| No. | Type(s) | Class Title | |
| 5313 | PI | Data Analytics 
Registration via LPIS from 2024-02-22 15:00 to 2024-03-06 23:59 De-registration via LPIS from 2024-02-22 15:00 to 2024-03-09 23:59 |
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| Instructor(s) | PD Mag.Dr. Gertraud Malsiner-Walli, M.Stat., Dr. Lucas Kook |
| Subject(s) Bachelor Programs | Specialization in Business Administration Course II - Data Science Course II - Data Science Course II - Data Science |
| Credit hours | 2 |
| Language of instruction | English |
| Detailed schedule | ||||
| Tue, | 2024-03-12 | 08:00-11:00 | TC.5.15 (Map) | |
| Thu, | 2024-03-14 | 18:00-20:00 | TC.-1.61 (Map) | |
| Tue, | 2024-03-19 | 09:00-12:00 | TC.5.15 (Map) | |
| Thu, | 2024-03-21 | 18:00-20:00 | TC.-1.61 (Map) | |
| Tue, | 2024-04-09 | 08:00-11:00 | TC.5.15 (Map) | |
| Thu, | 2024-04-11 | 18:00-20:00 | TC.-1.61 (Map) | |
| Mon, | 2024-04-15 | 11:30-14:30 | D5.0.001 (Map) | |
| Tue, | 2024-04-16 | 10:00-12:00 | D2.-1.019 Workstation Room (Map) | |
| Tue, | 2024-04-23 | 08:00-11:00 | TC.5.15 (Map) | |
| Tue, | 2024-04-30 | 09:00-12:00 | P | TC.1.02 (Map) |
| Download schedule (ical) | Subscribe schedule | ||||
| Further information | https://learn.wu.ac.at/vvz/24s/5313 |
| Contact details: | ||
| gertraud.malsiner-walli@wu.ac.at; lucas.kook@wu.ac.at | ||
| Contents: | ||
One core element of modern Data Science are computational methodologies from the field of Machine Learning as well as Statistical Learning. The main methods will be discussed to allow for handling Regression, Classification and Clustering tasks. Real-life examples and data sets will be used. The statistical programming language R will be used to solve problems numerically. |
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| Learning Outcomes: | ||
| Students are able to identify a data science problem and choose the appropriate technology to solve the problem. Furthermore, the students are able to implement the respective algorithms using the statistical programming language R by selecting useful extension packages. Upon completion of the course participants will be able to: 1. Analyze data science problems structurally and find the appropriate method to solve the respective problem. 2. Solve data science problems using R. |
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| Attendance requirements: | ||
You are allowed to skip one unit at maximum. This regulation holds also for the online modus. At the final presentation of the project results you must be present. |
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| Teaching/learning method(s): | ||
| At the beginning theoretical foundations of Machine Learning technologies will be presented. Furthermore, an introduction to R for Data Science will be given. Over the course of the lecture student presentations will be a central part. | ||
| Assessment: | ||
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| Prerequisites: | ||
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. |
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