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Jeffrey S. Saltz - Data Science for Business With R

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Jeffrey S. Saltz Data Science for Business With R

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Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting 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 businesss customer survey dataset to illustrate how to turn data in 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. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field.

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Data Science for Business With R

Sara Miller McCune founded SAGE Publishing in 1965 to support the dissemination of usable knowledge and educate a global community. SAGE publishes more than 1000 journals and over 800 new books each year, spanning a wide range of subject areas. Our growing selection of library products includes archives, data, case studies and video. SAGE remains majority owned by our founder and after her lifetime will become owned by a charitable trust that secures the company's continued independence.

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Data Science for Business With R
  • Jeffrey S. Saltz
  • Syracuse University
  • Jeffrey M. Stanton
  • Syracuse University
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Copyright 2022 by SAGE Publications, Inc.

All rights reserved. Except as permitted by U.S. copyright law, no part of this work may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without permission in writing from the publisher.

All third party trademarks referenced or depicted herein are included solely for the purpose of illustration and are the property of their respective owners. Reference to these trademarks in no way indicates any relationship with, or endorsement by, the trademark owner.

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Brief Contents
Instructor Preface

Data science has continued to grow in importance within many different organizational contexts. As data science tools improve, a larger and larger number of students will need to possess a basic level of data science skills and knowledge. With this in mind, our goal of creating this book was to provide an easy to understand introductory data science text that provides an intuitive understanding of how to apply data science concepts within a variety of organizational contexts. As such, we've worked to make the book easy to read and to provide students with hands on learning opportunities that will prepare them for the workplace. We chose to use the R programming language, because it is freely available to all students and instructors, it is relatively easy to learn as a first language, the R-Studio development environment is superior to most of the alternatives, and it connects conveniently with common spreadsheet formats.

We've tested the content of this book thoroughly over many semesters with a diverse range of students. In teaching our introductory data science course, we have noticed an increase in the number of business majors taking the course. In response, this book has a specific focus on how to use data science within business contexts. We leverage one business case across the whole book this includes a realistic data set that we use to illustrate a variety of data science techniques. As such, our book will help students gain an appreciation of not only the different strategies that a data scientist can use to get insight in the data, but also allows students to understand the full life cycle of executing a data science project from reading and cleaning data, to exploratory analysis, to using machine learning insights to provide actionable advice to a client or business partner.

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