Lecture 1 - Introduction to data analytics
Lecture 2 - Python Fundamentals - I
Lecture 3 - Python Fundamentals - II
Lecture 4 - Central Tendency and Dispersion - I
Lecture 5 - Central Tendency and Dispersion - II
Lecture 6 - Introduction to Probability - I
Lecture 7 - Introduction to Probability - II
Lecture 8 - Probability Distributions - I
Lecture 9 - Probability Distributions - II
Lecture 10 - Probability Distributions - III
Lecture 11 - Python Demo for Distributions
Lecture 12 - Sampling and Sampling Distribution
Lecture 13 - Distribution of Sample Means, population, and variance
Lecture 14 - Confidence interval estimation: Single population - I
Lecture 15 - Confidence interval estimation: Single population - II
Lecture 16 - Hypothesis Testing - I
Lecture 17 - Hypothesis Testing - II
Lecture 18 - Hypothesis Testing - III
Lecture 19 - Errors in Hypothesis Testing
Lecture 20 - Hypothesis Testing: Two sample test - I
Lecture 21 - Hypothesis Testing: Two sample test - II
Lecture 22 - Hypothesis Testing: Two sample test - III
Lecture 23 - ANOVA - I
Lecture 24 - ANOVA - II
Lecture 25 - Post Hoc Analysis (Tukey’s test)
Lecture 26 - Randomize block design (RBD)
Lecture 27 - Two Way ANOVA
Lecture 28 - Linear Regression - I
Lecture 29 - Linear Regression - II
Lecture 30 - Linear Regression - III
Lecture 31 - Estimation, Prediction of Regression Model Residual Analysis - I
Lecture 32 - Estimation, Prediction of Regression Model Residual Analysis - II
Lecture 33 - Multiple Regression Model - I
Lecture 34 - Multiple Regression Model - II
Lecture 35 - Categorical variable regression
Lecture 36 - Maximum Likelihood Estimation - I
Lecture 37 - Maximum Likelihood Estimation - II
Lecture 38 - Logistic Regression - I
Lecture 39 - Logistic Regression - II
Lecture 40 - Linear Regression Model Vs Logistic Regression Model
Lecture 41 - Confusion matrix and ROC - I
Lecture 42 - Confusion Matrix and ROC - II
Lecture 43 - Performance of Logistic Model - III
Lecture 44 - Regression Analysis Model Building - I
Lecture 45 - Regression Analysis Model Building (Interaction) - II
Lecture 46 - Chi - Square Test of Independence - I
Lecture 47 - Chi-Square Test of Independence - II
Lecture 48 - Chi-Square Goodness of Fit Test
Lecture 49 - Cluster analysis: Introduction - Part I
Lecture 50 - Clustering analysis - Part II
Lecture 51 - Clustering analysis - Part III
Lecture 52 - Cluster analysis - Part IV
Lecture 53 - Cluster analysis - Part V
Lecture 54 - K- Means Clustering
Lecture 55 - Hierarchical method of clustering - I
Lecture 56 - Hierarchical method of clustering - II
Lecture 57 - Classification and Regression Trees (CART) - I
Lecture 58 - Measures of attribute selection
Lecture 59 - Attribute selection Measures in (CART) - II
Lecture 60 - Classification and Regression Trees (CART) - III
