Master level course in Physical Geography at Marburg University

Data analysis is a key competence for professional geographers that requires profound knowledge in both (statistical) analysis methods and computer sciences. While the reason for the former is obvious, the latter is a direct result of a growing data deluge, induced by technological progress on both the fields of data collection and distribution.

Data analysis is based on a variety of skills related to organizing, handling, describing and understanding a diversity of datasets. By using the programming environment R, this course will not just open the door to a cosmos of data analysis functionality but will moreover provide a domain specific and flexible tool for workflow automation.

Intended learning outcomes

At the end of this course you should be able to

  • organize a variety of datasets and (intermediate) analysis results in structured fashion,
  • document your workflow in an understandable and transparent manner, collaborate in teams and handle issues and task management using Git and GitHub as software management tool and platform,
  • implement data analysis workflows using tailored R scripts along with readily available functions from third-party R packages,
  • model relationships between data variables and calculate reliable error estimates, and to
  • critically evaluate your analysis.

Setting

This course will take place in a hybrid synchronous setting in presence in room F 14 | 00A19. In addition, there will be regular meetings with a tutor. Details on the additional tutor sessions will be provided in the first regular session, which will take place on Tuesday 21.10.2025 at 9:15 am (German time) in room F 14 | 00A19. The virtual room for online participants will be announced by email and must be accesses via ILIAS. Note that the tutor sessions are voluntary.

Syllabus

The course encompasses 14 sessions from 22.10.2024 to 11.02.2024.

Session Date Topic Content
    Data basics  
01 21.10.2025 First things first Expectations. Data and information, R, R Studio, R markdown, GitHub, GitHub classroom
02 28.10.2025 First things second Working environment, data sets, data types, data structures, logical operators, control structures
    Data exploration  
03 04.11.2025 Look at your data Reading and writing (tabulated) data, visual data exploitation, descriptive statistics
04 11.11.2025 Clean your data Tailoring data sets, fill values and NA, aggregating, merging or sub-setting data sets. Feedback
    Data modelling  
05 18.11.2025 Explain your data Linear regression modelling, confidence intervals, sample tests, variance analysis
06 25.11.2025 Predict your data Cross-validation
07 02.12.2025 Select your variables Multiple linear models, feature selection
08 09.12.2025 Predict your non-linear data Generalized additive models
09 16.12.2025 Predict your temporal data Auto-correlation, AR and ARIMA models
Christmas break  
10 13.01.2026 Explain your temporal data Decomposing time series
20.01.2026 Build-in hold No course, used as buffer
    Marburg Open Hackathon  
11 27.01.2026 MOHA session Marked assignment
    Visualization  
12 03.02.2026 Visualize your data Publication quality graphics
    Wrap up  
13 10.02.2026 Wrap up Time for questions and feedback, individual data analysis problems, goodbye

Deliverables

The graded course certificate will be based on an individual portfolio hosted as a personal repository on GitHub. The individual portfolio items are defined in the respective course assignments along with the information if they will be marked or not.

Preparation and prerequisites

The course assumes basic knowledge and skills in R and geo-information science.

This course uses additionally provided material for teaching basic R skills, which can be found here.

Instructor

Dirk Zeuss

University of Marburg