The project contains a lecture course “Data Mining in Business Analytics”, materials of workshops and code. It is devoted to algorithms of data analysis and their applications. It includes practical examples from business and financial sector. The compete set of materials are here. Use “save link as” if your browser does not open these PDFs.

- Course syllabus
- Introduction to Data Analysis
- Written exam: the white sheet and the full draft version
- Examples of Data Mining problems (MIT Open courseware)
- Introduction, quantitative modelling
- Examples of Data Mining problems (MIT Open courseware)
- MSExcell Linear regression and Prices forecasting
- MSExcell k-NN Forecasting example
- Google spreadsheet commands
- DMBA data repository
- Indicators and decision making part1 and part2
- Pairwise comparison
- Kemeny-Young method (wiki)
- Pareto optimal front part 1 and part 2
- Risk analysis and banking scoring and risk analysis flowchart
- 1) Weight of evidence 2) Cohort analysis 3) Stability report 4) Scorecard planning (SAS)
- Logistic regression calculator (Statpages)
- Atoms for Scilab
- Data preparation and General statistics (Uni.-Princeton )
- Sociological data processing
- Verification of scoring models
- Classification and decision trees
- Forecasting of goods consumption
- Nonparametric regression
- Forecasting of energy consumption/stock option price
- The next day energy consumption forecasting
- Management and standards, CRISP-DM
- IDEF0 diagrams
- Show1, Show2, and Memos
- Resume