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 Brian Harvey - The MIT Press
This series is for people who are interested in computer programming because it's fun. The three volumes use the Logo as the vehicle for an exploration of computer science from the perspective of symbolic computation and artificial intelligence.
by Andrzej Yatsko, Walery Suslow - De Gruyter Open
The objective of this book is to provide the reader with all the necessary elements to get him or her started in the modern field of informatics and to allow him or her to become aware of the relationship between key areas of computer science.
An electronic book for teaching Computational Science and Engineering. The intended audience are students in science and engineering at the advanced undergraduate level and higher. Tutorials for networking and visualization software are included.
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.