R Programming Language

Course Curriculum

Chapter 1: R Programming Language

Lecture 1: Introduction to R Programming

Lecture 2: R Installation & Setting R Environment

Lecture 3: Variables, Operators & Data types

Lecture 4: Structures

Lecture 5: Vectors

Lecture 6: Vector Manipulation & Sub-Setting

Lecture 7: Constants

Lecture 8: RStudio Installation & Lists Part 1

Lecture 9: Lists Part 2

Lecture 10: List Manipulation, Sub-Setting & Merging

Lecture 11: List to Vector & Matrix Part 1

Lecture 12: Matrix Part 2

Lecture 13: Matrix Accessing

Lecture 14: Matrix Manipulation, rep function & Data Frame

Lecture 15: Data Frame Accessing

Lecture 16: Column Bind & Row Bind

Lecture 17: Merging Data Frames Part 1

Lecture 18: Merging Data Frames Part 2

Lecture 19: Melting & Casting

Lecture 20: Arrays

Lecture 21: Factors

Lecture 22: Functions & Control Flow Statements

Lecture 23: Strings & String Manipulation with Base Package

Lecture 24: String Manipulation with Stringi Package Part 1

Lecture 25: String Manipulation with Stringi Package Part 2 & Date and Time Part 1

Lecture 26: Date and Time Part 2

Lecture 27: Data Extraction from CSV File

Lecture 28: Data Extraction from EXCEL File

Lecture 29: Data Extraction from CLIPBOARD, URL, XML & JSON Files

Lecture 30: Introduction to DBMS

Lecture 31: Structured Query Language, MySQL Installation & Normalization

Lecture 32: Data Definition Language Commands

Lecture 33: Data Manipulation Language Commands

Lecture 34: Sub Queries & Constraints

Lecture 35: Aggregate Functions, Clauses & Views

Lecture 36: Data Extraction from Databases Part 1

Lecture 37: Data Extraction from Databases Part 2 & DPlyr Package Part 1

Lecture 38: DPlyr Package Part 2

Lecture 39: DPlyr Functions on Air Quality Data Set

Lecture 40: Plyr Package for Data Analysis

Lecture 41: Tidyr Package with Functions

Lecture 42: Factor Analysis

Lecture 43: Prob.Table & CrossTable

Lecture 44: Statistical Observations Part 1

Lecture 45: Statistical Observations Part 2

Lecture 46: Statistical Analysis on Credit Data set

Lecture 47: Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts

Lecture 48: Box Plots

Lecture 49: Histograms & Line Graphs

Lecture 50: Scatter Plots & Scatter plot Matrices

Lecture 51: Low Level Plotting

Lecture 52: Bar Plot & Density Plot

Lecture 53: Combining Plots

Lecture 54: Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot

Lecture 55: Mat Plot, ECDF & Box Plot with IRIS Data set

Lecture 56: Additional Box Plot Style Parameters

Lecture 57: Set.Seed Function & Preparing Data for Plotting

Lecture 58: QPlot, ViolinPlot, Statistical Methods & Correlation Analysis

Lecture 59: ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal

Lecture 60: Data Exploration and Visualization

Lecture 61: Machine Learning, Types of ML with Algorithms

Lecture 62: How Machine Solve Real Time Problems

Lecture 63: Nearest Neighbor(KNN) Classification

Lecture 64: KNN Classification with Cancer Data set Part 1

Lecture 65: KNN Classification with Cancer Data set Part 2

Lecture 66: Navie Bayes Classification

Lecture 67: Navie Bayes Classification with SMS Spam Data set & Text Mining

Lecture 68: 68. WordCloud & Document Term Matrix

Lecture 69: Train & Evaluate a Model using Navie Bayes

Lecture 70: MarkDown using Knitr Package

Lecture 71: Decision Trees

Lecture 72: Decision Trees with Credit Data set Part 1

Lecture 73: Decision Trees with Credit Data set Part 2

Lecture 74: Support Vector Machine, Neural Networks & Random Forest

Lecture 75: Regression & Linear Regression

Lecture 76: Multiple Regression

Lecture 77: Generalized Linear Regression, Non Linear Regression & Logistic Regression

Lecture 78: 78. Clustering

Lecture 79: K-Means Clustering with SNS Data Analysis

Lecture 80: Association Rules (Market Basket Analysis)

Lecture 81: Market Basket Analysis using Association Rules with Groceries Data set

Lecture 82: Python Libraries for Data Science