Chapter 1, Acquire and Prepare the Ingredients – Reading Your Data, provides the recipes to acquire, format, and cleanse data from multiple formats. Handling missing values, standardizing datasets, and transforming between numerical and categorical data are also covered.
Chapter 2, What's in There? – Exploratory Data Analysis, shows you how to perform exploratory data analysis and find underlying patterns to understand our dataset before getting into the analysis process.
Chapter 3, Where does it belong? - Classification, covers several classification techniques from basic classification trees, logistic regression, and support vector machines to text classification using Naive Bayes to find sentiment analysis.
Chapter 4, Give me a number - Regression, covers several algorithms for data prediction, such as linear regression, random forests, neural networks, and regression trees.
Chapter 5, Can you simplify that? – Data Reduction Techniques, covers code recipes for data reduction and clustering. We explore the different clustering algorithms in a practical way.
Chapter 6, Lessons from history - Time Series Analysis, explores how to work with financial time series data, how to visualize it, and how to perform predictions using the ARIMA algorithms.
Chapter 7, How does it look? - Advance data visualization, explores how to make attractive visualizations, 3D graphs, and advanced maps.
Chapter 8, This May also interest you – Building Recommendations Systems, guides you step by step through applying machine learning and data mining techniques, building and optimizing recommender models, followed by a fraud system practical example.
Chapter 9, It's all about Connections – Social Network Analysis, explores how to acquire, visualize, and cluster social network data using public APIs.
Chapter 10, Put your best foot forward – Document and present your Analysis, shows you how to show and share the results of the data analysis. It includes recipes to use R markdown, KnitR, and Shiny to create reports and dynamic dashboards.
Chapter 11, Work Smarter, not Harder – Efficient and elegant R code, covers recipes to handle large datasets using the apply family of functions, the plyr package, and using data tables to slice and dice data.
Chapter 12, Where in the world? – Geospatial Analysis, teaches you how to perform a geospatial data analysis implementing tools such as Google Maps and QGIS using R implementations. It covers how to import maps and visualize your own data into the maps.
Chapter 13, Playing nice – Working with external data sources, shows you how to work with external data sources such as Excel, MySql, or MongoDB, and how to perform large data processing methods with in-memory processing using Apache Spark.