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An Introduction to Data Science

An Introduction to Data Science PDF

Author: Jeffrey S. Saltz and Jeffrey M. Stanton

Publisher: SAGE Publications


Publish Date: October 3, 2017

ISBN-10: 150637753X

Pages: 288

File Type: Epub

Language: English

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Book Preface

This book began as the key ingredient to one of those massive open online courses, or MOOCs, and was written from the start to welcome people with a wide range of backgrounds into the world of data science. In the years following the MOOC we kept looking for, but never found, a better textbook to help our students learn the fundamentals of data science. Instead, over time, we kept refining and improving the book such that it has now become in integrated part of how we teach data science.

In that welcoming spirit, the book assumes no previous computer programming experience, nor does it require that students have a deep understanding of statistics. We have successfully used the book for both undergraduate and graduate level introductory courses. By using the free and open source R platform (R Core Team, 2016) as the basis for this book, we have also ensured that virtually everyone has access to the software needed to do data science. Even though it takes a while to get used to the R command line, our students have found that it opens up great opportunities to them, both academically and professionally.

In the pages that follow, we explain how to do data science by using R to read data sets, clean them up, visualize what’s happening, and perform different modeling techniques on the data. We explore both structured and unstructured data. The book explains, and we provide via an online repository, all the commands that teachers and learners need to do a wide range of data science tasks.

If your goal is to consider the whole book in the span of 14 or 15 weeks, some of the earlier chapters can be grouped together or made optional for those learners with good working knowledge of data concepts. This approach allows an instructor to structure a semester so that each week of a course can cover a different chapter and introduce a new data science concept.

Many thanks to Leah Fargotstein, Yvonne McDuffee, and the great team of folks at Sage Publications, who helped us turn our manuscript into a beautiful, professional product. We would also like to acknowledge our colleagues at the Syracuse University School of Information Studies, who have been very supportive in helping us get student feedback to improve this book. Go iSchool!

There were a number of reviewers we would like to thank who provided extremely valuable feedback during the development of the manuscript:

  • Luis F. Alvarez Leon, University of Southern California
  • Youngseek Kim, University of Kentucky
  • Nir Kshetri, UNC Greensboro
  • Richard N. Landers, Old Dominion University
  • John W. Mohr, University of California, Santa Barbara
  • Ryan T. Moore, American University and The Lab @ DC
  • Fred Oswald, Rice University
  • Eliot Rich, University at Albany, State University of New York
  • Ansaf Salleb-Aouissi, Columbia University
  • Toshiyuki Yuasa, University of Houston

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