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.
Syllabus 10
Week 1
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
Week 2
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
Week 3
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
Week 4
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
Week 5
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
Week 6
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
Week 7
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
Week 8
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
Week 9
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
Week 10
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, Cr…
Lecture 47 : Modelling of Variables : Least Square Method - One Variable
Advantages and disadvantages
Advantages
Taught by IIT and IISc professors, and it follows the Indian university syllabus closely.
All videos and assignments are free on NPTEL and SWAYAM.
The certificate is recognised by many Indian universities for credit transfer and by GATE aspirants.
Great for GATE and semester exam preparation.
Taught by Indian Institute of Technology Kanpur, one of the strongest names in its field.
Completely free.
Self-paced: start any time.
A clear syllabus (10 parts) you can see before you start.
Disadvantages
The certificate needs a proctored exam at a centre, which has a fee.
Recorded classroom lectures: thorough, but slower than made-for-online courses.
New runs start on fixed dates (January and July).
Learning is free, but the certificate costs money.
Some points apply to every course of this kind; see how we rank.
Free to learn
Free: Every video and assignment is free on NPTEL and SWAYAM. Enrol when the next run opens.
Certificate: Optional. It needs a proctored exam at a centre, which has a fee.
Before you start
Mathematics background up to class 10 is needed. Having some preliminary knowledge will be helpful but not necessarily mandatory.
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