Data Science with Julia

This is a book for people who want to learn about the Julia language with a view to using it for data science. Some effort has gone into making this book suitable for someone who has familiarity with the R software and wants to learn about Julia. However, prior knowledge of R is not a requirement. While this book is not intended as a textbook for a course, some may find it a useful book to follow for a course that introduces statistics or data science students to Julia. It is our sincere hope that students, researchers and data scientists in general, who wish to learn Julia, will find this book beneficial. This book is not intended as an exhaustive introduction to data science. In fact, this book is far from an exhaustive introduction to data science. There are many very good books that one can consult to learn about different aspects of data science, but this book is primarily about Julia. Nevertheless, several important topics in data science are covered. These include data visualization, supervised learning, and unsupervised learning. When discussing supervised learning, we place some focus on gradient boosting a machine learning technique because we have found this approach very effective in applications. However, for unsupervised learning, we take a more statistical approach and place some focus on the use of probabilistic principal components analysers and a mixture thereof.