Data Science with Julia

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.

"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

• Chapter 1 Introduction DATA SCIENCE BIG DATA JULIA JULIA PACKAGES R PACKAGES DATASETS Overview Beer Data Coffee Data Leptograpsus Crabs Data Food Preferences Data x Data Iris Data OUTLINE OF THE CONTENTS OF THISMONOGRAPH Chapter 2 Core Julia VARIABLE NAMES TYPES Numeric Floats Strings Tuples DATA STRUCTURES Arrays Dictionaries CONTROL FLOW Compound Expressions Conditional Evaluation Loops Basics Loop termination Exception Handling FUNCTIONS Chapter 3 Working with Data DATAFRAMES CATEGORICAL DATA IO USEFUL DATAFRAME FUNCTIONS SPLIT-APPLY-COMBINE STRATEGY QUERYJL Chapter 4 Visualizing Data GADFLYJL VISUALIZING UNIVARIATE DATA DISTRIBUTIONS VISUALIZING BIVARIATE DATA ERROR BARS FACETS SAVING PLOTS Chapter 5 Supervised Learning INTRODUCTION CROSS-VALIDATION Overview K-Fold Cross-Validation K-NEAREST NEIGHBOURS CLASSIFICATIONCLASSIFICATION AND REGRESSION TREES Overview Classification Trees Regression Trees Comments BOOTSTRAP RANDOM FORESTS GRADIENT BOOSTING Overview Beer Data Food Data COMMENTS Chapter 6 Unsupervised Learning INTRODUCTION PRINCIPAL COMPONENTS ANALYSIS PROBABILISTIC PRINCIPAL COMPONENTSANALYSIS EM ALGORITHM FOR PPCA Background: EM Algorithm E-step M-step Woodbury Identity Initialization Stopping Rule Implementing the EM Algorithm forPPCA K-MEANS CLUSTERING MIXTURE OF PPCAS Model Parameter Estimation Illustrative Example: Coffee Data Chapter 7 R Interoperability ACCESSING R DATASETS INTERACTING WITH R EXAMPLE: CLUSTERING AND DATA REDUCTIONFOR THE COFFEE DATA Coffee Data PGMM Analysis VSCC Analysis EXAMPLE: FOOD DATA Overview Random Forests