1.   Introduction to Data Science and R

a.   What is Data Science

b.   Benefits and Uses of Data Science

c.   What is R

d.   History of R

e.   The R Environment and Statistics

f.   Why Should We Learn R

g.   Installing R and R Studio

h.   Overview of R Screen and R Studio Screen

i.   Installing packages in R

j.   Basic Computations in R

2.   Basic and Intermediate R

a.   Basic Commands in R

b.   Variable and Variable Naming

c.   Data Types in R

d.   Data Structures in R

e.   Conditional and Control Flow

f.   Loops

g.   Functions

h.   The Apply Family

i.   Utilities

3.   Data Manipulation and Joining Using dplyr

a.   Transforming Data in R

b.   Transforming Data with dplyr

c.   Aggregating Data

d.   Selecting and Transforming Data

e.   Case Study (Data Manipulation)

f.   Transforming Data with tidyr

g.   Joining Tables

h.   Left and Right Joins

i.   Full, Semi, and Anti Joins

j.   Case Study (Joining Tables)

4.   Importing and Exporting Data in R

a.   Importing data from flat files with utils

b.   Importing data using readr & data.table

c.   Importing Excel data

d.   Importing data from databases

e.   Importing data from the web

f.   Importing data from statistical software packages

Review Class 1: Lectures 1 to 4

5.   Data Visualization in R

a.   A Quick Introduction to base R graphics

b.   Different plot types

c.   Adding details to plots

d.   Managing plots

e.   Advanced plot customization and beyond

6.   Data visualization with ggplot2

a.   Introduction

b.   Aesthetics

c.   Geometries

d.   Themes

e.   Statistics

f.   Coordinates

g.   Facets

h.   Best Practices

7.   Writing Functions in R and Introduction to Statistics in R

a.   How to write a function

b.   All about arguments

c.   Return values and scope

d.   Case Study (Functions)

e.   Summary Statistics

f.   Random Numbers and Probability

g.   Distributions and Central Limit Theorem

h.   Correlation and Experimental Design

8.   Sampling in R and Hypothesis Testing in R

a.   Introduction to Sampling

b.   Simple, Systematic, Stratified, and Cluster Sampling

c.   Accuracy of Sample Statistics

d.   Bootstraps

e.   A/B Testing, Z-Score, P-Values, Type I and Type II errors

f.   t-test, ANOVA

g.   proportion t-test, chi-square

h.   rank-based non-parametric test

Review Class 2: Lectures 5 to 8

9.   Regressions in R

a.   Simple Linear Regression

b.   Predictions and model objects

c.   Assessing model fit

d.   Multiple linear Regression

e.   Simple logistics regression

f.   Multiple logistic Regression

g.   Training and Evaluating Regression Models

h.   Non-linear Regression

10. Machine Learning with R : Unsupervised Learning (Clustering) & Supervised Learning

(Classification)

a.   Calculating Distance Between Observations

b.   k-means

c.   Hierarchical clustering

d.   Dimensionality reduction with PCA

e.   Case Study

f.   K-Nearest Neighbors

g.   Naïve Bayes

h.   Linear Regression

i.   Classification Tree

11. Machine Learning with R: Supervised Learning

a.   Decision Tree

b.   Random Forest

c.   Support Vector Machine

d.   Artificial Neural Network

12. Reporting with R Markdown and Building Web Application with Shiny

a.   Introduction to Markdown

b.   Getting Started with R Markdown

c.   Adding Analyses and Visualizations

d.  Improving the Report

e.  Customizing the Report

f.   Getting Started with Shiny

g.   Inputs, Outputs, and Layouts

h.   Reactive Programming

i.   Build Shiny Apps

Review Class 3: Lectures 9 to 12