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.
Course features
The course provides a basis for the parallel Geo Information Systems and Remote Sensing courses. It is intended as a blended learning module in our study program although the provided introductions, explanations and examples might be useful for self-study, too.
Syllabus
| Session | Topic | Content |
|---|---|---|
| Data basics | ||
| 01 | First things first | Data and information, R, R Studio, R markdown, GitHub, GitHub classroom |
| 02 | First things second | Working environment, data sets, data types, data structures, logical operators, control structures |
| Data exploration | ||
| 03 | Look at your data | Reading and writing (tabulated) data, visual data exploitation, descriptive statistics |
| 04 | Clean your data | Tailoring data sets, fill values and NA, aggregating, merging or sub-setting data sets |
| Data modelling | ||
| 05 | Explain your data | Linear regression modelling, confidence intervals, sample tests, variance analysis |
| 06 | Predict your data | Cross-validation |
| 07 | Select your variables | Multiple linear models, feature selection |
| 08 | Predict your non-linear data | Generalized additive models |
| 09 | Predict your temporal data | Auto-correlation, AR and ARIMA models |
| 10 | Explain your temporal data | Decomposing time series |
| Marburg Open Hackathon | ||
| 11 | MOHA session | Marked assignment |
| Visualization | ||
| 12 | Visualize your data | Publication quality graphics |
| Evaluation | ||
| 13 | Evaluation | Official course evaluation session |
| Wrap up | ||
| 14 | Wrap up | Time for questions, addressing potential 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.
Instructor
