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

by Nailong Zhang

Overview

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. 

Key features:

• Allows you to learn R and Python in parallel
• Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas
• Provides a concise and accessible presentation
• Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc.

Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

Product Details

ISBN-13:	9780367897062
Publisher:	CRC Press
Publication date:	11/12/2020
Series:	Chapman & Hall/CRC Data Science Series
Pages:	216
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Nailong Zhang is lead Data Scientist at Mass Mutual Life Insurance Company.

Table of Contents

Assumptions about the reader’s background
Book overview

Introduction to R/Python Programming
/Calculator

Variable and Type
/Functions
/Control flows
Some built-in data structures
Revisit of variables
/Object-oriented programming (OOP) in R/Python
Miscellaneous

More on R/Python Programming
/Work with R/Python scripts
/Debugging in R/Python
Benchmarking
/Vectorization
/Embarrassingly parallelism in R/Python
Evaluation strategy
/Speed up with C/C++ in R/Python
/A first impression of functional programming Miscellaneous

data.table and pandas
/SQL
/Get started with data.table and pandas
Indexing & selecting data
/Add/Remove/Update
/Group by
/Join

Random Variables, Distributions & Linear Regression
/A refresher on distributions
Inversion sampling & rejection sampling
/Joint distribution & copula
/Fit a distribution
/Confidence interval
/Hypothesis testing
/Basics of linear regression
/Ridge regression

Optimization in Practice
/Convexity
/Gradient descent
/Root-finding
General purpose minimization tools in R/Python
Linear programming
Miscellaneous

Machine Learning - A gentle introduction
/Supervised learning
/Gradient boosting machine
/Unsupervised learning
Reinforcement learning
/Deep Q-Networks
/Computational differentiation
Miscellaneous