Course syllabus: Data Mining in Business Analytics

The course is devoted to algorithms of intelligent data analysis and their applications. It includes numerous practical examples from the business and financial sector. Business analytics problem solutions are combined with programming tips and techniques of implementation. The course includes 14 lectures, 14 workshops, and an exam.

Course objectives:

To gain an understanding of how managers use data mining to formulate and solve problems in the business and financial sectors.
To learn principles of managerial decision-making support techniques.
To become familiar with the algorithms needed to process, analyze and report business data.
To learn how to use selected data mining software in business analytics.

Course outline:

Using data mining methods in business, an introduction
- Introduction to SciLab (workshop)
Management and standards in business analytics
- Introduction to report systems
- Customer data collection and preprocessing
- Data manipulation and visualization
Managerial decision support
- Delphi method, problems of voting, Pareto slicing
Integral indicator construction
- Making indicators for energy sector, ecology and finance
Risk analysis and credit risk scorecards
- Banking application scorecards developing
Sociological data processing
- Linear, ordinal and nominal scale conversion
Risk model verification
- Scorecards testing and implementation
Customer multilevel modeling
- Model comparison
Customer behavior analysis
- Clustering of telecomm customers
Customer engagement and collaborative filtering
- Nearest neighborhood and metric spaces
Forecasting of goods consumption
- Non-parametric regression
Forecasting of energy consumption
- Singular structure analysis
Econometrical scenario analysis
- Auto-regression analysis
Exam and case test
- Coursework discussion

Prerequisites: Knowledge of linear algebra and statistics is required.

Software: SciLab.

Grading: One-hour writing exam.