A Tour of Data Science: Learn R and Python in Parallel

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
	0.1 Assumptions About the Reader’s Background
	0.2 Book Overview
Chapter 1 Introduction to R/Python Programming
	1.1 Calculator
	1.2 Variable and Type
	1.3 Functions
	1.4 Control Flows
	1.5 Some Built-In Data Structures
	1.6 Revisit of Variables
	1.7 Object-Oriented Programming (OOP) In R/Python
	1.8 Miscellaneous
Chapter 2 More on R/Python Programming
	2.1 Work with R/Python Scripts
	2.2 Debugging in R/Python
	2.3 Benchmarking
	2.4 Vectorization
	2.5 Embarrassingly Parallelism in R/Python
	2.6 Evaluation Strategy
	2.7 Speed Up With C/C++ in R/Python
	2.8 A First Impression of Functional Programming
	2.9 Miscellaneous
Chapter 3 data. table and pandas
	3.1 SQL
	3.2 Get Started with data.table and Pandas
	3.3 Indexing & Selecting Data
	3.4 Add/Remove/Update
	3.5 Group by
	3.6 Join
Chapter 4 Random Variables, Distributions & Linear Regression
	4.1 A Refresher on Distributions
	4.2 Inversion Sampling & Rejection Sampling
	4.3 Joint Distribution & Copula
	4.4 Fit a Distribution
	4.5 Confidence Interval
	4.6 Hypothesis Testing
	4.7 Basics of Linear Regression
	4.8 Ridge Regression
Chapter 5 Optimization in Practice
	5.1 Convexity
	5.2 Gradient Descent
	5.3 Root-Finding
	5.4 General Purpose Minimization Tools in R/Python
	5.5 Linear Programming
	5.6 Miscellaneous
Chapter 6 Machine Learning - A gentle introduction
	6.1 Supervised Learning
	6.2 Gradient Boosting Machine
	6.3 Unsupervised Learning
	6.4 Reinforcement Learning
	6.5 Deep Q-Networks
	6.6 Computational Differentiation
	6.7 Miscellaneous
Bibliography
Index