Lernergebnisse:

1. Apply mathematical transformations to derive new economic indicators.

2. Normalize and scale raw data to improve model performance.

3. Utilize statistical techniques to generate derivative features from collected data.

Lernziele:

1. Extract and analyze micro-level data such as pricing trends, consumer behavior, and competitive dynamics from credible and real-time sources.

2. Apply comparative analysis and regression techniques using industry-standard tools (Excel, R) to quantify supply and demand factors.

3. Implement frameworks like SWOT analysis and Porter’s Five Forces to conduct thorough competitive evaluations and scenario assessments.

4. Integrate real-world case studies and practical examples to translate microeconomic insights into effective market recommendations.

Module

Lernziele:

1. Combine diverse datasets from macro and micro sources using tools such as Excel, R, and Bloomberg to create comprehensive economic models.

2. Implement time-series analysis and scenario planning techniques to simulate and predict market dynamics.

3. Validate integrated models with cross-validation techniques, sensitivity analysis, and thorough documentation to ensure accuracy.

4. Demonstrate practical data integration, model calibration, and interpretation of outcomes to support real-world economic decision-making.

Module

Lernziele:

1. Identify economic cycle phases and key market signals using technical analysis methods and cycle theory with practical tools.

2. Apply both fundamental and technical analysis methodologies, integrating real-time data and case studies, to evaluate investment scenarios.

3. Utilize simulation tools and risk assessment frameworks such as Value at Risk (VaR) and Sharpe Ratio to analyze and quantify risk-return trade-offs.

4. Develop and test scenario-based investment strategies incorporating dynamic market indicators and economic sentiment analysis.

Module

Lernziele:

1. Aggregate macroeconomic and microeconomic insights to build detailed, multi-scenario economic forecasts using tools such as Tableau and Power BI.

2. Implement advanced forecasting techniques including Monte Carlo simulations, scenario analysis, and regression modeling to predict market trends.

3. Develop practical, scenario-based strategic recommendations by translating technical data analysis into actionable investment strategies.

4. Present findings through practical data visualizations and comprehensive technical reports that clearly communicate forecast outcomes.

Module

Lernziele:

1. Apply rigorous data cleaning, statistical analysis, and validation techniques using platforms like Excel and R to enhance data reliability.

2. Utilize advanced visualization tools (e.g., Tableau, Power BI) to accurately and ethically communicate economic trends and analysis outcomes.

3. Conduct systematic evaluations to identify and mitigate biases in data interpretation through peer reviews and practical scenario testing.

Module