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 David S. Touretzky - Benjamin-Cummings Pub Co
This is a gentle introduction to Common Lisp for students taking their first programming course. No prior mathematical background beyond arithmetic is assumed. There are lots of examples, the author avoided technical jargon.
by Peter Van Roy, Seif Haridi - The MIT Press
Covered topics: concurrency, state, distributed programming, constraint programming, formal semantics, declarative concurrency, message-passing concurrency, forms of data abstraction, building GUIs, transparency approach to distributed programming.
by Robert M. Keller - Harvey Mudd College
This book is intended for a second course in computer science, one emphasizing principles wherever it seems possible. It is not limited to programming, it attempts to use various programming models to explicate principles of computational systems.
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.