Modules / Lectures

Module Name | Download |
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Sl.No | Chapter Name | MP4 Download |
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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 |

65 | Lecture 65: Basics of Tests of Hypothesis and Decision Rules | Download |

66 | Lecture 66: Test Procedures for One Sample Test for Mean with Known Variance | Download |

67 | Lecture 67: One Sample Test for Mean with Unknown Variance | Download |

68 | Lecture 68: Two Sample Test for Mean with Known and Unknown Variances | Download |

69 | Lecture 69: Test of Hypothesis for Variance in One and Two Samples | Download |

Sl.No | Chapter Name | English |
---|---|---|

1 | Lecture 01: Data Science- Why, What, and How? | Download Verified |

2 | Lecture 02: Installation and Working with R | Download Verified |

3 | Lecture 03: Installation and Working with R Studio | Download Verified |

4 | Lecture 04: Calculations with R as a Calculator | Download Verified |

5 | Lecture 05: Calculations with Data Vectors | Download Verified |

6 | Lecture 06: Built-in Commands and Bivariate Plots | Download To be verified |

7 | Lecture 07: Logical Operators and Selection of Sample | Download To be verified |

8 | Lecture 8: Introduction to Probability | Download To be verified |

9 | Lecture 9: Sample Space and Events | Download To be verified |

10 | Lecture 10: Set Theory and Events using Venn Diagrams | Download To be verified |

11 | Lecture 11: Relative Frequency and Probability | Download To be verified |

12 | Lecture 12: Probability and Relative Frequency - An Example | Download To be verified |

13 | Lecture 13: Axiomatic Definition of Probability | Download To be verified |

14 | Lecture 14: Some Rules of Probability | Download To be verified |

15 | Lecture 15: Basic Principles of Counting- Ordered Set, Unordered Set, and Permutations | Download To be verified |

16 | Lecture 16: Basic Principles of Counting- Combination | Download To be verified |

17 | Lecture 17: Conditional Probability | Download To be verified |

18 | Lecture 18: Multiplication Theorem of Probability | Download To be verified |

19 | Lecture 19: Bayes' Theorem | Download To be verified |

20 | Lecture 20: Independent Events | Download To be verified |

21 | Lecture 21: Computation of Probability using R | Download To be verified |

22 | Lecture 22: Random Variables - Discrete and Continuous | Download To be verified |

23 | Lecture 23: Cumulative Distribution and Probability Density Function | Download To be verified |

24 | Lecture 24: Discrete Random Variables, Probability Mass Function and Cumulative Distribution Function | Download To be verified |

25 | Lecture 25: Expectation of Variables | Download To be verified |

26 | Lecture 26: Moments and Variance | Download To be verified |

27 | Lecture 27: Data Based Moments and Variance in R Software | Download To be verified |

28 | Lecture 28: Skewness and Kurtosis | Download To be verified |

29 | Lecture 29: Quantiles and Tschebyschev’s Inequality | Download To be verified |

30 | Lecture 30: Degenerate and Discrete Uniform Distributions | Download To be verified |

31 | Lecture 31: Discrete Uniform Distribution in R | Download To be verified |

32 | Lecture 32: Bernoulli and Binomial Distribution | Download To be verified |

33 | Lecture 33: Binomial Distribution in R | Download To be verified |

34 | Lecture 34: Poisson Distribution | Download To be verified |

35 | Lecture 35: Poisson Distribution in R | Download To be verified |

36 | Lecture 36: Geometric Distribution | Download To be verified |

37 | Lecture 37: Geometric Distribution in R | Download To be verified |

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 |

65 | Lecture 65: Basics of Tests of Hypothesis and Decision Rules | PDF unavailable |

66 | Lecture 66: Test Procedures for One Sample Test for Mean with Known Variance | PDF unavailable |

67 | Lecture 67: One Sample Test for Mean with Unknown Variance | PDF unavailable |

68 | Lecture 68: Two Sample Test for Mean with Known and Unknown Variances | PDF unavailable |

69 | Lecture 69: Test of Hypothesis for Variance in One and Two Samples | 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 |