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 Susan Rodger - Duke University
These lecture notes present an introduction to theoretical computer science including studies of abstract machines, the language hierarchy from regular languages to recursively enumerable languages, noncomputability and complexity theory.
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 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.
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.