The graph in the previous recipe is much too large for us to get a feel for what is happening at the individual level, although we'll soon look at analyses that tell us interesting things about populations and communities. However, in order for us to see something interesting immediately, let's extract a subgraph to play with. In particular, we'll collect a subgraph for a particular hero in our dataset. When a subgraph is generated with a single person or actor as a focal point, it is called an ego network, and, in fact, the degree of an ego network might be a measure of an individual's self-worth!
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