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