The Ultimate R Programming & Machine Learning Course

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Welcome Message

Chapter 2: Getting started

Lecture 1: Downloading and installing R

Lecture 2: Introduction to R console

Lecture 3: Learn About R studio

Lecture 4: Learn How to Install and Load Packages with R studio

Lecture 5: Learn How to Use R as a calculator

Lecture 6: Learn and Understand R variables

Lecture 7: Understanding the different data types

Lecture 8: Learn How to store data in vectors – 1

Lecture 9: Learn How to store data in vectors – 2

Lecture 10: Learn and Understand Call Functions

Lecture 11: Advanced Data Structures

Lecture 12: Data Structures in R – Learning Data.frames

Lecture 13: Data Structures in R – Data.frames in Depth

Lecture 14: Data Structures in R – Learning Lists

Lecture 15: Data Structures – Learning Matrices

Lecture 16: Data Structures – Learning Arrays

Lecture 17: R – Learn How to Read a CSV File into R

Lecture 18: R – Excel is not easily readable

Lecture 19: R – Learn How to Read from database

Lecture 20: R – Learn How to Read data files from other statistical tools

Lecture 21: R – Learn How to Load binary R files

Lecture 22: R – Learn How to Load data included with R

Lecture 23: R – Learn How to Scrape data from the web

Chapter 3: R In Depth – Learn How to Create Statistical Graphs

Lecture 1: Introduction

Lecture 2: Base Graphics – Making histograms & Making scatterplots

Lecture 3: Base Graphics – Making boxplots

Lecture 4: Intro to ggplot2

Lecture 5: ggplot2 – Learn about plot histograms & densities

Lecture 6: Learn how to make scatterplots

Lecture 7: Learn how to make boxplots & Violin plots

Lecture 8: Learn how to make line plots

Lecture 9: Learn how to create small multiples

Lecture 10: Learn how to control colors and shapes

Lecture 11: Learn how to add themes to graphs

Chapter 4: R Programming In Depth

Lecture 1: Introduction

Lecture 2: Understanding the basics of function arguments

Lecture 3: R In Depth – Return a value from a function

Lecture 4: R In Depth – Gain flexibility with do.call

Lecture 5: R In Depth – Use if statements to control program flow

Lecture 6: R In Depth – Stagger if statements with else

Lecture 7: R In Depth – Check multiple statements with switch

Lecture 8: R In Depth – Run checks on entire vectors

Lecture 9: R In Depth – Check compound statements

Lecture 10: R In Depth – Iterate with a for loop

Lecture 11: R In Depth – Iterate with a while loop

Lecture 12: R In Depth – Control loops with break and next

Chapter 5: Learn and Understand Data Munging

Lecture 1: Introduction

Lecture 2: Data Munging – Repeat an operation on a list

Lecture 3: Data Munging – Learn About the mapply

Lecture 4: Data Munging – Learn About the aggregate function

Lecture 5: Data Munging – Learn About plyr package

Lecture 6: Data Munging – Combine datasets

Lecture 7: Data Munging – Join datasets

Lecture 8: Data Munging – Switch storage paradigms

Chapter 6: Learn How to Manipulate Strings

Lecture 1: Introduction

Lecture 2: Manipulating Strings – Combine strings together

Lecture 3: Manipulating Strings – Extract text

Chapter 7: Understanding Statistics in R

Lecture 1: Introduction

Lecture 2: Statistics – Draw numbers from probability distributions

Lecture 3: Statistics – Calculate averages, standard deviations and correlations

Lecture 4: Statistics – Compare samples with t-tests and analysis of variance

Chapter 8: Learn and Understand Linear Models

Lecture 1: Introduction

Lecture 2: Linear Models – Explore the data

Lecture 3: Linear Models – Fit multiple regression models

Lecture 4: Linear Models – Fit logistic regression

Lecture 5: Linear Models – Fit Poisson regression

Lecture 6: Linear Models – Analyze survival data

Lecture 7: Linear Models – Assess model quality with residuals

Lecture 8: Linear Models – Compare models

Lecture 9: Linear Models – Judge accuracy

Lecture 10: Linear Models – Estimate uncertainty with the bootstrap

Lecture 11: Linear Models – Choose variables using stepwise selection

Chapter 9: Learn More About Models

Lecture 1: Introduction

Lecture 2: Models – Decrease uncertainty with weakly informative priors

Lecture 3: Models – Learn About Fit nonlinear least squares

Lecture 4: Models – Learn About Splines

Lecture 5: Models – Learn About GAMs

Lecture 6: Models – Fit decision trees to make a random forest

Chapter 10: Learn and Understand Time Series

Lecture 1: Introduction

Lecture 2: Time Series – Fit and assess ARIMA models

Lecture 3: Time Series – Learn How to Use VAR for multivariate time series

Lecture 4: Time Series – Learn How to Use GARCH for better volatility modeling

Chapter 11: Learn About Clustering

Lecture 1: Clustering – Partition data

Lecture 2: Clustering – Robustly cluster, even with categorical data, with PAM

Lecture 3: Clustering – Perform hierarchical clustering

Chapter 12: knitr – Slideshows & Reports

Lecture 1: Introduction