NOC:Statistical Foundation for Big Data Analysis

Lecture 1 - Big Data : Definition, Examples and Challenges

Lecture 2 - Big Data Analysis vs Big Data Processing : Role of Dimensional Statistics

Lecture 3 - Statistical Framework - 1

Lecture 4 - Statistical Framework - 2

Lecture 5 - Statistical Framework - 3

Lecture 6 - Point Estimation: Unbiased Estimation

Lecture 7 - Point Estimation: Likelihood - 1

Lecture 8 - Point Estimation: Likelihood - 2

Lecture 9 - Method of Moments

Lecture 10 - Point Estimation: Asymptotics

Lecture 11 - Interval Estimation - 1

Lecture 12 - Interval Estimation - 2

Lecture 13 - Basics of Hypothesis Testing - I

Lecture 14 - Basics of Hypothesis Testing - II

Lecture 15 - Basics of Hypothesis Testing - III

Lecture 16 - Bias Variance and Mean Squared Errors

Lecture 17 - Bias Variance and Mean Squared Errors

Lecture 18 - Bias Variance and Mean Squared Errors

Lecture 19 - Bias Variance and Mean Squared Errors

Lecture 20 - Bias Variance and Mean Squared Errors

Lecture 21 - Bias Variance Decomposition

Lecture 22 - Cross Validation and Bias Variance Tradeoff

Lecture 23 - Methods of Cross Validation

Lecture 24 - Regression vs Classification

Lecture 25 - Inference on Logistic Regression

Lecture 26 - Regression: Linear Regression - 1

Lecture 27 - Regression: Linear Regression - 2

Lecture 28 - Regression: Multiple Linear Regression - 1

Lecture 29 - Regression: Multiple Linear Regression - 2

Lecture 30 - Regression: Multiple Linear Regression - 3

Lecture 31 - Generalities of Multivariate Distribution

Lecture 32 - Bivariate Normal Distribution

Lecture 33 - Multivariate Normal Distribution - 1

Lecture 34 - Multivariate Normal Distribution - 2

Lecture 35 - Multivariate Normal Distribution - 3

Lecture 36 - Supervised Learning vs Unsupervised Learning

Lecture 37 - Heirarchical Clustering

Lecture 38 - K-means clustering

Lecture 39 - Curse of Dimensionality

Lecture 40 - Behaviour of Volume in High Dimension

Lecture 41 - Introduction to Principal Component Analysis

Lecture 42 - Eigenvalues and Eigenvectors

Lecture 43 - Mathematical Formulation of Principal Component Analysis

Lecture 44 - Mathematical Foundation of Principal Component Analysis

Lecture 45 - Population Principal Component Analysis for Normal Distribution

Lecture 46 - Population vs Sample Principal Component Analysis

Lecture 47 - Mathematical Formulation of Sample PCA

Lecture 48 - Sample PCA for Covariance matrix and Correlation Matrix

Lecture 49 - Examples of Sample PCA

Lecture 50 - Inference on Principal Component Analysis

Lecture 51 - Network Data and Random Graph

Lecture 52 - Properties of Random Graph

Lecture 53 - Law of Large Numbers for Random Graph - 1

Lecture 54 - Law of Large Numbers for Random Graph - 2

Lecture 55 - Law of Large Numbers for Random Graph - 3

Lecture 56 - Introduction to Network Medicine

Lecture 57 - Selected Case Study on Network Medicine

Lecture 58 - Social Network Analysis

Lecture 59 - Simple Linear Regression on Social Network Data

Lecture 60 - Conclusion and Course Summary