyze data and make predictions.

Throughout the book, the authors emphasize the importance of workflows that are clear, reproducible, and shareable. They provide examples of good practices, such as using version control tools like Git and creating reproducible reports using tools like R Markdown. The authors also include coverage of the basics of version control, which is essential for maintaining and collaborating on projects.

In addition to the technical content, the book also includes a wealth of resources for further learning. The authors provide links to online tutorials, workshops, and courses that can help learners expand their knowledge and skills in data science. They also include examples of real-world data sets and projects that can be used as starting points for learners to apply their knowledge and build their own data science projects.

Overall, Data Science: A First Introduction is a comprehensive and accessible guide that provides learners with the skills and knowledge they need to become successful data scientists. Whether you are a beginner or an experienced researcher, this book will leave you well-prepared for data science projects and ready to explore the exciting world of data analysis and visualization.

Weight: 840g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367524685