Practical Data Science Cookbook
Publisher: Packt Publishing
Final Release Date: September 2014
Pages: 396

As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.

Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis—R and Python.

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oreillyPractical Data Science Cookbook
 
4.0

(based on 3 reviews)

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Pros

  • Easy to understand (3)
  • Helpful examples (3)

Cons

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4.0

Useful book

By May

from London

Pros

  • Easy to understand
  • Helpful examples

Cons

    Best Uses

      Comments about oreilly Practical Data Science Cookbook:

      This is a useful book for people who wants to know what data science do in real. Recommend it.

       
      4.0

      More of a beginners guide

      By The_Cthulhu_Kid

      from Vienna, Austria

      About Me Developer

      Verified Reviewer

      Pros

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

      Cons

        Best Uses

        • Novice
        • Student

        Comments about oreilly Practical Data Science Cookbook:

        [Full disclosure this review is based on a free review copy!]
        When I first started getting in to programming I became very interested in data science. I dabbled a little in R and played a lot with Python trying to find what would fit me best. I was trying to get my head around a subject that I find fascinating and had I had access to The Practical Data Science Cookbook I would have had a much better idea of what I wanted to pursue.

        The very first recipe in the book explains the data science pipeline:

        - Acquisition
        - Exploration & understanding
        - Munging, wrangling & manipulation
        - Analysis & modelling
        - Communicating & operating

        and talks about how this isn't necessarily a linear process. This is our starting point and provides the structure of the chapters.
        Unlike many cookbooks this seems much more like a tutorial than a collection of recipes. The book is split by language, the first part covers the more statistically minded R and the second the more code oriented Python.

        The four chapters, following the setup chapter, are recipes for use with R. To be honest I didn't work through each recipe in detail as I am not a great fan of R (it's the damned syntax!). I do, however, have enough R knowledge to go through and understand the material. The recipes are well laid out and interesting allowing you to work with data from various areas (including the NFL and the stock market) and how to actually use the data.

        Python gets slightly more coverage with six chapters although the second uses the same data as the first R recipes. I found the python recipes to be a lot of fun perhaps because they are more application oriented. We not only work with the data to get answers but also how to share and make use of those answers.

        The best chapter, at least for me, is almost certainly chapter 8 where we analyse social graphs. Specifically social graphs comprised of Marvel superheroes! Here we find the connections between heroes based on shared appearances in comics. Each recipe builds your understanding of social networks in general while being specific enough to generate interesting data.

        The book isn't without its flaws -- some errors in the code samples (easy enough to catch) or at times being slightly repetitive -- but all in all it is an interesting read and nice introduction to data science.

        The authors have done their best to pick interesting projects and they have done well. They offer insights in to many possible avenues of investigation and is a great place for beginners to start. Each chapter might not be for everyone but anyone interested in data science will find something that will spark their interest.

         
        4.0

        Great interactive title

        By RPZ

        from Bethesda, MD

        About Me Student

        Verified Buyer

        Pros

        • Easy to understand
        • Helpful examples

        Cons

          Best Uses

          • Intermediate
          • Novice

          Comments about oreilly Practical Data Science Cookbook:

          This book throws you into the deep end with R and Python programming, but by following through on the examples with explanation after the fact you get a much better notion of how data scientists actually approach and apply solutions.

          I found it much more helpful than a lot of the online lectures I had found before. By actually watching code constructed before you, you get a handle on some of the nuts and bolts of solving a problem. The book is divided into several chapters called 'recipes,' each of which outlines a different problem and solution.

          I don't give it 5 stars because one of the sites they recommend to scrape financial data no longer provides it for free, meaning that that 'recipe' is no longer accessible.

          However, I think it's an excellent resource for those with a moderate amount of programming experience who want to see how the new data science is actually done.

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