Introduction Data Science and Python for Digital Health (Wintersemester 2020/2021)
Dozent:
Prof. Dr. Christoph Lippert
(Digital Health - Machine Learning)
,
Dr. rer. nat. Stefan Konigorski
(Digital Health - Machine Learning)
,
M.Sc. Aiham Taleb
(Digital Health - Machine Learning)
,
Jana Fehr
(Digital Health - Machine Learning)
Allgemeine Information
- Semesterwochenstunden: 2
- ECTS: 3
- Benotet:
Ja
- Einschreibefrist: 01.10.2020 - 20.11.2020
- Lehrform: compact course V / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
- Maximale Teilnehmerzahl: 35
Studiengänge, Modulgruppen & Module
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-C Concepts and Methods
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-T Technologies and Tools
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-S Specialization
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-T Technologies and Tools
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-S Specialization
Beschreibung
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.
- 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) (Due to on Corona Situation)
- 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: Handout of final project assignment for week 2
If you have further questions, please contact teaching-lippert@hpi.de
Leistungserfassung
- Solving a data analysis assignment in a team of max. 5 people
- 30min Team presentation on analysis results (70% team grade)
- 30min Q&A after presentation (30% individual grade)
Termine
Monday 15.03.2021 until Friday, 26.03.2021
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