Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI
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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

Digital Health MA
  • 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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