Data Mining and Predictive Analytics

1. Data Preparation
2. Data Preprocessing
3. Exploratory Data Analysis
4. Dimension Reduction Methods
5. Univariate Statistical Analysis
6. Multivariate Statistics
7. Preparing to Model the Data
8. Simple Linear Regression
9. Multiple Regression and Model Building
10. K-Nearest Neighbor Algorithm
11. Decision Trees
12. Neural Networks
13. Logistic Regression
14. Naive Bayes and Bayesian Networks
15. Model Evaluation Techniques
16. Cost Benefit Analysis using Data Driven Costs
17. Cost Benefit Analysis for Trinary and k-Nary Classification Models
18. Graphics Evaluation of Classification Models
19. Hierarchical and k-Means Clustering
20. Kohonen Networks
21. Birch Clustering
22. Measuring Cluster Goodness
23. Association Rules
24.Segmentation Models
25.Ensemble Methods : Bagging and Boosting
26. Model Voting and Propensity Averaging
27. Genetic Algorithms
28. Imputation of Missing Data
29. Case Study Part-1 : Business Understanding data Preparation and Eda
30. Case study part 2 : Clustering and Principles Components Analysis
31. Case Study Part 3 : Modeling and Evaluation for Performance and Interpretability

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