NOC:Exploratory Data Analysis for Data Science with R Software

Lecture 1 - How to Learn and Follow the Course

Lecture 2 - Data Science - Why, What and How ?

Lecture 3 - Introduction to R Software

Lecture 4 - Calculations with R Software - Basics and R as a Calculator

Lecture 5 - Calculations with R Software - Calculations with Data Vectors

Lecture 6 - Calculations with R Software - Built-in Commands and Missing Data Handling

Lecture 7 - Calculations with R Software - Operations with Matrices

Lecture 8 - Data Preparation - CSV and TXT Data Files

Lecture 9 - Data Preparation - Excel Files and Data Frame

Lecture 10 - Introduction to Exploratory Data Analysis - Objectives, Steps and Basic Definitions

Lecture 11 - Introduction to Exploratory Data Analysis - Variables and Types of Data

Lecture 12 - Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Attribute and Discrete Data

Lecture 13 - Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Continuous Data

Lecture 14 - Frequency Distribution - Frequency Distribution with R Package

Lecture 15 - Univariate Graphics and Plots - Bar Diagrams

Lecture 16 - Univariate Graphics and Plots - Subdivided Bar Plots and Pie Diagrams

Lecture 17 - Univariate Graphics and Plots - 3D Pie Diagram and Tree Map

Lecture 18 - Univariate Graphics and Plots - Histogram, Kernel Density and Stem-Leaf Plots

Lecture 19 - Univariate Graphics and Plots - Graphics with ggplot2 Package

Lecture 20 - Univariate Graphics and Plots - Creating Graphics with ggplot2 Package

Lecture 21 - Univariate Graphics and Plots - Bar Diagram and Tree Map with ggplot2

Lecture 22 - Univariate Graphics and Plots - Grouped Bar and Scatter Diagrams with ggplot2 Package

Lecture 23 - Univariate Graphics and Plots - Histogram, Dot Chart and Kernel Density Plots with ggplot2

Lecture 24 - Central Tendency of Data - Arithmetic Mean

Lecture 25 - Central Tendency of Data - Median

Lecture 26 - Central Tendency of Data - Quantiles

Lecture 27 - Central Tendency of Data - Mode

Lecture 28 - Central Tendency of Data - Geometric Mean and Harmonic Mean

Lecture 29 - Variation in Data - Range, Interquartile Range and Quartile Deviation

Lecture 30 - Variation in Data - Absolute Deviation and Absolute Mean Deviation

Lecture 31 - Variation in Data - Mean Squared Error, Variance and Standard Deviation

Lecture 32 - Variation in Data - Computation of Variance and Standard Error with R

Lecture 33 - Variation in Data - Coefficient of Variation and Summary

Lecture 34 - Variation in Data - Box Plots and Violin Plots

Lecture 35 - Moments - Raw, Central and Absolute Moments

Lecture 36 - Moments - Computation of Moments in R

Lecture 37 - Moments - Skewness and Kurtosis

Lecture 38 - Scaling of Data - Centering, Scaling and Z-Scores

Lecture 39 - Association of Variables - Scatter and Smooth Scatter Plots

Lecture 40 - Association of Variables - Line Chart, Time Series Plot and Bubble Chart

Lecture 41 - Association of Variables - Quantile - Quantile Plot

Lecture 42 - Association of Variables - Three-Dimensional Plots, Heat Maps and Word Cloud

Lecture 43 - Association of Variables - Correlation Coefficient

Lecture 44 - Association of Variables - Correlation Coefficient using R Software

Lecture 45 - Association of Variables - Rank Correlation Coefficient

Lecture 46 - Association of Variables - Measures of Association for Discrete and Counting Variables: Bivariate Frequency and Contingency Tables

Lecture 47 - Association of Variables - Measures of Association for Discrete and Counting Variables with R Commands: Contingency Table, Chi-Squared Statistic, Cramer‘s V Statistic and Contingency Coefficient

Lecture 48 - Modelling of Variables : Least Squares Method - One Variable

Lecture 49 - Modelling of Variables : Least Squares Method - More than One Variable and R Commands

Lecture 50 - Handling Text Data : Basic Operations with Text and Strings

Lecture 51 - Handling Text Data : Operations for Finding Patterns in Text Data with Pipe Operator

Lecture 52 - Handling Text Data : Operations for Finding Patterns in Text Data without Pipe Operator

Lecture 53 - Handling Text Data : Analysis of Text Data - Frequency and Plots

Lecture 54 - Selection of Sample : Simple Random Sampling

Lecture 55 - Selection of Sample : Simple Random Sampling with R

Lecture 56 - EDA with Multivariate Data : Sum and Mean Vectors

Lecture 57 - EDA with Multivariate Data : Covariance and Correlation Matrices