Introduction to Python – University of Copenhagen

Data Science Lab > Courses & workshops > Workshops > Introduction to Python

Workshops and small courses

Introduction to Python

The workshop will provide an introduction to the dominant programming language in data science, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open source libraries available. 

The workshop will introduce the basic programming constructs in Python and then provide data science examples, including data import, visualization, and analysis. We will introduce the integrated development interfaces Spyder and Jypiter. We may use the Tensorflow Deep Learning framework as an example of an active open source framework. 

The course is aimed at researchers at SCIENCE (incl. PhD students), who need tools for data exploration, data analysis, and data visualization. Master's thesis students may register for participation and will be accepted if space permits. 

When and where?

  • The last course took place January 30-31 2018. The next course is planned for January 2019 (in the week between blocks 2 and 3).  
  • Both days were from 9.00 to 16.00 with lunch break at 12.00.
  • The course was in room 4-0-24 at the Nørre Campus Biocenter (Ole Maaløesvej 5, 2200 Copenhagen).
  • The course will have one day of planned introduction lectures and exercises.
    The second day is primarily for further hands-on experience under guidance. 

Who can participate?

  • The course is intended for people with some programming experience (e.g. up to 100 lines of code), but no or little prior knowledge of Python
  • The course is open for PhD students and employees at SCIENCE
  • The course is only open for students at Bachelor or Master level at SCIENCE if space permits
  • The number of participants is limited to 55

What is going to happen?

  • Lecture-type introductions and overviews of basic functionalities
  • Hands-on exercises 
  • You are welcome to bring your own data (as xlsx or csv files, say) and work with those, particularly during the second day
  • The first day will consist of presentations as well as exercises. The second day is a follow-up day where you can continue working under guidance, but there will be no presentations

Which topics?

  • Introduction to Python, Spyder, and Jypiter
  • Basic programming constructs (variables, objects, conditionals, loops, ...)
  • Import of data and data manipulation
  • Visualization
  • Machine Learning libraries
  • Data Science libraries
  • Possibly work with own data

What to do before the course?

Anaconda and the Python/Spyder/Jupyter packages are avaliable for Windows, Mac and Linux. 

How to register?


  • If you have questions, then you are most welcome to contact Erik Dam from the Data Science Lab (

Course Material

Below you find material related to the course. You may want to download and/or print the exercise files before the course, to make the workflow easier. 

  • Python documentation, tutorials, beginner's guide, numerous books/videos: 
  • The Python Data Science Handbook:
  • Presentations, Exercises, and Solutions are availabel to students who followed the course upon request.

Other relevant links:

  • The Google Colaboratory Project:
    his allows you to co-develop in Jupyter notebooks that are then executed on Googles servers (including GPU support). You can upload data via Google Drive.