2026-10-15, 2026-10-16, 2026-10-19 Crash-course in Data Science with Python (09:00-17:00)
Reiter
Crash-course in Data Science with Python
This course offers an introduction to Python for data science for people with no previous experience with Python or programming in general. To goal of the course is to acquire the necessary basic knowledge to allow you to then follow more advanced courses on specialized topics such as compuer vision, ML, NLP etc. The basic topics are:
- Installations: get all necessary software installed on your machine (or remote machiens) to use Python and scientific Python packages. Learn the basics of using a terminal
- Running code: learn about different solutions to write and run code (Jupyter, VSCode) on your personal machine or using remote services (university cluster, Google Colab etc.)
- Python basics: learn about essentials of Python (variables, importing packages, data structures, control flow etc.) This content is not exhaustive and covers only a subset of base Python functionalities needed for scientific applications.
- Scientific computing: the major part of the course will cover a series of packages (Numpy, Pandas, Seaborn) allowing you to handle numerical and tabular data and to plot information. These foundational packages (or similar data structures) are widely used as basis for other domain specific packages.
- Using AI assistants: learn how to integrate coding assistants in your work to help you develop analysis pipelines in a controlled way.
Course Objectives
- Be able to run Python code using different tools
- Knowing how to create computing environments with necessary packages installed
- Be familiar with foundational scientific computing Python packages (numpy, pandas) and their data structures (arrays, dataframes)
- Be able to do some basic plots (scatter plots, histograms etc.)
- Have an understanding how to make the best use of coding assistants
Target group
- All UniBE members, but in particular PhD students and postodcs who need to develop analysis pipelines
Prerequisites
- Participants must bring their own laptops
- No previous knowledge in Python is necessary. Exposure to any other programming language or programming concepts (variables, loops) is of benefit but not strictly required.
Methods
The course will alternate between presentation of topics (code, mathematics) and practical programming exercises.
Course material
Github repository: https://github.com/guiwitz/DAVPy
Certificate
- A certificate will be delivered to participants who have attended the whole training.
Coaches
- Guillaume Witz is a research software engineer at the DSL of the University of Bern, specialised in bioimage analysis.
Time : 2026-10-15, 2026-10-16, 2026-10-19 09:00-17:00
Location : Room 116, Muesmattstrasse 27, 3012 Bern (Muesmatt Library Building)
Training language: English
Participants : Max 25
Registraion : Mandatory
The Data Science Lab is there to boost your research by supporting you solving computing challenges.
https://www.dsl.unibe.ch/