Course Content
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Module 1 Foundations of Date Science: Data Visualization and Interpretation
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Module 2 Data Wrangling and Data Imputation
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Module 3 Predictive Analytics: Supervised Learning Algorithms
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Module 4 Advanced Analytics
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Module 5
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Module 6 Machine Learning Algorithms/AI
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Module 7
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Module 8
Part – 11
Part-11 Logistic Regression Analysis
- Logistic Regression
- Discriminate Regression Analysis
- Multiple Discriminant Analysis
- Stepwise Discriminant Analysis
- Logit function
- Test of Associations
- Chi-square strength of association
- Binary Regression Analysis
- Profit and Logit Models
- Estimation of probability using logistic regression,
- Wald Test statistics for Model
- Hosmer Lemshow
- Nagurkake R square
- Pseudio R square
- Maximum likelihood estimation
- Model Fit
- Model cross validation
- Discrimination functions
- AIC
- BIC (Bayesian information criterion)
- Kappa Statistics
- AIC
- BIC
- Error/ Confusion matrices
- ROC
- APE
- MAPE
- Lift Curve
- Sensitivity
- Misclassification Rating
- Specificity
- Maximum Absolute Error
- Recall
- Miss classification
- Root Final Prediction Error
- Gini Coefficient
- Schwarz’s Bayesian Criterion