Statistics in Connected Healthcare (Sommersemester 2023)
Lecturer:
Prof. Dr. Bert Arnrich
(Digital Health - Connected Healthcare)
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 01.04.2023 - 07.05.2023
- Examination time §9 (4) BAMA-O: 24.07.2023
- Teaching Form: Lecture / Exercise
- Enrolment Type: Compulsory Elective Module
- Course Language: English
Programs, Module Groups & Modules
- 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
Description
- 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
Examination
The final grade is composed of three equal parts:
- Experimental data collection and data analysis: 1/3
- Technical report: 1/3
- Presentation: 1/3
Dates
Mondays 13:30pm &
Tuesdays 9:15-10:45am
in G2.U10/14
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