Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Wednesday | 03/15/23 | 08:30 AM - 12:30 PM | TC.3.06 |
| Wednesday | 03/22/23 | 08:30 AM - 12:30 PM | D2.0.342 Teacher Training Raum |
| Wednesday | 03/29/23 | 08:30 AM - 12:30 PM | TC.3.08 |
| Wednesday | 04/12/23 | 08:30 AM - 12:30 PM | TC.3.08 |
| Wednesday | 04/19/23 | 08:30 AM - 12:30 PM | D2.0.392 |
| Wednesday | 04/26/23 | 08:30 AM - 12:30 PM | D5.1.003 |
The course consists of several blocks:
In the first block, an introduction into applications of data science in the field of sensor data will be given. We will use data collected by everyday devices like smartphones, but the methods apply to all machine generated data. The following topics will be covered: Basics of sensory data, Handling noise and missing values, feature engineering, learning based on sensory data.
In the second block, we will look at methods and models to make use of such data.
All practical examples will be done in R language.
After completing this course students will have knowledge about different areas of application for data science. Students will have a basic understanding of area-specific challenges and algorithms. Besides an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.
The rules on the attendance of a Continuous Assessment Course (PI) apply.
Pursuant to the general guidelines issued by the Vice-Rector for Academic Programs and Student Affairs, the attendance requirement is met if a student is present at least 80% of the time. Students who fail to meet the attendance requirement will be de-registered from the continuous assessment course with a “fail” grade.
The final grade will be computed on the basis of two assignments and a presentation.
First assignment: 35 %
Second assignment: 35%
Presentation: 30%
Grades:
1: =>90%
2: =>80%
3: =>70%
4: =>60%
Successful conclusion of the course 1 of SBWL Data Science.
Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).
Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.
Back