TY  - BOOK
AU  -  Zhang, Nailong
TI  - A tour of data science: : learn R and Python in parallel
SN  - 9780367895860
U1  - 006.312 
PY  - 2021///
CY  - Boco Raton
PB  - CRC Press 
KW  - Python (Computer program language)
KW  - R (Computer program language)
KW  - Data mining
N1  - 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 


N2  - 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
ER  -