Think Stats, 2nd Edition
Exploratory Data Analysis
Publisher: O'Reilly Media
Final Release Date: October 2014
Pages: 226

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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oreillyThink Stats, 2nd Edition
 
4.8

(based on 4 reviews)

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Pros

  • Concise (3)
  • Easy to understand (3)

Cons

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    • Novice (3)
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    4.0

    This book is just what I was looking for

    By warren the runner

    from Broomfield, Co

    About Me Designer, Developer

    Verified Buyer

    Pros

    • Accurate
    • Concise
    • Easy to understand
    • Helpful examples
    • Well-written

    Cons

      Best Uses

      • Novice
      • Student

      Comments about oreilly Think Stats, 2nd Edition:

      The book is well written and easy to understand. I had been looking for a book to learn the Anaconda toolset and brush up on my stats skills. This book fit the bill perfectly.

       
      5.0

      Best intro to stats for programmers ever

      By cast42

      from Belgium

      About Me Developer

      Verified Buyer

      Pros

        Cons

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          • Novice

          Comments about oreilly Think Stats, 2nd Edition:

          Instead of using formula's, this book takes simple python programs to explain basic statistics.

          (1 of 1 customers found this review helpful)

           
          5.0

          Stats without the Noise

          By David Huttleston Jr

          from Madison WI

          About Me Developer, Sys Admin

          Verified Buyer

          Pros

          • Concise
          • Easy to understand
          • Well-written

          Cons

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            • Intermediate
            • Novice

            Comments about oreilly Think Stats, 2nd Edition:

            Exploring and testing the relationships within a dataset are the focus of this book. The author uses python and pandas to present the statistical methods used. But, python and pandas is not the focus of the book, it is just the presentation tool. There is a lot of material in the github repo, which is used in a "blackbox" fashion by the author to allow the logic of the statistical thinking to shine. The result is the clearest treatment of stats I've seen-- at least from the point-of-view of a programmer who handles data work flow challenges. This book, paired with either the online pandas docs or "Python for Data Analysis" is the best way to dig deep.

            (1 of 1 customers found this review helpful)

             
            5.0

            A Good Book for Python-Based Statistics

            By Joe

            from West Liberty, WV

            About Me Educator, Sys Admin

            Verified Reviewer

            Pros

            • Accurate
            • Concise
            • Easy to understand

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              • Intermediate

              Comments about oreilly Think Stats, 2nd Edition:

              This book is a great intermediate-level text on statistical analysis using Python. It is not for the absolute novice, but could be easily used as a supplementary textbook or reference in a statsitics course (assuming the use of Python instead of R). The examples in the book all tie together well, with a common theme.
              If you are looking to perform basic univariate statistical procedures in your Python program, and are unsure how, this book is for you. This book is not an advanced statistical text (e.g.: no PCA, no MDS, no FA...) so it would benefit those familiar with programming yet not data analysis.

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