TECH6 is taught in the fifth semester, which is focused on market and business analytics. It builds on students’ knowledge and skills in mathematics (TECH1 and TECH4), statistics (TECH3), and programming (TECH2 and TECH5). Its main goal is to strengthen core competencies needed in data-driven analyses of economic activity and familiarize students with a battery of methods used for prediction and establishing causal relationships in experimental and observational data. TECH6 is also a bridge to the sixth semester on data-driven decisions for sustainable value creation and a foundation for more advanced courses in econometrics, time series analysis, and machine learning taught at the Master’s level.
Course outline:
Part 1: Introduction to R
- Solve problems from TECH3 course in R to bridge it to previous classes and knowledge of applied statistics and Python
- Use AI tools to translate Python code to R and understand the differences
Part 2: Getting data
- Data harvesting, scraping
- Building databases: experimental design to gather data
- Ethical issues with data collocation, storage, analysis, and privacy concerns
Part 3: Econometrics and causal inference
- Linear model
- Testing and inference
- Methods for causal analysis (e.g., DiD, IV,...)
Part 4: Prediction and forecasting
- Supervised and unsupervised machine learning methods
- Validating ML models
- Fallacies such as algorithmic bias
Part 5: Presenting results
- Who is the audience? Presentation style and practice
- Visualization of results: simplification without oversimplifying