Modules / Lectures
Module NameDownload


Sl.No Chapter Name MP4 Download
1Lecture 01: Data Science- Why, What, and How? Download
2Lecture 02: Installation and Working with RDownload
3Lecture 03: Installation and Working with R Studio Download
4Lecture 04: Calculations with R as a Calculator Download
5Lecture 05: Calculations with Data Vectors Download
6Lecture 06: Built-in Commands and Bivariate Plots Download
7Lecture 07: Logical Operators and Selection of Sample Download
8Lecture 8: Introduction to Probability Download
9Lecture 9: Sample Space and Events Download
10Lecture 10: Set Theory and Events using Venn Diagrams Download
11Lecture 11: Relative Frequency and Probability Download
12Lecture 12: Probability and Relative Frequency - An Example Download
13Lecture 13: Axiomatic Definition of Probability Download
14Lecture 14: Some Rules of Probability Download
15Lecture 15: Basic Principles of Counting- Ordered Set, Unordered Set, and Permutations Download
16Lecture 16: Basic Principles of Counting- Combination Download
17Lecture 17: Conditional Probability Download
18Lecture 18: Multiplication Theorem of Probability Download
19Lecture 19: Bayes' Theorem Download
20Lecture 20: Independent Events Download
21Lecture 21: Computation of Probability using R Download
22Lecture 22: Random Variables - Discrete and Continuous Download
23Lecture 23: Cumulative Distribution and Probability Density Function Download
24Lecture 24: Discrete Random Variables, Probability Mass Function and Cumulative Distribution Function Download
25Lecture 25: Expectation of Variables Download
26Lecture 26: Moments and Variance Download
27Lecture 27: Data Based Moments and Variance in R Software Download
28Lecture 28: Skewness and Kurtosis Download
29Lecture 29: Quantiles and Tschebyschev’s Inequality Download
30Lecture 30: Degenerate and Discrete Uniform Distributions Download
31Lecture 31: Discrete Uniform Distribution in R Download
32Lecture 32: Bernoulli and Binomial Distribution Download
33Lecture 33: Binomial Distribution in R Download
34Lecture 34: Poisson Distribution Download
35Lecture 35: Poisson Distribution in R Download
36Lecture 36: Geometric Distribution Download
37Lecture 37: Geometric Distribution in R Download
38Lecture 38: Continuous Random Variables and Uniform Distribution Download
39Lecture 39: Normal Distribution Download
40Lecture 40: Normal Distribution in R Download
41Lecture 41: Normal Distribution – More Results Download
42Lecture 42: Exponential Distribution Download
43Lecture 43: Bivariate Probability Distribution for Discrete Random Variables Download
44Lecture 44: Bivariate Probability Distribution in R Software Download
45Lecture 45: Bivariate Probability Distribution for Continuous Random Variables Download
46Lecture 46: Examples in Bivariate Probability Distribution Functions Download
47Lecture 47: Covariance and Correlation Download
48Lecture 48: Covariance and Correlation ‐ Examples and R Software Download
49Lecture 49: Bivariate Normal Distribution Download
50Lecture 50: Chi square Distribution Download
51Lecture 51: t - Distribution Download
52Lecture 52: F - Distribution Download
53Lecture 53: Distribution of Sample Mean, Convergence in Probability and Weak Law of Large Numbers Download
54Lecture 54: Central Limit Theorem Download
55Lecture 55: Needs for Drawing Statistical Inferences Download
56Lecture 56: Unbiased Estimators Download
57Lecture 57: Efficiency of Estimators Download
58Lecture 58: Cramér–Rao Lower Bound and Efficiency of Estimators Download
59Lecture 59: Consistency and Sufficiency of EstimatorsDownload
60Lecture 60: Method of MomentsDownload
61Lecture 61: Method of Maximum Likelihood and Rao Blackwell TheoremDownload
62Lecture 62: Basic Concepts of Confidence Interval EstimationDownload
63Lecture 63: Confidence Interval for Mean in One Sample with Known VarianceDownload
64Lecture 64: Confidence Interval for Mean and VarianceDownload
65Lecture 65: Basics of Tests of Hypothesis and Decision RulesDownload
66Lecture 66: Test Procedures for One Sample Test for Mean with Known VarianceDownload
67Lecture 67: One Sample Test for Mean with Unknown VarianceDownload
68Lecture 68: Two Sample Test for Mean with Known and Unknown VariancesDownload
69Lecture 69: Test of Hypothesis for Variance in One and Two SamplesDownload

