NOC:Data Analytics with Python


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