• Learning basic libraries such as Numpy, Pandas, Scikit-learn, Matplotlib
  • Learning and applying basic Data Science, Statistics and Machine Leanring concepts
  • Working with Data types
  • Prepare students for advanced courses (e.g., deep learning)

General Information

  • 2 weeks of full-time course and one presentation day. First week with lectures and small exercises, second week working on final assignment.
  • Course content: Python libraries for data analysis: numpy, pandas, scikit-learn, statsmodels, matplotlib, seaborn, applied health data analysis
  • Weekly Hours: 1st week: 3h lectures in the morning, 3h coding exercises in the afternoon, second week practical health data analysis in a team.
  • Second week: Working on assignment and result presentation with Q&A (graded)
  • Credits: 3
  • Graded: yes
  • Date: Monday 15.03.2021 - Friday, 26.03.2021
  • Teaching Form: Digital Hands-on seminar
  • Course Language: English
  • Location: Online (Zoom)
  • Participant limit: 35 participants