Essentials of Data Science With R Software _ 1: Probability and Statistical Inference TANAZ0P2NK

URI	`m2://res/TANAZ0P2NK`
Kind	course
Creators	Shalabh (instructor)
Date	2021-02-12
MSC	60-XX
Scope	in
Ids	course 05pjsgx75:111104146

Expressions

e1: edition

Language	en
Date	2021-02-12

m1: page, IIT Kanpur (record). nptel 111104146
m2: page, 2021-02-12 (record). nptel 111104146

Contents

part 1: Week 1
session 1: Lecture 01: Data Science- Why, What, and How?
session 2: Lecture 02: Installation and Working with R
session 3: Lecture 03: Installation and Working with R Studio
session 4: Lecture 04: Calculations with R as a Calculator
session 5: Lecture 05: Calculations with Data Vectors
session 6: Lecture 06: Built-in Commands and Bivariate Plots
session 7: Lecture 07: Logical Operators and Selection of Sample
part 2: Week 2
session 8: Lecture 8: Introduction to Probability
session 9: Lecture 9: Sample Space and Events
session 10: Lecture 10: Set Theory and Events using Venn Diagrams
session 11: Lecture 11: Relative Frequency and Probability
session 12: Lecture 12: Probability and Relative Frequency - An Example
session 13: Lecture 13: Axiomatic Definition of Probability
part 3: Week 3
session 14: Lecture 14: Some Rules of Probability
session 15: Lecture 15: Basic Principles of Counting- Ordered Set, Unordered Set, and Permutations
session 16: Lecture 16: Basic Principles of Counting- Combination
session 17: Lecture 17: Conditional Probability
session 18: Lecture 18: Multiplication Theorem of Probability
session 19: Lecture 19: Bayes' Theorem
part 4: Week 4
session 20: Lecture 20: Independent Events
session 21: Lecture 21: Computation of Probability using R
session 22: Lecture 22: Random Variables - Discrete and Continuous
session 23: Lecture 23: Cumulative Distribution and Probability Density Function
session 24: Lecture 24: Discrete Random Variables, Probability Mass Function and Cumulative Distribution Function
session 25: Lecture 25: Expectation of Variables
part 5: Week 5
session 26: Lecture 26: Moments and Variance
session 27: Lecture 27: Data Based Moments and Variance in R Software
session 28: Lecture 28: Skewness and Kurtosis
session 29: Lecture 29: Quantiles and Tschebyschev's Inequality
session 30: Lecture 30: Degenerate and Discrete Uniform Distributions
session 31: Lecture 31: Discrete Uniform Distribution in R
part 6: Week 6
session 32: Lecture 32: Bernoulli and Binomial Distribution
session 33: Lecture 33: Binomial Distribution in R
session 34: Lecture 34: Poisson Distribution
session 35: Lecture 35: Poisson Distribution in R
session 36: Lecture 36: Geometric Distribution
session 37: Lecture 37: Geometric Distribution in R
part 7: Week 7
session 38: Lecture 38: Continuous Random Variables and Uniform Distribution
session 39: Lecture 39: Normal Distribution
session 40: Lecture 40: Normal Distribution in R
session 41: Lecture 41: Normal Distribution - More Results
session 42: Lecture 42: Exponential Distribution
part 8: Week 8
session 43: Lecture 43: Bivariate Probability Distribution for Discrete Random Variables
session 44: Lecture 44: Bivariate Probability Distribution in R Software
session 45: Lecture 45: Bivariate Probability Distribution for Continuous Random Variables
session 46: Lecture 46: Examples in Bivariate Probability Distribution Functions
session 47: Lecture 47: Covariance and Correlation
session 48: Lecture 48: Covariance and Correlation - Examples and R Software
part 9: Week 9
session 49: Lecture 49: Bivariate Normal Distribution
session 50: Lecture 50: Chi square Distribution
session 51: Lecture 51: t - Distribution
session 52: Lecture 52: F - Distribution
session 53: Lecture 53: Distribution of Sample Mean, Convergence in Probability and Weak Law of Large Numbers
part 10: Week 10
session 54: Lecture 54: Central Limit Theorem
session 55: Lecture 55: Needs for Drawing Statistical Inferences
session 56: Lecture 56: Unbiased Estimators
session 57: Lecture 57: Efficiency of Estimators
part 11: Week 11
session 59: Lecture 59: Consistency and Sufficiency of Estimators
session 60: Lecture 60: Method of Moments
session 61: Lecture 61: Method of Maximum Likelihood and Rao Blackwell Theorem
session 62: Lecture 62: Basic Concepts of Confidence Interval Estimation
session 63: Lecture 63: Confidence Interval for Mean in One Sample with Known Variance
session 64: Lecture 64: Confidence Interval for Mean and Variance
part 12: Week 12
session 65: Lecture 65: Basics of Tests of Hypothesis and Decision Rules
session 66: Lecture 66: Test Procedures for One Sample Test for Mean with Known Variance
session 67: Lecture 67: One Sample Test for Mean with Unknown Variance
session 68: Lecture 68: Two Sample Test for Mean with Known and Unknown Variances
session 69: Lecture 69: Test of Hypothesis for Variance in One and Two Samples
part 13: Live Session
session 70: Live Session 01-04-2021
session 71: Live Session 03-12-2022
session 72: Live Session 08-03-2024

Catalogue records

nptel: 111104146 (manifestation)
nptel_video: 111104146 (manifestation)