Teaching

Course by: prof. Katarzyna Kopczewska & Maria Kubara, PhD

This course explores the application of machine learning techniques to spatial data, addressing both the challenges and opportunities of modeling spatial patterns. Students will learn advanced methods such as spatial clustering, Geographically Weighted Regression with Random Forest, Spatial Random Forest, and artificial neural networks in a spatial setting. The course emphasizes hands-on learning with R software, allowing students to implement spatial machine learning models and analyze real-world spatial datasets. Tailored to the interests and skill levels of participants, this workshop-style course provides a solid foundation for applying machine learning in regional science, urban studies, and geospatial analytics.

Course by: prof. Katarzyna Kopczewska

This course introduces students to unsupervised machine learning techniques for discovering patterns and extracting valuable insights from unlabeled data. Covering three key areas—clustering, dimension reduction, and association rule learning—students will explore algorithms such as k-means, hierarchical clustering, PCA, and Apriori, along with their applications in business and data-driven decision-making. The course balances theory with hands-on practice in R, emphasizing real-world datasets and visualization techniques. 

Course details are here.

Course by: prof. Katarzyna Kopczewska

This course provides a hands-on introduction to R, covering essential skills for data analysis, statistical modeling, and econometrics. Part 1 focuses on data preparation, cleaning, visualization, and reproducible research, ensuring students can efficiently manage and analyze datasets. Part 2 explores statistical modeling techniques, including probability distributions, inequality measures, Monte Carlo simulations, and bootstrapping. Part 3 delves into applied econometric modeling, covering regression, panel data models, propensity score matching, and causal inference techniques like Difference-in-Differences and Regression Discontinuity. Designed as a practical, project-based course, students will work with real-world data and apply advanced analytical methods, making it ideal for those in econometrics, data science, and social sciences.

Course details are here.

Course by: Maria Kubara, PhD

This course equips students with a comprehensive understanding of AI (and Large Language Models), from its fundamentals and model training to its practical applications in text generation, graphics, and data analytics. Students will develop prompt engineering skills, learning how to communicate effectively with AI models. A critical thinking module trains them to assess AI-generated content, recognize AI hallucinations, and address ethical and legal concerns. The course also explores AI’s role as a personal and professional assistant, covering academic writing, data analytics, and everyday tasks. Hands-on workshops provide real-world experience with AI tools, automation, and plug-ins, preparing students to strategically integrate AI into their workflow while maintaining originality and creativity.

Course details are here.

Course by: Maria Kubara, PhD

This course provides a hands-on introduction to R, one of the most powerful statistical programming languages used in data science, business analytics, and scientific research. Students will learn coding fundamentals in R, including data import, cleaning, visualization, and statistical analysis, with a focus on efficient workflow management in RStudio. The course also covers RMarkdown for reproducible research, data manipulation with tidyverse, and custom function development. Designed for master’s students in Econometrics, Informatics, and Data Science, this computer lab-based course equips participants with essential programming and analytical skills, preparing them for both academic research and industry applications. 

Course details are here.

Course by: Maria Kubara, PhD

This course is designed for those with prior experience in R who want to master advanced programming techniques, automation, and package development. Students will learn how to optimize R scripts for efficiency, use loops and functional programming alternatives (apply family), and implement parallel processing. The course also covers debugging, profiling, and optimizing algorithms, including the use of C++ (Rcpp package) for performance improvements. A strong focus is placed on writing reusable functions, creating custom R packages, and metaprogramming. By the end of the course, students will be able to write clean, efficient, and scalable R code, making it ideal for quantitative research, econometrics, and data science applications.

Course details are here.

Course by: Kateryna Zabarina, PhD

This course introduces students to spatial econometric techniques for analyzing point and line patterns, with applications in business location, transport economics, and real estate markets. Students will learn how to handle geo-localized data, visualize spatial patterns, and apply models such as Ripley’s K-function, point process models, and spatio-temporal analysis. The course is hands-on, using R software (prior knowledge is welcome but not required). Assessment is based on an article review and a group research project, equipping students with practical skills in spatial data analysis and modeling.

Course details are here.