Sl.No Chapter Name English
1Lecture 01: Data Science- Why, What, and How? Download
Verified
2Lecture 02: Installation and Working with RDownload
Verified
3Lecture 03: Installation and Working with R Studio Download
Verified
4Lecture 04: Calculations with R as a Calculator Download
Verified
5Lecture 05: Calculations with Data Vectors Download
Verified
6Lecture 06: Built-in Commands and Bivariate Plots Download
To be verified
7Lecture 07: Logical Operators and Selection of Sample Download
To be verified
8Lecture 8: Introduction to Probability Download
To be verified
9Lecture 9: Sample Space and Events Download
To be verified
10Lecture 10: Set Theory and Events using Venn Diagrams Download
To be verified
11Lecture 11: Relative Frequency and Probability Download
To be verified
12Lecture 12: Probability and Relative Frequency - An Example Download
To be verified
13Lecture 13: Axiomatic Definition of Probability Download
To be verified
14Lecture 14: Some Rules of Probability Download
To be verified
15Lecture 15: Basic Principles of Counting- Ordered Set, Unordered Set, and Permutations Download
To be verified
16Lecture 16: Basic Principles of Counting- Combination Download
To be verified
17Lecture 17: Conditional Probability Download
To be verified
18Lecture 18: Multiplication Theorem of Probability Download
To be verified
19Lecture 19: Bayes' Theorem Download
To be verified
20Lecture 20: Independent Events Download
To be verified
21Lecture 21: Computation of Probability using R Download
To be verified
22Lecture 22: Random Variables - Discrete and Continuous Download
To be verified
23Lecture 23: Cumulative Distribution and Probability Density Function Download
To be verified
24Lecture 24: Discrete Random Variables, Probability Mass Function and Cumulative Distribution Function Download
To be verified
25Lecture 25: Expectation of Variables Download
To be verified
26Lecture 26: Moments and Variance Download
To be verified
27Lecture 27: Data Based Moments and Variance in R Software Download
To be verified
28Lecture 28: Skewness and Kurtosis Download
To be verified
29Lecture 29: Quantiles and Tschebyschev’s Inequality Download
To be verified
30Lecture 30: Degenerate and Discrete Uniform Distributions Download
To be verified
31Lecture 31: Discrete Uniform Distribution in R Download
To be verified
32Lecture 32: Bernoulli and Binomial Distribution Download
To be verified
33Lecture 33: Binomial Distribution in R Download
To be verified
34Lecture 34: Poisson Distribution Download
To be verified
35Lecture 35: Poisson Distribution in R Download
To be verified
36Lecture 36: Geometric Distribution Download
To be verified
37Lecture 37: Geometric Distribution in R Download
To be verified
38Lecture 38: Continuous Random Variables and Uniform Distribution PDF unavailable
39Lecture 39: Normal Distribution PDF unavailable
40Lecture 40: Normal Distribution in R PDF unavailable
41Lecture 41: Normal Distribution – More Results PDF unavailable
42Lecture 42: Exponential Distribution PDF unavailable
43Lecture 43: Bivariate Probability Distribution for Discrete Random Variables PDF unavailable
44Lecture 44: Bivariate Probability Distribution in R Software PDF unavailable
45Lecture 45: Bivariate Probability Distribution for Continuous Random Variables PDF unavailable
46Lecture 46: Examples in Bivariate Probability Distribution Functions PDF unavailable
47Lecture 47: Covariance and Correlation PDF unavailable
48Lecture 48: Covariance and Correlation ‐ Examples and R Software PDF unavailable
49Lecture 49: Bivariate Normal Distribution PDF unavailable
50Lecture 50: Chi square Distribution PDF unavailable
51Lecture 51: t - Distribution PDF unavailable
52Lecture 52: F - Distribution PDF unavailable
53Lecture 53: Distribution of Sample Mean, Convergence in Probability and Weak Law of Large Numbers PDF unavailable
54Lecture 54: Central Limit Theorem PDF unavailable
55Lecture 55: Needs for Drawing Statistical Inferences PDF unavailable
56Lecture 56: Unbiased Estimators PDF unavailable
57Lecture 57: Efficiency of Estimators PDF unavailable
58Lecture 58: Cramér–Rao Lower Bound and Efficiency of Estimators PDF unavailable
59Lecture 59: Consistency and Sufficiency of EstimatorsPDF unavailable
60Lecture 60: Method of MomentsPDF unavailable
61Lecture 61: Method of Maximum Likelihood and Rao Blackwell TheoremPDF unavailable
62Lecture 62: Basic Concepts of Confidence Interval EstimationPDF unavailable
63Lecture 63: Confidence Interval for Mean in One Sample with Known VariancePDF unavailable
64Lecture 64: Confidence Interval for Mean and VariancePDF unavailable
65Lecture 65: Basics of Tests of Hypothesis and Decision RulesPDF unavailable
66Lecture 66: Test Procedures for One Sample Test for Mean with Known VariancePDF unavailable
67Lecture 67: One Sample Test for Mean with Unknown VariancePDF unavailable
68Lecture 68: Two Sample Test for Mean with Known and Unknown VariancesPDF unavailable
69Lecture 69: Test of Hypothesis for Variance in One and Two SamplesPDF unavailable


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