People Innovation Excellence


Learning Outcomes:

On successful completion of this course, students will be able to: explain the motivations, concepts, methods, and methodologies for different types of analytics; Identify the fundamental tools and methods of data analysis and statistics for descriptive analytics; Practice several approaches for predictive analytics including: trendlines and regression analysis, forecasting, introductory data mining techniques, building and analyzing models on spreadsheets, and simulati; Practice several approaches for prescriptive analytics including: linear, integer, and nonlinear optimization models and applications.


  1. Introduction to Business Analytics;
  2. Database Analytics;
  3. Descriptive Statistics;
  4. Probability Distributions and Data Modeling;
  5. Statistical Inference;
  6. Trendlines and Regression Analysis;
  7. Forecasting Techniques;
  8. Spreadsheet Modeling and Analysis;
  9. Simulation and Risk Analysis;
  10. Linear Optimization;
  11. Integer and Nonlinear Optimization;
  12. Optimization Analytics;
  13. Decision Analysis;
  14. Connect, Customize, Organize Data and Create Filters;
  15. Build Common Views 1;
  16. Build Common Views 2;
  17. Calculated Fields and Table Calculations 1;
  18. Apply Analytics 1;
  19. Work with Multiple Data Sources;
  20. Create, Share, and Publish Dashboards and Stories;
  21. Tableau Fundamentals Review and Data Restructuring with Filters;
  22. Managing Data Sources;
  23. Calculated Fields and Table Calculations 2;
  24. Create Level of Detail (LOD) Expressions;
  25. Apply Analytics 2;
  26. Map Geographic Data and Customizing Tableau’s Geocoding.

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