An Introduction to Data Science First Edition
Use the Amazon App to scan ISBNs and compare prices.
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Frequently bought together
About the Author
Jeffrey S. Saltz is currently an Associate Professor at Syracuse University, in the School of Information Studies. His research and teaching focus on helping organizations leverage information technology and data for competitive advantage. Specifically, Jeff’s current research focuses on the socio-technical aspects of data science projects, such as how to coordinate and manage data science teams. In order to stay connected to the “real world”, Jeff consults with clients ranging from professional football teams to Fortune 500 organizations.
Prior to becoming a professor, Jeff’s 20+ years of industry experience focused on leveraging emerging technologies and data analytics to deliver innovative business solutions. In his last corporate role, at JPMorgan Chase, he reported to the firm′s Chief Information Officer and drove technology innovation across the organization. Jeff also held several other key technology management positions at the company, including CTO and Chief Information Architect.
Jeff has also served as chief technology officer and principal investor at Goldman Sachs, where he invested and helped incubate technology start-ups. He started his career as a programmer, project leader and consulting engineer with Digital Equipment Corp.
Jeff holds a B.S. degree in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania and a Ph.D. in Information Systems from the New Jersey Institute of Technology.
Jeffrey M. Stanton, Ph.D. (University of Connecticut, 1997) is Associate Provost of Academic Affairs and Professor of Information Studies at Syracuse University. Dr. Stanton’s research focuses on organizational behavior and technology. He is the author of Information Nation: Educating the Next Generation of Information Professionals (2010), with Dr. Indira Guzman and Dr. Kathryn Stam. Stanton has also published many scholarly articles in peer-reviewed behavioral science journals, such as the Journal of Applied Psychology, Personnel Psychology, and Human Performance. His articles also appear in Journal of Computational Science Education, Computers and Security, Communications of the ACM, Computers in Human Behavior, the International Journal of Human-Computer Interaction, Information Technology and People, the Journal of Information Systems Education, the Journal of Digital Information, Surveillance and Society, and Behaviour & Information Technology. He also has published numerous book chapters on data science, privacy, research methods, and program evaluation. Dr. Stanton’s methodological expertise is in psychometrics with published works on the measurement of job satisfaction and job stress. Dr. Stanton′s research has been supported through 18 grants and supplements including the National Science Foundation’s CAREER award.
- Publisher : SAGE Publications, Inc; First edition (September 22, 2017)
- Language : English
- Paperback : 288 pages
- ISBN-10 : 150637753X
- ISBN-13 : 978-1506377537
- Item Weight : 1.1 pounds
- Dimensions : 7.38 x 0.65 x 9.13 inches
- Best Sellers Rank: #83,590 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
There was a problem filtering reviews right now. Please try again later.
The code is not bad but it could be better. The typesetting on the code blocks could use work. The large font makes it easy to read but the way it wraps makes it rough to study and it causes it to not really conform to the popular R styles (like Google's). The choice of R packages is okay but it could be better. In particular, *many* data scientists work extensively with the tidyverse packages and they do data manipulation using dplyr. While bits of the tidyverse are presented it does not get enough attention and the lack of dplyr support is a very bad oversight. Basically the code looks closer to 2014/2015 then 2017/2018.
If you are a reader starting from zero then this is not a bad buy but if you have any data manipulation experience start with R for Data Science: Import, Tidy, Transform, Visualize, and Model Data . It is superb and free on the web.
R is a bit of a mixed bag. If you're not a programmer, you'll probably love it. If you are, well my internal dialog went something like, "@*#(&*$KHUSDFKJ@!!!! Another 'language' to learn!" Luckily, R is more of an overgrown statistical package than an actual language.
This book is short, precise and to the point, and I wouldn't hesitate to recommend it.
So expect to dip toes into the water but this book is very solidly in the shallow end of data understanding, exploration, and data science.