040172 VU Doing Data Science (MA) (2020W)
Continuous assessment of course work
Labels
The course language is English.Only students who signed up for the class in univis/u:space are allowed to take the class (that means, that you have to at least be on the waiting list if you want to take this class). No exceptions possible.
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 14.09.2020 09:00 to We 23.09.2020 12:00
- Deregistration possible until Sa 31.10.2020 12:00
Details
max. 80 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Watch the short welcome video!
This is a hybrid class: generally, lectures will be recorded. This means that you can participate in the classroom (with distance) or online and/or watch the recorded lecture at a later time.Lecture times are Tuesday, 15:00-16:30, and Wednesday, 13:15-14:45. Other times and rooms are reserved for lab work and will be announced separately. We encourage you to participate in lectures during these times, since this allows for personal interaction (in the classroom or via online communication).The first appointment will be Tuesday, Oct 6, 15:00
Tuesday
06.10.
15:00 - 16:30
Digital
Wednesday
07.10.
13:15 - 14:45
Digital
Wednesday
07.10.
15:00 - 16:30
Digital
Tuesday
13.10.
13:15 - 14:45
Digital
Tuesday
13.10.
15:00 - 16:30
Digital
Wednesday
14.10.
13:15 - 14:45
Digital
Wednesday
14.10.
15:00 - 16:30
Digital
Tuesday
20.10.
13:15 - 14:45
Digital
Tuesday
20.10.
15:00 - 16:30
Digital
Wednesday
21.10.
13:15 - 14:45
Digital
Wednesday
21.10.
15:00 - 16:30
Digital
Tuesday
27.10.
13:15 - 14:45
Digital
Tuesday
27.10.
15:00 - 16:30
Digital
Wednesday
28.10.
13:15 - 14:45
Digital
Wednesday
28.10.
15:00 - 16:30
Digital
Tuesday
03.11.
13:15 - 14:45
Digital
Tuesday
03.11.
15:00 - 16:30
Digital
Wednesday
04.11.
13:15 - 14:45
Digital
Wednesday
04.11.
15:00 - 16:30
Digital
Tuesday
10.11.
13:15 - 14:45
Digital
Tuesday
10.11.
15:00 - 16:30
Digital
Wednesday
11.11.
13:15 - 14:45
Digital
Wednesday
11.11.
15:00 - 16:30
Digital
Tuesday
17.11.
13:15 - 14:45
Digital
Wednesday
18.11.
15:00 - 16:30
Digital
Tuesday
24.11.
13:15 - 14:45
Digital
Tuesday
24.11.
15:00 - 16:30
Digital
Wednesday
25.11.
13:15 - 14:45
Digital
Wednesday
25.11.
15:00 - 16:30
Digital
Tuesday
01.12.
13:15 - 14:45
Digital
Tuesday
01.12.
15:00 - 16:30
Digital
Wednesday
02.12.
13:15 - 14:45
Digital
Wednesday
02.12.
15:00 - 16:30
Digital
Tuesday
08.12.
15:00 - 16:30
Digital
Wednesday
09.12.
13:15 - 14:45
Digital
Wednesday
09.12.
15:00 - 16:30
Digital
Tuesday
15.12.
13:15 - 14:45
Digital
Tuesday
15.12.
15:00 - 16:30
Digital
Wednesday
16.12.
13:15 - 14:45
Digital
Wednesday
16.12.
15:00 - 16:30
Digital
Tuesday
12.01.
13:15 - 14:45
Digital
Tuesday
12.01.
15:00 - 16:30
Digital
Tuesday
19.01.
13:15 - 14:45
Digital
Tuesday
19.01.
15:00 - 16:30
Digital
Wednesday
20.01.
13:15 - 14:45
Digital
Wednesday
20.01.
15:00 - 16:30
Digital
Tuesday
26.01.
13:15 - 14:45
Digital
Wednesday
27.01.
15:00 - 16:30
Digital
Information
Aims, contents and method of the course
This course covers the fundamentals of setting up, managing, and conducting data science projects. Students acquire knowledge of processes describing how to approach and implement data science projects. They know the particular steps of the CRISP industry-standard, learn about various cases of how to apply this to different applications (from different areas such as business, humanities, astronomy), and are able to conduct data science projects themselves.This course consists of lectures, tutorials, showcases, and project presentations. Students will work on their own data science projects in interdisciplinary groups.
Assessment and permitted materials
Midterm test (30%): Nov 11, 15:00
Final test (30%): Dez 15, 15:00
Project work (40%): Ongoing, final presentations: Jan 26, Jan 27
Final test (30%): Dez 15, 15:00
Project work (40%): Ongoing, final presentations: Jan 26, Jan 27
Minimum requirements and assessment criteria
Midterm test and one more examination (project work / final test) must be passed individually. **
For project work, attendance is mandatory, including kick-off and project presentations.In total, 100 points can be achieved. Grades are assigned as follows:
1 (very good) • 100-90 points
2 (good) • 89-76 points
3 (satisfactory) • 75-63 points
4 (sufficient) • 62-50 points
5 (not enough) • 49-0 points
For project work, attendance is mandatory, including kick-off and project presentations.In total, 100 points can be achieved. Grades are assigned as follows:
1 (very good) • 100-90 points
2 (good) • 89-76 points
3 (satisfactory) • 75-63 points
4 (sufficient) • 62-50 points
5 (not enough) • 49-0 points
Examination topics
Midterm test/Final test: Slides and topics covered in the lectures.
Project work: topic-specific poster presentation, handout, KNIME workflow.
Project work: topic-specific poster presentation, handout, KNIME workflow.
Reading list
Provost/Fawcett: Data Science for Business - What you need to know about data mining and data-analytic thinking. http://www.data-science-for-biz.com/Silipo: KNIME Beginner's Luck - A Guide to KNIME Analytics Platform for beginners. https://www.knime.com/knimepress/beginners-luck
Association in the course directory
Last modified: Fr 12.05.2023 00:12