Every data science project starts with data and this chapter is no different. For this recipe, we will dive into a dataset that contains fuel efficiency performance metrics, measured in miles per gallon (MPG) over time, for most makes and models of automobiles available in the U.S. since 1984. This data is courtesy of the U.S. Department of Energy and the US Environmental Protection Agency. In addition to fuel efficiency data, the dataset also contains several features and attributes of the automobiles listed, thereby providing the opportunity to summarize and group data to determine which groups tend to have better fuel efficiency historically and how this has changed over the years. The latest version of the dataset is available at http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip, and information about the variables in the dataset can be found at http://www.fueleconomy.gov/feg/ws/index.shtml#vehicle. The data was last updated on December...
Practical Data Science Cookbook
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Practical Data Science Cookbook
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Overview of this book
<p>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.</p>
<p>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.</p>
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Practical Data Science Cookbook
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Table of Contents (18 chapters)
Practical Data Science Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Preparing Your Data Science Environment
Driving Visual Analysis with Automobile Data (R)
Simulating American Football Data (R)
Modeling Stock Market Data (R)
Visually Exploring Employment Data (R)
Creating Application-oriented Analyses Using Tax Data (Python)
Driving Visual Analyses with Automobile Data (Python)
Working with Social Graphs (Python)
Recommending Movies at Scale (Python)
Harvesting and Geolocating Twitter Data (Python)
Optimizing Numerical Code with NumPy and SciPy (Python)
Index
Customer Reviews