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Beschreibung
Learn statistical analysis, data visualization, and how to train machine learning algorithms in Python
Learning Goals:
- Understanding the difference between data science, machine learning, and artificial intelligence
- How to calculate descriptive statistics and confidence intervals and prepare them graphically
- How to work in VSCode, Python and a coding agent with version control in a team on an analysis project
- How to perform linear regressions and use imputation methods for missing values
- How to train simple neural networks with Python and TensorFlow and use them for predictions
What You Get
You'll receive a practical introduction to data science and machine learning using VSCode, Python, and a coding agent. You will work in a team on an analysis project and practically implement the various steps relevant to data science or machine learning projects using current tools and libraries.
All required software and online course content is free. You will need your own laptop and a coding agent: we strongly recommend a Claude Pro subscription for Claude Code (about $20 per month); alternatively, the free plans of Codex or Cursor can be used.
This course is part of the opencampus.sh Machine Learning Degree program. Participants in the Machine Learning Degree program receive preferential access to this course. More information about the degree program can be found here.
What You Should Bring
BEFORE the course begins, you should complete the preparation described in the course handbook here (about 3.5 hours: setting up your laptop and watching short introductory videos).
No programming experience is required, since the code is written with the help of a coding agent. Optional videos are provided for participants who want to learn to program themselves. Please plan for about 10 hours of effort per week, including the sessions. Once teams are formed and analysis projects begin (from the 3rd or 4th session), additional time should be planned to work on the project.
To receive a Certificate of Achievement (or ECTS) for this course, active participation is expected, you may not miss more than two course sessions, and you must complete a practical project in a team of two to four people. At the end of the course, the project must be presented and submitted on EduHub, including a well-documented public GitHub repository..
Certificates of attendance are not issued for this course!
Termine
Dienstag, 03.11.2026
15:00 - 16:45
Dienstag, 10.11.2026
15:00 - 16:45
Dienstag, 17.11.2026
15:00 - 16:45
Dienstag, 24.11.2026
15:00 - 16:45
Dienstag, 01.12.2026
15:00 - 16:45
Dienstag, 08.12.2026
15:00 - 16:45
Dienstag, 15.12.2026
15:00 - 16:45
Dienstag, 22.12.2026
15:00 - 16:45
Dienstag, 05.01.2027
15:00 - 16:45
Dienstag, 12.01.2027
15:00 - 16:45
Dienstag, 19.01.2027
15:00 - 18:00
Online
Buchungszeitraum:
01.09.2026
-
23.10.2026
Dienstag, 03.11.2026
15:00 - 16:45
Dienstag, 10.11.2026
15:00 - 16:45
Dienstag, 17.11.2026
15:00 - 16:45
Dienstag, 24.11.2026
15:00 - 16:45
Dienstag, 01.12.2026
15:00 - 16:45
Dienstag, 08.12.2026
15:00 - 16:45
Dienstag, 15.12.2026
15:00 - 16:45
Dienstag, 22.12.2026
15:00 - 16:45
Dienstag, 05.01.2027
15:00 - 16:45
Dienstag, 12.01.2027
15:00 - 16:45
Dienstag, 19.01.2027
15:00 - 18:00
Buchungszeitraum:
01.09.2026
-
23.10.2026