Computational and Inferential Thinking: The Foundations of Data Science
by Ani Adhikari, John DeNero
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 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 David Reed - Prentice Hall
The book covers concepts in computing that are most relevant to the beginning student, including computer terminology, the Internet and World Wide Web, the history of computing, the organization and manufacture of computer technology, etc.
by Stefan Hugtenburg, Neil Yorke-Smith - TU Delft Open
This is a textbook for a one quarter introductory course in theoretical computer science. It includes topics from propositional and predicate logic, proof techniques, set theory and the theory of computation, along with practical applications to CS.
by Hans-Peter Bischof
This text is an introduction to the formal study of computation. The course will provide students with a broad perspective of computer science and will acquaint them with various formal systems on which modern computer science is based.