Description
Book SynopsisA 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 progr
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
