Chapter 1, Preparing Your Data Science Environment, introduces the data science pipeline and helps you get your data science environment properly set up with instructions for the Mac, Windows, and Linux operating systems. This chapter is a guideline for setting up the environment for R and Python on the preceding platforms.
Chapter 2, Driving Visual Analysis with Automobile Data with R, takes you through the process of analyzing and visualizing automobile data to identify trends and patterns in fuel efficiency over time. The chapter will give you a taste of acquisition, exploration, munging, analysis, and communication. The concepts will be implemented in R.
Chapter 3, Creating Application-Oriented Analyses Using Tax Data and Python, shows you how to use Python to transition your analyses from one-off, custom efforts to reproducible and production-ready code using income distribution data as the base for the project.
Chapter 4, Modeling Stock Market Data, shows you how to build your own stock screener and use moving averages to analyze historical stock prices. You will learn how to acquire, summarize, clean, and generate relative evaluations of data.
Chapter 5, Visually Exploring Employment Data, shows you how to obtain employment and earnings data from the Bureau of Labor Statistics and conduct geospatial analysis at different levels with R. The same will be implemented using Python. The focus of this chapter is on the transformation, manipulation, and visualization of data.
Chapter 6, Driving Visual Analyses with Automobile Data, mirrors the automobile data analyses and visualizations in Chapter 2, Driving Visual Analysis with Automobile Data with R, but does so using the powerful programming language, Python. It focuses on the implementation of the analysis model using Python.
Chapter 7, Working with Social Graphs, shows you how to build, visualize, and analyze a social network that consists of comic book character relationships. You will also see the R and Python implementation.
Chapter 8, Recommending Movies at Scale (Python), walks you through building a movie recommender system with Python. You will also learn the R and Python code to implement a predictive model and the use of collaborative filtering to implement a predictive model.
Chapter 9, Harvesting and Geolocating Twitter Data (Python), shows you how to connect to the Twitter API and plot the geographic information contained in profiles. You will also learn the use of RESTful APIs in TextMining
Chapter 10, Forecasting New Zealand Overseas Visitors, explains how to create time series objects and describes various methods to visualize time series data. You will also learn how to build an appropriate model for the data and identify if the data has any trends and seasonal components.
Chapter 11, German Credit Data Analysis, demonstrates Exploratory Data Analysis (EDA), with a few basic tree methods and random forest. You will learn the method to apply EDA, tree-based methods and random forest on some particular data.
