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 Allen B. Downey
This book teaches you to think like a computer scientist - to combine the best features of mathematics, natural science, and engineering, to use formal languages to denote ideas, to observe the behavior of complex systems, form hypotheses, etc.
- Educational Testing Service
This book contains one actual, full-length GRE Computer Science Test and test-taking strategies. The test consists of about 70 multiple-choice questions, some of which are grouped in sets and based on diagrams, graphs, and program fragments.
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 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.