9781491907337-1491907339-Think Stats: Exploratory Data Analysis

Think Stats: Exploratory Data Analysis

ISBN-13: 9781491907337
ISBN-10: 1491907339
Edition: 2
Author: Allen Downey
Publication date: 2014
Publisher: O'Reilly Media
Format: Paperback 223 pages
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Book details

ISBN-13: 9781491907337
ISBN-10: 1491907339
Edition: 2
Author: Allen Downey
Publication date: 2014
Publisher: O'Reilly Media
Format: Paperback 223 pages

Summary

Think Stats: Exploratory Data Analysis (ISBN-13: 9781491907337 and ISBN-10: 1491907339), written by authors Allen Downey, was published by O'Reilly Media in 2014. With an overall rating of 4.3 stars, it's a notable title among other Data Modeling & Design (Databases & Big Data, Microsoft Programming, Programming) books. You can easily purchase or rent Think Stats: Exploratory Data Analysis (Paperback) from BooksRun, along with many other new and used Data Modeling & Design books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $6.22.

Description

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.

  • Develop an understanding of probability and statistics by writing and testing code
  • Run experiments to test statistical behavior, such as generating samples from several distributions
  • Use simulations to understand concepts that are hard to grasp mathematically
  • Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
  • Use statistical inference to answer questions about real-world data
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