TU Wien:Data Acquisition and Survey Methods VU (Klaus Nordhausen)
- Data Acquisition and Survey Methods VU (Klaus Nordhausen) (TU Wien, 0 Resources)
- Data Acquisition and Survey Methods VU (Nordhausen) (TU Wien, 0 Resources)
- Data Acquisition and Survey Methods VU (Posekany) (TU Wien, 0 Resources)
|Department||Stochastik und Wirtschaftsmathematik|
|Master Data Science||Pflichtmodul FDS/CO - Fundamentals of Data Science - Core|
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Each class consists of lecture, discussion of the practial for the next session as well as students showing their solution of the current session's practical.
Attendance is mandatory and checked with lists to be signed.
Some R knowledge and also basic RMarkdown is definitely required. The R code for the exercises is usually quite simple and example code can often be found in lecture slides. Some basic statistical knowledge (e.g. t-tests, ANOVA) and basics of linear regression models are also required.
In English. Lecture with slides, theoretic concepts, terminology, R code snippet examples. Usually, questions to audience and small discussions, questions from audience are in my opinion encouraged. [SS 2019: this is a totally new class, so this all may be subject to change]
Practicals, that is problem sets. Mostly to be solved with small R scripts. Sometimes a few lines are to be written about theoretical concepts and important terminology. The practicals are to be deliverd as PDFs, created using RMarkdown.
[SS2019] There is no exam. There is a final assignment, with 3 larger tasks to solve, similar to the practicals. Again in RMardkdown, as a report showing R code and wirtten conclusion statements. This is graded together with the submitted practicals towards a final grade.
Dauer der Zeugnisausstellung
Attendance of classes and few hours for each week's practical. The final project is more work, expect a few days of work [iirc; from SS2019, the first time this class was held].
Lecture slides are provided on a per-session basis. One problem set for each practical (exercise).
For some of the presented models and concepts, I had to read a little in the literature to understand the ideas and applications better.
Verbesserungsvorschläge / Kritik