1.1 What is Data Science?

Data science is not only machine learning and statistics, also, it is not all about prediction. Alas, it is not even a discipline fully contained within STEM (Science, Technology, Engineering, and Mathematics) fields (Meng, 2019). But, one thing that we can assure with high confidence is that data science is always about data. Our aims of this book are twofold:

We cover why Julia is an extremely effective language for data science in Section 2. For now, let’s turn our attention towards data.

1.1.1 Data Literacy

According to Wikipedia, the formal definition of data literacy is “the ability to read, understand, create, and communicate data as information.”. We also like the informal idea that, as a data literate, you won’t feel overwhelmed by data, but instead can use it to make the right decisions. Data literacy can be seen as a highly competitive skill to possess. In this book we’ll cover two aspects of data literacy:

  1. Data Manipulation with DataFrames.jl (Section 4). In this chapter you will learn how to:
    1. Read CSV and Excel data into Julia.
    2. Process data in Julia, that is, learn how to answer data questions.
  2. Data Visualization with Plots.jl and Makie.jl (Section 5 and Section 6). In these chapter you will learn how to:


CC BY-NC-SA 4.0 Jose Storopoli, Rik Huijzer and Lazaro Alonso