Schedule-1

• What is R programming?
• Advantages of R ?
• What we can do with R?
• Statistics using R?
• Overview and History of R

Schedule-2

• Installing R in Mac/Windows/Ubuntu
• Installing R studio in Mac/Windows/Ubuntu
• Setting up Working Directory for (Windows/Mac)
• R Console Input and Evaluation

Schedule-3, 4

• R Objects and Attributes
• Vectors and Lists
• Matrices
• Factors
• Missing Values
• Data Frames
• Reading Large tables, Reading Tabular data

Schedule -5

• Subsetting – Basics
• Subsetting – Lists
• Subsetting – Matrices
• Subsetting – DataFrames
• Vectorized operations

Schedule -6

• If-else
• While
• For
• Repeat, Next, Break

Schedule-7

• Loop Functions
• Lapply
• apply
• mapply
• tapply
• split

Schedule-8

• Generating Random Numbers
• Simulating linear model
• Random sampling

Schedule-9

• Working with dplyr package

Schedule-10

• Working with lubridate package

Schedule-11

• Working with data.table package

Schedule-12

• Working with ggplot2 (graphics) package

Schedule-13

• Introduction to Machine Learning

Schedule-14

• Mini-Project 1 (Data Wrangling)

Schedule-15

• Mini-Project 2 ( Graphics using R)

Schedule-16

• Mini-Project-13 (Linear Regression using R) – Machine Learning case study

Schedule-17

• Creating an R package (Optional)

All the schedules will be for two days each 1 hour of class on first day followed by 2 days of lab on the next day. Schedule 17 would take 3 days. Each Mini Project will take three days time (Depends on Student though) Pre-Requsite: Having knowledge on Statistics can be helpful.