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Course10610400257 lectures

Exploratory Data Analysis for Data Science with R Software (Hindi)

Prof. Shalabh · IIT Kanpur

Start with lecture 1 At NPTEL

About

Any data analysis requires statistical tools. The course describes the basic statistical tools and related concepts used in the exploratory data analysis. The use of analytical and graphical tools in data science will be explained. Their implementation using open-source R software will be demonstrated with the related software commands along with the interpretation of the outcomes of analytical and graphical tools.

Intended audience: All UG students in Mathematics, Engineering, Management

Prerequisites: Mathematics background up to class 10 is needed. Having some preliminary knowledge will be helpful but not necessarily mandatory.

Lectures

Week 1

  1. Lecture 0 : How to Learn and Follow the Course YouTube
  2. Lecture 1 : Data Science - Why, What and How? YouTube
  3. Lecture 2 : Introduction to R Software YouTube
  4. Lecture 3 : Calculations with R Software - Basics and R as a Calculator YouTube
  5. Lecture 4 : Calculations with R Software - Calculations with Data Vectors YouTube
  6. Lecture 5 : Calculations with R Software - Built in Commands and Missing Data Handling YouTube
  7. Lecture 6 : Calculations with R Software - Operations with Matrices YouTube

Week 2

  1. Lecture 7 : Data Preparation - CSV and TXT Data Files YouTube
  2. Lecture 8 : Data Preparation - Excel Files and Data Frame YouTube
  3. Lecture 9 : Introduction to Exploratory Data Analysis - Objectives, Steps and Basic Definitions YouTube
  4. Lecture 10 : Introduction to Exploratory Data Analysis - Variables and Type of Data YouTube
  5. Lecture 11 : Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Attribute and Discrete Data YouTube
  6. Lecture 12 : Frequency Distribution - Absolute, Relative and Cumulative Frequencies of Continuous Data YouTube

Week 3

  1. Lecture 13 : Frequency Distribution : Frequency Distribution with R Package YouTube
  2. Lecture 14 : Univariate Graphics and Plots - Bar Diagrams YouTube
  3. Lecture 15 : Univariate Graphics and Plots - Subdivided Bar Plots and Pie Diagrams YouTube
  4. Lecture 16 : Univariate Graphics and Plots - 3D Pie Diagram and Tree Map YouTube
  5. Lecture 17 : Univariate Graphics and Plots - Histogram, Kernel Density and Stem - Leaf Plots YouTube

Week 4

  1. Lecture 18 : Univariate Graphics and Plots: Graphics with ggplot2 package YouTube
  2. Lecture 19 : Univariate Graphics and Plots: Creating Graphics with ggplot2 package YouTube
  3. Lecture 20 : Univariate Graphics and Plots - Bar Diagram and Tree Map with ggplot2 YouTube
  4. Lecture 21 : Univariate Graphics and Plots - Grouped Bar and Scatter Diagrams with ggplot2 Package YouTube
  5. Lecture 22 : Univariate Graphics and Plots - Histogram, Dot Chart and Kernel Density Plots with ggplot2 YouTube

Week 5

  1. Lecture 23 : Central Tendency of Data - Arithmetic Mean YouTube
  2. Lecture 24 : Central Tendency of Data - Median YouTube
  3. Lecture 25 : Central Tendency of Data - Quantiles YouTube
  4. Lecture 26 : Central Tendency of Data - Mode YouTube
  5. Lecture 27 : Central Tendency of Data - Geometric Mean and Harmonic Mean YouTube

Week 6

  1. Lecture 28 : Variation in Data - Range, Interquartile Range and Quartile Deviation YouTube
  2. Lecture 29 : Variation in Data - Absolute Deviation and Absolute Mean Deviation YouTube
  3. Lecture 30 : Variation in Data - Mean Squared Error, Variance and Standard Deviation YouTube
  4. Lecture 31 : Variation in Data - Computation of Variance and Standard Error with R YouTube

Week 7

  1. Lecture 32 : Variation in Data - Coefficient of Variation and Summary YouTube
  2. Lecture 33 : Variation in Data - Box Plots and Violin Plots YouTube
  3. Lecture 34 : Moments - Raw, Central and Absolute Moments YouTube
  4. Lecture 35 : Moments - Computation of Moments in R YouTube

Week 8

  1. Lecture 36 : Moments - Skewness and Kurtosis YouTube
  2. Lecture 37 : Scaling of Data - Centering, Scaling and Z- Scores YouTube
  3. Lecture 38 : Association of Variables - Scatter and Smooth Scatter Plots YouTube
  4. Lecture 39 : Association of Variables - Line Chart, Time Series Plot and Bubble Chart YouTube

Week 9

  1. Lecture 40 : Association of Variables - Quantile- Quantile Plot YouTube
  2. Lecture 41 : Association of Variables - Three Dimensional Plots, Heat Maps and Word Cloud YouTube
  3. Lecture 42 : Association of Variables - Correlation Coefficient YouTube
  4. Lecture 43 : Association of Variables - Correlation Coefficient using R Software YouTube

Week 10

  1. Lecture 44 : Association of Variables - Rank Correlation Coefficient YouTube
  2. Lecture 45 : Association of Variables - Measures of Association for Discrete and Counting Variables : Bivariate Frequency and Contingency Tables YouTube
  3. 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 YouTube
  4. Lecture 47 : Modelling of Variables : Least Square Method - One Variable YouTube

Week 11

  1. Lecture 48 : Modelling of Variables: Least Squares Method-More than One Variable and R Commands YouTube
  2. Lecture 49 : Handling Text Data: Basic Operations with Text and Strings YouTube
  3. Lecture 50 : Handling Text Data: Operations for Finding Patterns in Text Data with Pipe Operator YouTube
  4. Lecture 51 : Handling Text Data: Operations for Finding Patterns in Text Data without Pipe Operator YouTube

Week 12

  1. Lecture 52 : Handling Text Data: Analysis Or Text Data - Frequency & Plots YouTube
  2. Lecture 53 : Selection of Sample : Simple Random Sampling YouTube
  3. Lecture 54 : Selection of Sample : Simple Random Sampling with R YouTube
  4. Lecture 55 : EDA with Multivariate Data: Sum and Mean Vectors YouTube
  5. Lecture 56 : EDA with Multivariate Data: Covariance and Correlation Matrices YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kanpur
Department
Computer Science and Engineering
Teacher
Prof. Shalabh
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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