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
Biography
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
