Tools & Languages Overview
Research data analysis requires the right tools for the job. This section covers programming languages, statistical software, visualization tools, and documentation solutions available to TUM researchers.
What you'll find here:
- Programming Languages — R, Python, C/C++, JavaScript, SQL and their research applications
- Analytics & Visualization Software — MATLAB, SPSS, Power BI, Tableau, and other GUI-based tools
- Documentation Tools — ELN, DataTagger, and other tools for documenting research
Quick Reference Table
| Tool | Type | Best Use Case | TUM Access |
|---|---|---|---|
| R | Programming Language | Statistical analysis, bioinformatics, data viz | Free, open source |
| Python | Programming Language | ML/AI, automation, data engineering | Free, open source |
| MATLAB | Scientific Computing | Engineering, simulations | TUM License |
| SPSS | Statistical Software | Social sciences, surveys | Proprietary |
| Power BI | Business Intelligence | Dashboards, reports | TUM M365 |
| Tableau | Data Visualization | Interactive exploration | Proprietary |
| eLabFTW | Electronic Lab Notebook | Experiment documentation | TUM ELN |
| TUM DataTagger | Data Annotation | Collaborative metadata | TUM DataTagger |
Reproducibility First
For research that others should be able to reproduce, programming-based approaches (R, Python) with version control are preferred over GUI-based tools. Code documents every step of your analysis.