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**: 1,824

**Type**: Textbook

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**Post time**: 29 Sep 2016 04:00:00

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
15
Introduction to Computer Science
33
Introduction to Computer Programming
52
Algorithms and Data Structures
24
Artificial Intelligence
24
Computer Vision
28
Machine Learning
6
Neural Networks
22
Game Development and Multimedia
25
Data Communication and Networks
5
Coding Theory
15
Computer Security
8
Information Security
35
Cryptography
3
Information Theory
17
Computer Organization and Architecture
22
Operating Systems
1
Image Processing
10
Parallel Computing
4
Concurrent Programming
19
Relational Database
3
Document-oriented Database
13
Data Mining
16
Big Data
17
Data Science
23
Digital Libraries
22
Compiler Design and Construction
25
Functional Programming
11
Logic Programming
26
Object Oriented Programming
21
Formal Methods
70
Software Engineering
3
Agile Software Development
7
Information Systems
5
Geographic Information System (GIS)

Mathematics
68
Mathematics
13
Algebra
28
Linear Algebra
3
Number Theory
8
Numerical Methods
2
Precalculus
10
Calculus
5
Category Theory
10
Proofs
19
Discrete Mathematics
24
Theory of Computation
14
Graph Theory
1
Complex Analysis
13
Probability
43
Statistics
7
Game Theory
5
Queueing Theory
13
Operations Research
16
Computer Aided Mathematics

Supporting Fields
19
Web Design and Development
1
Mobile App Design and Development
29
System Administration
2
Cloud Computing
9
Electric Circuits
6
Embedded System
26
Signal Processing
4
Network Science
3
Project Management

Operating System
Programming/Scripting
6
Ada
13
Assembly
34
C / C++
8
Common Lisp
2
Forth
36
Java
12
JavaScript
1
Lua
15
Microsoft .NET
1
Rexx
12
Perl
6
PHP
66
Python
12
R
1
Rebol
13
Ruby
2
Scheme
3
Tcl/Tk

Miscellaneous