DECISION ANALYTICS (2/2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – Define the concepts of decision analytics and its underlying theory in relation to a broad range of related professional areas; LO2 – Explain the concepts of decision analytics and its underlying theory in relation to a broad range of related professional areas; LO3 – Design of analytic modeling techniques and offer effective recommendations for analytic initiatives and solutions; LO4 – Analyze the implementation of analytic techniques in support of critical decision-making.

Topics:

1. Elementary Machine Learning using Rapid Miner (1);
2. Regression Analysis: (1) (Lab);
3. Project finalization;
4. Finding Relationships among Variables (lab);
5. Regression Analysis: (1);
6. Regression Analysis: (2);
7. Describing the Distribution of a Single Variable;
8. Introduction Decision Analytics;
9. Project mentoring and collection (lab);
10. Advanced Data Analysis: Data mining (2) (Lab);
11. Probabilistic Reasoning: Representation (Lab);
12. Elementary Machine Learning using Rapid Miner (2) (Lab);
13. Quiz 2; Quiz 1;
14. Advanced Data Analysis: Data mining;
15. Project presentation;
16. Project mentoring and collection (Lab) (2);
17. Probabilistic Reasoning: Representation;
18. Advanced Data Analysis: Data mining (1) (Lab);
19. Probabilistic Reasoning: Simple decision;
20. Finding Relationships among Variables;
21. Elementary Machine Learning using Rapid Miner (1) (Lab);
22. Elementary Machine Learning using Rapid Miner (2);
23. Model Uncertainty: Model Based Method (Lab);
24. Regression Analysis: (2) (Lab);
25. Model Uncertainty: Model Based Method.