Teaching

I teach graduate (Masters and PhDs) and undergraduate students to academic and business audiences. I cover subjects such as

• (Business) Statistics
• Statistical learning
• Financial data analysis
• Predictive analytics
• Decision making with marketing data
• Choice modelling
• Regression analysis
• Econometrics

I use DataCamp and my own GitHub repositories to teach statistical methods with Software.

Time-Series Analysis:

This course provides students with a comprehensive understanding of time-series analysis. The course explores methods to analyze time-dependent data, make informed predictions, and understand temporal dynamics in economic and business contexts. It combines theoretical foundations and practical applications to equip students with the skills necessary to conduct rigorous empirical investigation and interpret data with a time dimension. The course covers topics such as stationarity, smoothing, autocorrelation, ARMA, forecasting, spectral analysis, volatility modeling, and more. Special emphasis is given to their application in the domains of business and economics.

The repository timeseriesanalysis contains the Jupyter Notebook teaching materials for Time-Series Analysis. It works with Binder, an interactive computational environment, to interact with the GitHub repository. The materials are programmed with the statistical computing language R.

Advanced Analytics:

This course aims to equip students with a strong foundation in statistical concepts and regression analysis while emphasizing their practical application in business and economics. The course looks at classic problems for inference such as the simultaneous causality bias and explore instrumental variable estimation as a solution. It gives them a strong foundation in using data and analytics to make informed decisions. The course also teaches choice modelling to help predict what drives people’s choices. These models, such as decision trees and also multinomial choice modes, can be used to guide decision making. To take full advantage of analytics methodologies and methods, this course adopts a hands-on approach and familiarizes students with the language of data analysis, R. Students will gain proficiency in utilizing these software tools to visualize, analyze, and interpret data from practical exercises and assignments.

The repository advancedanalytics contains the Jupyter Notebook teaching materials for Advanced Analytics. It works with Binder, an interactive computational environment, to interact with the GitHub repository. The materials are programmed with the statistical computing language R.