Foundations of Data Science

Provides the background needed for a modern theoretical course in computer science.

**Publication date**: 11 Apr 2014

**ISBN-10**:
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**ISBN-13**:
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**Paperback**:
414 pages

**Views**: 14,359

**Type**: Textbook

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**Post time**: 29 Sep 2016 05:00:00

Foundations of Data Science

Provides the background needed for a modern theoretical course in computer science.

Excerpts from the Preface:

John Hopcroft and Ravindran Kannan wrote:Computer science as an academic discipline began in the 60’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context free languages, and computability. In the 70’s, algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications. There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks, which are by far the largest such structures, presents both opportunities and challenges for theory.

While traditional areas of computer science are still important and highly skilled individuals are needed in these areas, the majority of researchers will be involved with using computers to understand and make usable massive data arising in applications, not just how to make computers useful on specific well-defined problems. With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as automata theory, algorithms and related topics gave students an advantage in the last 40 years. One of the major changes is the switch from discrete mathematics to more of an emphasis on probability, statistics, and numerical methods.

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About The Author(s)

John E. Hopcroft is the IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell University. Hopcroft's research centers on theoretical aspects of computing, especially analysis of algorithms, automata theory, and graph algorithms. His most recent work is on the study of information capture and access.

Ravindran Kannan is a Principal Researcher at Microsoft Research India, where he leads the algorithms research group. He is also the first adjunct faculty of Computer Science and Automation Department of Indian Institute of Science.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

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Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
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Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
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Python
Rebol
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