Part 1. Introduction to data science

In part 1, we concentrate on the most essential tasks in data science: working with your partners, defining your problem, and examining your data.

Chapter 1 covers the lifecycle of a typical data science project. We look at the different roles and responsibilities of project team members, the different stages of a typical project, and how to define goals and set project expectations. This chapter serves as an overview of the material that we cover in the rest of the book and is organized in the same order as the topics that we present.

Chapter 2 dives into the details of loading data into R from various external formats and transforming the data into a format suitable for analysis. It also discusses the most important R data structure for a data scientist: the data frame. More details about the R programming language are covered in appendix A.

Chapters 3 and 4 cover the data exploration and treatment that you should do before proceeding to the modeling stage. In chapter 3, we discuss some of the typical problems and issues that you’ll encounter with your data and how to use summary statistics and visualization to detect those issues. In chapter 4, we discuss data treatments that will help you deal with the problems and issues in your data. We also recommend some habits and procedures that will help you better manage the data throughout the different stages of the project.

On completing part 1, you’ll understand how to define a data science project, and you’ll know how to load data into R and prepare it for modeling and analysis.