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Introduction to Data Science

Jeffrey S. Saltz | Jeffrey M. Stanton
€105.99
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A01=Jeffrey M. Stanton
A01=Jeffrey Morgan Stanton
A01=Jeffrey S. Saltz
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An Introduction to Data Science
Author_Jeffrey M. Stanton
Author_Jeffrey Morgan Stanton
Author_Jeffrey S. Saltz
automatic-update
big data
Category1=Non-Fiction
Category=JHBC
Category=UKS
Category=UNH
Category=UT
Category=UTE
Coding
COP=United States
data munging
data visualization
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Language_English
model building
PA=Available
Price_€50 to €100
PS=Active
R platform
softlaunch
Statistics
text mining

Product details

  • ISBN 9781506377537
  • Weight: 500g
  • Dimensions: 187 x 231mm
  • Publication Date: 21 Dec 2017
  • Publisher: SAGE Publications Inc
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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Description

An Introduction to Data Science is an easy-to-read, gentle introduction for advanced undergraduate, certificate, and graduate students coming from a wide range of backgrounds into the world of data science. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using the R programming language and RStudio® from the ground up. Short chapters allow instructors to group concepts together for a semester course and provide students with manageable amounts of information for each concept. By taking students systematically through the R programming environment, the book takes the fear out of data science and familiarizes students with the environment so they can be successful when performing advanced functions.

 

The authors cover statistics from a conceptual standpoint, focusing on how to use and interpret statistics, rather than the math behind the statistics. This text then demonstrates how to use data effectively and efficiently to construct models, predict outcomes, visualize data, and make decisions. Accompanying digital resources provide code and datasets for instructors and learners to perform a wide range of data science tasks.  
About Jeffrey M. Stanton | Jeffrey Morgan Stanton | Jeffrey S. Saltz
Jeffrey S. Saltz is an Associate Professor at Syracuse University in the School of Information Studies and Director of the school′s Master′s of Science program in Applied Data Science. His research and teaching focus on helping organizations leverage information technology and data for competitive advantage. Specifically, his 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”, Dr. Saltz consults with clients ranging from professional football teams to Fortune 500 organizations. Prior to becoming a professor, Dr. Saltz′s two decades 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. He also served as Chief Technology Officer and Principal Investor at Goldman Sachs, where he helped incubate technology start-ups. He started his career as a programmer, project leader and consulting engineer with Digital Equipment Corp. Dr. Saltz 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 PhD in Information Systems from the New Jersey Institute of Technology. Jeffrey M. Stanton, Ph.D. is a Professor at Syracuse University in the School of Information Studies. Dr. Stanton’s research focuses on the impacts of machine learning on organizations and individuals. He is the author of Reasoning with Data (2017), an introductory statistics textbook. 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 research has been supported through 19 grants and supplements including the National Science Foundation’s CAREER award. Before getting his PhD, Stanton was a software developer who worked at startup companies in the publishing and professional audio industries. He holds a bachelor′s degree in Computer Science from Dartmouth College, and a master′s and Ph.D. in Psychology from the University of Connecticut.

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