Statistics in Connected Healthcare (Sommersemester 2023)
Dozent:
Prof. Dr. Bert Arnrich
(Digital Health - Connected Healthcare)
Allgemeine Information
- Semesterwochenstunden: 4
- ECTS: 6
- Benotet:
Ja
- Einschreibefrist: 01.04.2023 - 07.05.2023
- Prüfungszeitpunkt §9 (4) BAMA-O: 24.07.2023
- Lehrform: Vorlesung / Übung
- Belegungsart: Wahlpflichtmodul
- Lehrsprache: Englisch
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
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-C Concepts and Methods
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-T Technologies and Tools
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-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
- DANA: Data Analytics
- HPI-DANA-K Konzepte und Methoden
- DANA: Data Analytics
- HPI-DANA-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- 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
- This course introduces basic concepts of statistical data analysis and their practical application in mobile computing.
- The course covers the entire range of statistical data analysis. Topics include how to design an empirical data collection in a statistical valid way, how to collect data from daily life with the help of mobile computing, and how to achieve statistical test results.
- Lessons learned will be applied in practice by conducting empirical experiments with mobile phones.
- There are no special requirements to attend this lecture since all needed background knowledge is provided within the course.
- Please find more detailed information from here
Leistungserfassung
The final grade is composed of three equal parts:
- Experimental data collection and data analysis: 1/3
- Technical report: 1/3
- Presentation: 1/3
Termine
Mondays 13:30pm &
Tuesdays 9:15-10:45am
in G2.U10/14
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