Introduction Data Science with Python for Digital Health (Wintersemester 2022/2023)

Lecturer: Prof. Dr. Christoph Lippert (Digital Health - Machine Learning) , Wei-Cheng Lai (Digital Health - Machine Learning) , Jana Fehr (Digital Health - Machine Learning) , Noel Danz (Digital Health - Machine Learning) , Hadya Yassin (Digital Health - Machine Learning)

General Information

• Weekly Hours: 2
• Credits: 3
• Graded: yes
• Enrolment Deadline: 01.10.2022 - 31.10.2022
• Examination time §9 (4) BAMA-O: 31.03.2023
• Teaching Form: compact course V / Exercise
• Enrolment Type: Compulsory Elective Module
• Course Language: English
• Maximum number of participants: 35

Programs, Module Groups & Modules

Description

General Information

• Basic introduction into Datascience using Python
• 2 weeks of full-time course and one presentation day.
• First week with lectures in the morning and hands-on exercises in the afternoon
• Second week comprises working on final assignment in groups and presenting results on the last course day (Friday)
• 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.
• Credits: 3
• Graded: yes
• Date: Lecture week from 20th - 24th of March 2023, Project week from March 27th to 31st. Final presentation day of project results on 31st of March 2023
• Teaching Form: Digital Hands-on seminar
• Course Language: English
• Location: Campus III (seminar room & pool room)
• Participant limit: 35 participants

Description

• 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)

Course Structure

• First week
  • Morning lecture
  • Afternoon hands-on exercises and discussing solutions
  • Handout of project assignment
• Second week
  • Working on assignment
  • Friday: Presenting final project assignment for week 2

If you have further questions, please contact teaching-lippert@hpi.de

Requirements

Google Colab or Jupyter Notebook environment:

We will work with notebooks in google colab to ensure consistent programming environments for everyone. If you don't want to use google colab, you can set up your notebook in jupyter i.e. with Anaconda. Here you can find a link on how to do this. Please be ready to open notebooks with either colab or jupyter when the course starts.

 

Basic programming skills

We will assume that participants have programmed before, i.e. know how to

• write and call functions
• work with external libraries
• use different data-types (int, char, strings, list, dictionaries)
• write loops, ...

in at least one programming language. If you want to look up how to do this in python, we recommend checking out this tutorial.

Or enroll in the self-paced open HPI course ‘Fundamentals of Programming for Digital Health
https://open.hpi.de/courses/hpi-dh-fprog2021. This course also provides auto-graded exercises.

Examination

• Solving a data analysis assignment in a team of max. 5 people
• 30min Team presentation on analysis results (70% team grade)
• 15min Q&A after presentation (30% individual grade) 

Dates

Block Course on Campus III (Seminar Room G1 + Pool room in basement G2)

We will meet on the first day in the DHC pool room G2.U.10-14

Lecture week 20th - 24th of March 2023
Project week from March 27th to 31st.
Final presentation day of project results on 31st of March 2023

Times: morning session from 9:15am - 12:00pm  + afternoon session from 1:30 -5pm

Zurück