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Course Overview

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.

Documentation

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