PřF:Bi8700 Topics data manag, anal vis - Course Information
Bi8700 Topics on data management, analysis and visualization
Faculty of ScienceSpring 2020
- Extent and Intensity
- 0/1/0. 2 credit(s). Type of Completion: z (credit).
- Teacher(s)
- RNDr. Martin Komenda, Ph.D., MBA (lecturer)
Mgr. Matěj Karolyi (lecturer)
Mgr. Martin Víta, Ph.D. (lecturer) - Guaranteed by
- RNDr. Martin Komenda, Ph.D., MBA
RECETOX – Faculty of Science
Contact Person: RNDr. Martin Komenda, Ph.D., MBA
Supplier department: RECETOX – Faculty of Science - Prerequisites
- General interest in a domain of data processing, analysis and visualisation.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 12 student(s).
Current registration and enrolment status: enrolled: 0/12, only registered: 0/12, only registered with preference (fields directly associated with the programme): 0/12 - fields of study / plans the course is directly associated with
- Biomedical bioinformatics (programme PřF, N-MBB)
- Epidemiology and modeling (programme PřF, N-MBB)
- Mathematical Biology (programme PřF, N-EXB)
- Course objectives
- This course introduces selected topics from a domain of data processing, analysis and visualisation. 4 particular projects in a form of interactive workshops under the supervision of experts from practice will be organised. Each workshop will cover basics of the proven methodology and method for data mining. The active cooperation between students groups and mentors will be needed.
- Learning outcomes
- Student understands the need of systematic usage of data mining methodological background.
Student meets up-to-date trends in a domain of data processing, analysis and visualisation.
Student adopts new techniques during a solution of pilot research projects. - Syllabus
- The topics for spring 2020 are the following:
- Medical curriculum mapping
- Open data: Data analysis and visualisation
- Effective visualisation and data storytelling
- Deep learning (2 blocks)
- Teaching methods
- Practical-oriented workshops blocks in a form of 3 hours long classes, which consist of interactive quizzes, simplified tasks, CRISP-DM application in practice, discussion in pairs).
- Assessment methods
- At least 80 % of attendance.
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
The course is taught: in blocks.
- Enrolment Statistics (Spring 2020, recent)
- Permalink: https://is.muni.cz/course/sci/spring2020/Bi8700