Learn · Exploratory Data Analysis for Data Science with R Software (Hindi)
Lecture 17 of 57Week 3
Lecture 16 : Univariate Graphics and Plots - 3D Pie Diagram and Tree Map
Prof. Shalabh · IIT Kanpur
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- Exploratory Data Analysis for Data Science with R Software (Hindi), IIT Kanpur (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Lecture 0 : How to Learn and Follow the Course
- Lecture 1 : Data Science - Why, What and How?
- Lecture 2 : Introduction to R Software
- Lecture 3 : Calculations with R Software - Basics and R as a Calculator
- Lecture 4 : Calculations with R Software - Calculations with Data Vectors
- Lecture 5 : Calculations with R Software - Built in Commands and Missing Data Handling
- Lecture 6 : Calculations with R Software - Operations with Matrices
- Lecture 7 : Data Preparation - CSV and TXT Data Files
- Lecture 8 : Data Preparation - Excel Files and Data Frame
- Lecture 9 : Introduction to Exploratory Data Analysis - Objectives, Steps and Basic Definitions
- Lecture 10 : Introduction to Exploratory Data Analysis - Variables and Type of Data
- Lecture 11 : Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Attribute and Discrete Data
- Lecture 12 : Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Continuous Data
- Lecture 13 : Frequency Distribution : Frequency Distribution with R Package
- Lecture 14 : Univariate Graphics and Plots - Bar Diagrams
- Lecture 15 : Univariate Graphics and Plots - Subdivided Bar Plots and Pie Diagrams
- Lecture 16 : Univariate Graphics and Plots - 3D Pie Diagram and Tree Map
- Lecture 17 : Univariate Graphics and Plots - Histogram, Kernel Density and Stem - Leaf Plots
- Lecture 18 : Univariate Graphics and Plots: Graphics with ggplot2 package
- Lecture 19 : Univariate Graphics and Plots: Creating Graphics with ggplot2 package
- Lecture 20 : Univariate Graphics and Plots - Bar Diagram and Tree Map with ggplot2
- Lecture 21 : Univariate Graphics and Plots - Grouped Bar and Scatter Diagrams with ggplot2 Package
- Lecture 22 : Univariate Graphics and Plots - Histogram, Dot Chart and Kernel Density Plots with ggplot2
- Lecture 23 : Central Tendency of Data - Arithmetic Mean
- Lecture 24 : Central Tendency of Data - Median
- Lecture 25 : Central Tendency of Data - Quantiles
- Lecture 26 : Central Tendency of Data - Mode
- Lecture 27 : Central Tendency of Data - Geometric Mean and Harmonic Mean
- Lecture 28 : Variation in Data - Range, Interquartile Range and Quartile Deviation
- Lecture 29 : Variation in Data - Absolute Deviation and Absolute Mean Deviation
- Lecture 30 : Variation in Data - Mean Squared Error, Variance and Standard Deviation
- Lecture 31 : Variation in Data - Computation of Variance and Standard Error with R
- Lecture 32 : Variation in Data - Coefficient of Variation and Summary
- Lecture 33 : Variation in Data - Box Plots and Violin Plots
- Lecture 34 : Moments - Raw, Central and Absolute Moments
- Lecture 35 : Moments - Computation of Moments in R
- Lecture 36 : Moments - Skewness and Kurtosis
- Lecture 37 : Scaling of Data - Centering, Scaling and Z- Scores
- Lecture 38 : Association of Variables - Scatter and Smooth Scatter Plots
- Lecture 39 : Association of Variables - Line Chart, Time Series Plot and Bubble Chart
- Lecture 40 : Association of Variables - Quantile- Quantile Plot
- Lecture 41 : Association of Variables - Three Dimensional Plots, Heat Maps and Word Cloud
- Lecture 42 : Association of Variables - Correlation Coefficient
- Lecture 43 : Association of Variables - Correlation Coefficient using R Software
- Lecture 44 : Association of Variables - Rank Correlation Coefficient
- Lecture 45 : Association of Variables - Measures of Association for Discrete and Counting Variables : Bivariate Frequency and Contingency Tables
- Lecture 46 : Association of Variables: Measure of Association for Discrete and Counting Variables with R Commands: Contingency Table, Chi-Squared Statistic, Cramer`s V Statistic and Contingency Coefficient
- Lecture 47 : Modelling of Variables : Least Square Method - One Variable
- Lecture 48 : Modelling of Variables: Least Squares Method-More than One Variable and R Commands
- Lecture 49 : Handling Text Data: Basic Operations with Text and Strings
- Lecture 50 : Handling Text Data: Operations for Finding Patterns in Text Data with Pipe Operator
- Lecture 51 : Handling Text Data: Operations for Finding Patterns in Text Data without Pipe Operator
- Lecture 52 : Handling Text Data: Analysis Or Text Data - Frequency & Plots
- Lecture 53 : Selection of Sample : Simple Random Sampling
- Lecture 54 : Selection of Sample : Simple Random Sampling with R
- Lecture 55 : EDA with Multivariate Data: Sum and Mean Vectors
- Lecture 56 : EDA with Multivariate Data: Covariance and Correlation Matrices