Applied data science on real-world hospital data (Sommersemester 2023)
Dozent:
Prof. Dr. Bernhard Renard
(Data Analytics and Computational Statistics)
,
Ferdous Nasri
(Data Analytics and Computational Statistics)
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2023 - 07.05.2023
- Lehrform: Seminar
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Deutsch
- Maximale Teilnehmerzahl: 10
Studiengänge, Modulgruppen & Module
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-K Konzepte und Methoden
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-T Techniken und Werkzeuge
- ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- DASY: Data Systems
- HPI-DASY-K Konzepte und Methoden
- DASY: Data Systems
- HPI-DASY-T Techniken und Werkzeuge
- DASY: Data Systems
- HPI-DASY-S Spezialisierung
- DAPP: Data Applications
- HPI-DAPP-K Konzepte und Werkzeuge
- DAPP: Data Applications
- HPI-DAPP-T Techniken und Werkzeuge
- DAPP: Data Applications
- HPI-DAPP-S Spezialisierung
- 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
- SECA: Security Analytics
- HPI-SECA-K Konzepte und Methoden
- SECA: Security Analytics
- HPI-SECA-T Techniken und Werkzeuge
- SECA: Security Analytics
- HPI-SECA-S Spezialisierung
- CYAD: Cyber Attack and Defense
- HPI-CYAD-K Konzepte und Methoden
- CYAD: Cyber Attack and Defense
- HPI-CYAD-T Techniken und Werkzeuge
- CYAD: Cyber Attack and Defense
- HPI-CYAD-S Spezialisierung
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-S Specialization
- MALA: Machine Learning and Analytics
- HPI-MALA-C Concepts and Methods
- MALA: Machine Learning and Analytics
- HPI-MALA-T Technologies and Tools
- MALA: Machine Learning and Analytics
- HPI-MALA-S Specialization
Beschreibung
Within this course, we will work with hospital data from a collaboration partner to improve quality control processes. Students will implement software to interconnect data from different sources, design automated data analysis procedures, build dashboards for visualization, derive recommendations for decision makers in hospital regarding quality and discuss results with end users to iteratively improve performance.
Voraussetzungen
For this course you must have successfully passed the courses on Computational Statistics and Hierarchical Classification (or have equivalent knowlege explicitly approved by the teaching team before the first meeting of the course). Knowledge of German is required for the communication with project partners.
Participating in a pre-meeting with the external project partners during the semester break is strongly recommended.
Participants will need to sign a confidentiality agreement with project partners and agree to publication of code under a MIT licence.
Leistungserfassung
Presentation (40%)
Final Report (60%)
Termine
Topics for projects will be introduced in the first meeting of the class, and students will send their preferred projects to the teaching team by the first week, projects will be assigned by the end of the first week, last time point to drop the class is April 28th 2023.
The first meeting will be on Thursday, April 20th at 11:00 am in room K-1.02.
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