Think Stats: Probability and Statistics for Programmers

An introduction to Probability and Statistics for Python programmers.

**Tag(s):**
Python
Statistics

**Publication date**: 27 Oct 2014

**ISBN-10**:
1491907339

**ISBN-13**:
9781491907337

**Paperback**:
226 pages

**Views**: 8,640

**Type**: N/A

**Publisher**:
O’Reilly Media, Inc.

**License**:
Creative Commons Attribution-NonCommercial 3.0 Unported

**Post time**: 02 Apr 2016 12:00:00

Think Stats: Probability and Statistics for Programmers

An introduction to Probability and Statistics for Python programmers.

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The licensor cannot revoke these freedoms as long as you follow the license terms.

Click**here** to read the full license.

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Terms and Conditions:

From the Description:

Allen B. Downey wrote:This book is under the Creative Commons Attribution-NonCommercial 3.0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don't use it for commercial purposes.

From the Description:

Allen B. Downey wrote:Think Stats is an introduction to Probability and Statistics for Python programmers.

Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.

If you have basic skills in Python, you can use them to learn concepts in probability and statistics.Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.

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About The Author(s)

Allen B. Downey (born May 11, 1967) is an American computer scientist, Professor of Computer Science at the Franklin W. Olin College of Engineering and writer of free textbooks. Downey received in 1989 his BS and in 1990 his MA, both in Civil Engineering from the Massachusetts Institute of Technology, and his PhD in Computer Science from the University of California at Berkeley in 1997.

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Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
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Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

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