Computational and Inferential Thinking: The Foundations of Data Science
by Ani Adhikari, John DeNero
Publisher: GitBook 2017
Number of pages: 646
Data Science is about drawing useful conclusions from large and diverse data sets through exploration, prediction, and inference. Our primary tools for exploration are visualizations and descriptive statistics, for prediction are machine learning and optimization, and for inference are statistical tests and models.
Home page url
Download or read it online for free here:
by Victor Eijkhout - University of Texas
A computational scientist needs knowledge of several aspects of numerical analysis and discrete mathematics. This text covers: computer architecture, parallel computers, machine arithmetic, numerical linear algebra, applications.
by Max Hailperin, Barbara Kaiser, Karl Knight - Course Technology
The book Concrete Abstractions covers the programming and data structures basics. It will give first-time computer science students the opportunity to not only write programs, but to prove theorems and analyze algorithms as well.
by John Whitington - Coherent Press
Using examples from the publishing industry, Whitington introduces the fascinating discipline of Computer Science to the uninitiated. Chapters: Putting Marks on Paper; Letter Forms; Storing Words; Looking and Finding; Typing it In; Saving Space; etc.
by Owen L. Astrachan - McGraw - Hill
This book is designed for a first course in computer science that uses C++ as the programming language. The goal was to leverage the best features of the language using sound practices of programming and pedagogy in the study of computer science.