An Introduction to Data Science
- Jeffrey S. Saltz - Syracuse University, USA
- Jeffrey M. Stanton - Syracuse University, USA
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.
Supplements
- Lab and homework assignments accompany chapter material and are downloadable as R source code.
- R Code from the book, available as an R source file.
- Multimedia content includes links to YouTube videos showing demos of using R, audio, data, and web resources.
Password-protected Instructor Resources include the following:
- Editable, chapter-specific Microsoft® PowerPoint® slides offer you complete flexibility in easily creating a multimedia presentation for your course. Highlight essential content and features.
- Lab and homework assignments and their solutions accompany chapter material and are downloadable as R source code.
- R Code from the book, available as an R source file
- Multimedia content includes links to YouTube videos showing demos of using R, audio, data, and web resources that appeal to students with different learning styles and prompts classroom discussion.
The application of data science in decision-making and the introductory knowledge level in exposing students unfamiliar with data science to the world of decision-making using AI and ML.
