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.
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by Ivo Düntsch, Günther Gediga - Methodos Publishers (UK)
In this book the authors present an overview of the work they have done on the foundations and details of data analysis, the first attempt to do this in a non-invasive way. It is a look at data analysis from many different angles.
by Chris Bourke - University of Nebraska - Lincoln
A draft of text book for Computer Science I, covering CS1 topics in a generic manner using psuedocode with supplemental parts for specific languages. Computer Science is not programming. Programming is a necessary skill, but it is only the beginning.
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.
by Christine Alvarado, et al. - Harvey Mudd College
Our objective is to provide an introduction to computer science as an intellectually vibrant field rather than focusing exclusively on computer programming. We emphasize concepts and problem-solving over syntax and programming language features.