2026-02-10 ~ 2026-02-11 Introduction to Data Science with Python: Numpy/Pandas/Seaborn (09:00-17:00)
Reiter
Introduction to Data Science with Python
The three-day 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.
**Important Note regarding course structure and attendance**
Here you subscribe for only Days 2-3. You need to register additionally for day 1, "2026-02-09 Introduction to Data Science with Python: Basics", for the full course.
We recommend taking part in the three days. However you can also just take Day 1 if you are only interested in Python bascis. You can as well just take Days 2-3 and skip Day 1 if you already have experience with Python, conda, computational notebooks (Jupyter). Note that we won't provide support for setup on Days 2 and 3.
Day 1:
- Installations: you getting all necessary software installed on your machine (or remote machiens) to use Python and scientific Python packages
- Running code: learning about different solutions to write and run code (Jupyter, VSCode) on your personal machine or using remote services (university cluster, Google Colab etc.)
- Python: you will learn about basics of Python (variables, data structures, control flow etc.) This content is not exhaustive and covers only a subste of base Python functionalities needed for scientific applications
Day 2-3:
- 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.
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.)
- Understand how mathematical concepts such as distributions, vectors etc. are handled in the Python scientific eco-system-
Target group
- Students and Staff of UniBe.
Prerequisites
- Participants must bring 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/dsl-unibe-ch/Crash_Course_DataSciPy
Day 1 presentation: https://docs.google.com/presentation/d/1bw7uyuLFrUlHNGx3bcpYvaYgjL8OpwzEXG0A4jqWiLY/edit?usp=sharing
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.
- Mykhailo Vladymyrov is a research software engineer at the DSL of the University of Bern, specialised in Machine Learning.
- Roman Schwob is a bioinformatician at the DSL of the University of Bern, specialised in Computer Vision
- Matteo Boi is a research software engineer at the DSL of the University of Bern, specialized in Machine Learning
Time : 2026-02-10 09:00-17:00
Location : Note that the two days take place in different rooms! 10 February: Room 220, Mittelstrasse 43, 11 February: Room 224, Mittelstrasse 43
Online Participation:
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/