Practical Data Science Cookbook -- Table of Contents -- Practical Data Science Cookbook -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Downloading the color images of this book -- Errata -- Piracy -- Questions -- 1. Preparing Your Data Science Environment -- Introduction -- Understanding the data science pipeline -- How to do it... -- How it works... -- Installing R on Windows, Mac OS X, and Linux -- Getting ready -- How to do it... -- How it works... -- See also -- Installing libraries in R and RStudio -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Installing Python on Linux and Mac OS X -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Installing Python on Windows -- How to do it... -- How it works... -- See also -- Installing the Python data stack on Mac OS X and Linux -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Installing extra Python packages -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Installing and using virtualenv -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 2. Driving Visual Analysis with Automobile Data (R) -- Introduction -- Acquiring automobile fuel efficiency data -- Getting ready -- How to do it... -- How it works… -- Preparing R for your first project -- Getting ready -- How to do it... -- How it works... -- See also -- Importing automobile fuel efficiency data into R -- Getting ready -- How to do it...

How it works... -- There's more... -- There's more... -- See also -- Exploring and describing fuel efficiency data -- Getting ready -- How to do it... -- How it works... -- There's more... -- Analyzing automobile fuel efficiency over time -- Getting ready -- How to do it... -- How it works... -- See also -- Investigating the makes and models of automobiles -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 3. Simulating American Football Data (R) -- Introduction -- Requirements -- Acquiring and cleaning football data -- Getting ready -- How to do it… -- How it works… -- See also -- Analyzing and understanding football data -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Constructing indexes to measure offensive and defensive strength -- Getting ready -- How to do it… -- How it works… -- See also -- Simulating a single game with outcomes decided by calculations -- Getting ready -- How to do it… -- How it works… -- Simulating multiple games with outcomes decided by calculations -- Getting ready -- How to do it… -- How it works… -- There's more… -- 4. Modeling Stock Market Data (R) -- Introduction -- Requirements -- Acquiring stock market data -- How to do it... -- Summarizing the data -- Getting ready -- How to do it... -- How it works... -- There's more... -- Cleaning and exploring the data -- Getting ready -- How to do it... -- How it works... -- See also -- Generating relative valuations -- Getting ready -- How to do it... -- How it works... -- Screening stocks and analyzing historical prices -- Getting ready -- How to do it... -- How it works... -- 5. Visually Exploring Employment Data (R) -- Introduction -- Preparing for analysis -- Getting ready -- How to do it… -- How it works… -- See also -- Importing employment data into R -- Getting ready -- How to do it….

How it works… -- There's more… -- See also -- Exploring the employment data -- Getting ready -- How to do it… -- How it works… -- See also -- Obtaining and merging additional data -- Getting ready -- How to do it… -- How it works… -- Adding geographical information -- Getting ready -- How to do it… -- How it works… -- See also -- Extracting state- and county-level wage and employment information -- Getting ready -- How to do it… -- How it works… -- See also -- Visualizing geographical distributions of pay -- Getting ready -- How to do it… -- How it works… -- See also -- Exploring where the jobs are, by industry -- How to do it… -- How it works… -- There's more… -- See also -- Animating maps for a geospatial time series -- Getting ready -- How to do it… -- How it works… -- There is more… -- Benchmarking performance for some common tasks -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- 6. Creating Application-oriented Analyses Using Tax Data (Python) -- Introduction -- An introduction to application-oriented approaches -- Preparing for the analysis of top incomes -- Getting ready -- How to do it... -- How it works... -- Importing and exploring the world's top incomes dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Analyzing and visualizing the top income data of the US -- Getting ready -- How to do it... -- How it works... -- Furthering the analysis of the top income groups of the US -- Getting ready -- How to do it... -- How it works... -- Reporting with Jinja2 -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 7. Driving Visual Analyses with Automobile Data (Python) -- Introduction -- Getting started with IPython -- Getting ready -- How to do it… -- How it works… -- See also -- Exploring IPython Notebook -- Getting ready.

How to do it… -- How it works… -- There's more… -- See also -- Preparing to analyze automobile fuel efficiencies -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Exploring and describing fuel efficiency data with Python -- Getting ready -- How to do it… -- How it works… -- There's more... -- See also -- Analyzing automobile fuel efficiency over time with Python -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Investigating the makes and models of automobiles with Python -- Getting ready -- How to do it… -- How it works… -- See also -- 8. Working with Social Graphs (Python) -- Introduction -- Understanding graphs and networks -- Preparing to work with social networks in Python -- Getting ready -- How to do it... -- How it works... -- There's more... -- Importing networks -- Getting ready -- How to do it... -- How it works... -- Exploring subgraphs within a heroic network -- Getting ready -- How to do it… -- How it works... -- There's more... -- Finding strong ties -- Getting ready -- How to do it... -- How it works... -- There's more... -- Finding key players -- Getting ready -- How to do it... -- How it works... -- There's more… -- The betweenness centrality -- The closeness centrality -- The eigenvector centrality -- Deciding on centrality algorithm -- Exploring the characteristics of entire networks -- Getting ready -- How to do it... -- How it works... -- Clustering and community detection in social networks -- Getting ready -- How to do it... -- How it works... -- There's more... -- Visualizing graphs -- Getting ready -- How to do it... -- How it works... -- 9. Recommending Movies at Scale (Python) -- Introduction -- Modeling preference expressions -- How to do it… -- How it works… -- Understanding the data -- Getting ready -- How to do it… -- How it works… -- There's more….

Ingesting the movie review data -- Getting ready -- How to do it… -- How it works… -- Finding the highest-scoring movies -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Improving the movie-rating system -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Measuring the distance between users in the preference space -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Computing the correlation between users -- Getting ready -- How to do it… -- How it works… -- There's more… -- Finding the best critic for a user -- Getting ready -- How to do it… -- How it works… -- Predicting movie ratings for users -- Getting ready -- How to do it… -- How it works… -- Collaboratively filtering item by item -- Getting ready -- How to do it… -- How it works… -- Building a nonnegative matrix factorization model -- How to do it… -- How it works… -- See also -- Loading the entire dataset into the memory -- Getting ready -- How to do it… -- How it works… -- There's more… -- Dumping the SVD-based model to the disk -- How to do it… -- How it works… -- Training the SVD-based model -- How to do it… -- How it works… -- There's more… -- Testing the SVD-based model -- How to do it… -- How it works… -- There's more… -- 10. Harvesting and Geolocating Twitter Data (Python) -- Introduction -- Creating a Twitter application -- Getting ready -- How to do it... -- How it works... -- See also -- Understanding the Twitter API v1.1 -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Determining your Twitter followers and friends -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Pulling Twitter user profiles -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also.

Making requests without running afoul of Twitter's rate limits.