Data Science with Julia

There is a dearth of resources for data scientists, statisticians, etc., wishing to learn about Julia. Using well known data science methods, this book will both motivate the reader and assuage any unease. The book will get readers up to speed on key features of the Julia language and illustrate some of its advantages for data science work.

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 THIS MONOGRAPH 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 Contents _ ix CROSS-VALIDATION Overview K-Fold Cross-Validation K-NEAREST NEIGHBOURS CLASSIFICATION CLASSIFICATION 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 COMPONENTS ANALYSIS EM ALGORITHM FOR PPCA Background: EM Algorithm E-step M-step Woodbury Identity Initialization Stopping Rule Implementing the EM Algorithm for PPCA Comments 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 REDUCTION FOR THE COFFEE DATA Coffee Data PGMM Analysis VSCC Analysis EXAMPLE: FOOD DATA Overview Random Forests