Data Analytics With Python

Introduction to Data Analytics

Python Fundamentals -I

Python Fundamentals -II

Central Tendency and Dispersion - I

Central Tendency and Dispersion - II

Introduction to Probability-I

Introduction to Probability-II

Probability Distribution - I

Probability Distribution - II

Probability Distributions - III

Python Demo for Distribution

Sampling and Sampling Distribution

Distribution of Sample Means, population, and variance

Confidence interval estimation: Single population - I

Confidence Interval Estimation: Single Population - II

Hypothesis Testing- I

Hypothesis Testing- II

Hypothesis Testing-III

Errors in Hypothesis Testing

Hypothesis Testing about the Difference in Two Sample Means

Hypothesis testing : Two sample test -II

Hypothesis Testing: Two sample test - III

ANOVA- I

ANOVA- II

Post Hoc Analysis(Tukey’s test)

Randomize block design (RBD)

Two Way ANOVA

Linear Regression - I

Linear Regression - II

Linear Regression-III

Estimation, Prediction of Regression Model Residual Analysis

Estimation, Prediction of Regression Model Residual Analysis - II

MULTIPLE REGRESSION MODEL - I

MULTIPLE REGRESSION MODEL - II

Categorical variable regression

Maximum Likelihood Estimation- I

Maximum Likelihood Estimation- II

LOGISTIC REGRESSION- I

LOGISTIC REGRESSION- II

Linear Regression Model Vs Logistic Regression Model

Confusion matrix and ROC- I

Confusion matrix and ROC- II

Performance of Logistic Model-III

Regression Analysis Model Building - I

Regression Analysis Model Building - II

Chi - Square Test of Independence - I

Chi - Square Test of Independence - II

Chi-Square Goodness of Fit Test

Cluster analysis: Introduction- I

Cluster analysis: Introduction- II

Clustering analysis: Part III

Cluster analysis: Part IV

Cluster analysis: Part V

K- Means Clustering

Hierarchical method of clustering -I

Hierarchical method of clustering -II

Classification and Regression Trees (CART : I)

Measures of attribute selection

Attribute selection Measures in CART : II

Classification and Regression Trees (CART) - III