Data Science with Julia

“The book is ideal for people who want to learn Julia through machine-learning examples and is especially relevant for R users – Chapter 7 is devoted to interacting with R from within Julia. The book contains a good balance of equations, code, algorithms written from scratch, and use of built-in machine-learning algorithms. Readers can directly use the code, which is available on GitHub, or dive deeper into how the methods work. A nice feature is the inclusion of probabilistic principal components analysis (PPCA) and mixtures of PPCA for unsupervised learning.”~The Royal Statistical Society

”… the book is an excellent piece of work that makes a start with Julia very easy and that covers all essential aspects of the language. After making the first steps into the realm of Julia with the help of this book, the reader should be able afterwards to find the own path and to specialize into the more individual aspects of the language that no introductory textbook can cover. The same is true for the data science part. After reading the book, the reader will be able to perform the most common analyses alone and learn other, more specific methods from different sources afterwards.”~Daniel Fischer, International Statistical Review

“The book is ideal for people who want to learn Julia through machine-learning examples and is especially relevant for R users – Chapter 7 is devoted to interacting with R from within Julia. The book contains a good balance of equations, code, algorithms written from scratch, and use of built-in machine-learning algorithms. Readers can directly use the code, which is available on GitHub, or dive deeper into how the methods work. A nice feature is the inclusion of probabilistic principal components analysis (PPCA) and mixtures of PPCA for unsupervised learning.”~The Royal Statistical Society

”… the book is an excellent piece of work that makes a start with Julia very easy and that covers all essential aspects of the language. After making the first steps into the realm of Julia with the help of this book, the reader should be able afterwards to find the own path and to specialize into the more individual aspects of the language that no introductory textbook can cover. The same is true for the data science part. After reading the book, the reader will be able to perform the most common analyses alone and learn other, more specific methods from different sources afterwards.”~Daniel Fischer, International Statistical Review

Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics.

Peter Tait is a Ph.D. student at the Department of Mathematics and Statistics at McMaster University. Prior to returning to academia, he worked as a data scientist in the software industry, where he gained extensive practical experience.