In part 1, you learned to load and analyze data by using data structures that are part of Base Julia, like vectors, matrices, dictionaries, and named tuples. I am sure you will find these skills useful in your projects. However, many standard data processing tasks are repeatedly needed by users—for example, reading data from a CSV file or aggregating data. Since you do not want to reimplement these tasks from scratch every time, a wide collection of Julia packages were designed to make doing them easy and efficient.
In this second part of the book, you will learn how to use DataFrames.jl and related packages to build complex data analysis pipelines. We’ll cover a wide range of topics, starting from fetching and reading data, to data transformation, and finishing with building simple data analysis models and visualization.
You can expect that the topics covered in these chapters will gradually become more challenging from both data science and programming perspectives. I have selected the material in this part in a way that ensures that after learning, you will be ready to do data analysis projects and learn and use packages not covered in the book.