NPTEL : NOC:Data Analytics with Python (Computer Science and Engineering)

Co-ordinators : Prof. A. Ramesh


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