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.