What this book covers

Chapter 1, Getting Started with R, introduces basic concepts such as loading the data to R from different sources, implementing various preprocessing techniques to handle missing data and outliers, and managing data from different sources by merging and subsetting it. It also covers arithmetic and string operations in R. Overall, this chapter will help you convert the data to a usable format that can be consumed for further data analysis and model building.

Chapter 2, Exploratory Data Analysis, introduces different statistical techniques that assist not only in the better understanding of the data, but also help in developing intuition about the dataset by summarizing and visualizing the important characteristics of the variables in the dataset.

Chapter 3, Pattern Discovery, focuses on techniques to extract patterns from the raw data as well as to derive sequential patterns hidden in the data. This chapter will touch on the evaluation metrics and the tweaking of parameters to adjust the rank of the association rules. This chapter also discusses the business cases where these techniques can be used.

Chapter 4, Segmentation Using Clustering, demonstrates how and when to perform a clustering analysis, how to identify the ideal number of clusters for a dataset, and how the clustering can be implemented using R. It also focuses on hierarchical clustering and how it is different from normal clustering. You will also learn about the visualization of clusters.

Chapter 5, Developing Regression Models, demonstrates why regression models are used and how logistic regression is different from linear regression. It shows you how to implement regression models using R and also explores the various methods used to check the fit accuracy. It touches on the different methodologies that can be used to improve the accuracy of the model.

Chapter 6, Time Series Forecasting, explains forecasting from fundamentals such as converting the normal data frame to a time series data and shows you methods that help uncover the hidden patterns in time series data. It will also teach you the implementation of different algorithms for the forecasting.

Chapter 7, Recommendation Engine, shows you the basic idea behind a recommendation engine and some of the real-life use cases in the first part of the chapter. In the latter part of the chapter, the popular collaborative filtering algorithm based on items as well as users is explained in detail along with its implementation.

Chapter 8, Communicating Data Analysis, covers some of the best ways to communicate the results to the user, such as how to make data visualization better using packages in R such as ggplot and googleViz, and demonstrates stitching together the visualizations by creating an interactive dashboard using R shiny.