Data Science for Business With R

Data Science for Business with R is a textbook designed for students with little to no experience in computer science, focusing on the concepts foundational for students starting a business analytics or data science degree program. It features a running case using a global airline business's customer survey dataset to illustrate how to turn data into business decisions, and full integration of freely-available R and RStudio software. The book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation.

Format: Paperback / softback
Length: 424 pages
Publication date: 13 May 2021
Publisher: SAGE Publications Inc

Written by Jeffrey S. Saltz and Jeffrey M. Stanton, the book focuses on the concepts foundational for students embarking on a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business's customer survey dataset to illustrate how to turn data into business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available.

Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course.

The book is divided into four main sections:

Introduction to Data Science: This section provides an overview of the field of data science and its importance in business. It covers topics such as data types, data structures, and data analysis techniques.

R Programming: This section introduces the R programming language and its features. It covers topics such as data manipulation, statistical analysis, and visualization.

Data Analysis: This section covers the various data analysis techniques used in business, such as descriptive statistics, exploratory data analysis, and regression analysis.

Case Studies: This section features real-world case studies that demonstrate the application of data science techniques in business.

Throughout the book, the authors use a practical and hands-on approach to teaching data science. They provide clear explanations of complex concepts and use real-world examples to illustrate their points. The book also includes numerous exercises and coding examples to help readers practice what they have learned.

Data Science for Business with R is an essential resource for students, professionals, and anyone interested in learning how to use data science to make informed business decisions. With its comprehensive coverage of the field and practical approach, the book provides readers with the skills and knowledge they need to succeed in this rapidly evolving field.

Data Science for Business with R

Data Science for Business with R is a comprehensive guide designed to provide students with a solid foundation in data science and its applications in business. Written by Jeffrey S. Saltz and Jeffrey M. Stanton, the book focuses on the concepts and techniques foundational for students embarking on a business analytics or data science degree program.

To keep the book practical and applied, the authors feature a running case using a global airline business's customer survey dataset to illustrate how to turn data into business decisions. In addition to numerous examples throughout, the text also includes full integration of freely-available R and RStudio software, one of the most popular data science tools available.

The book is divided into four main sections:

Introduction to Data Science: This section provides an overview of the field of data science and its importance in business. It covers topics such as data types, data structures, and data analysis techniques.

R Programming: This section introduces the R programming language and its features. It covers topics such as data manipulation, statistical analysis, and visualization.

Data Analysis: This section covers the various data analysis techniques used in business, such as descriptive statistics, exploratory data analysis, and regression analysis.

Case Studies: This section features real-world case studies that demonstrate the application of data science techniques in business.

Throughout the book, the authors use a practical and hands-on approach to teaching data science. They provide clear explanations of complex concepts and use real-world examples to illustrate their points. The book also includes numerous exercises and coding examples to help readers practice what they have learned.

Data Science for Business with R is an essential resource for students, professionals, and anyone interested in learning how to use data science to make informed business decisions. With its comprehensive coverage of the field and practical approach, the book provides readers with the skills and knowledge they need to succeed in this rapidly evolving field.

In conclusion, Data Science for Business with R is a must-read for anyone interested in leveraging data science to drive business growth and innovation. Whether you are a student, professional, or simply curious about the field, this book provides a comprehensive and practical guide to help you get started.

Weight: 774g
Dimension: 188 x 232 x 27 (mm)
ISBN-13: 9781544370453