TU Wien:AKSTA Statistical Computing VU (Filzmoser)
|Lecturers||Prof. Filzmoser Peter|
|Department||Stochastik und Wirtschaftsmathematik|
|Links||tiss:107106 , Mattermost-Channel|
|Master Data Science||Pflichtmodul FDS/FD - Fundamentals of Data Science - Foundations|
|Bachelor Medizinische Informatik||Wahlmodul Statistische Datenanalyse|
|Bachelor Software & Information Engineering||Wahlmodul Statistische Datenanalyse|
- Programming in R (basics, and focus on solving problems related to statistical aspects of Data Science)
- How to read in data from files, clean them, organize them.
- some parts of the "tidyverse"
- Data visualization with R (base, ggplot2)
- basic SQL
This class makes use of the platform DataCamp. Students get (time-limited) access to the platform where they can (have to) take relevant courses, related to programming in R. Including how to read in, handle, visualize data, etc. The access allows also to do other classes, not related to R, such as for Python, SQL, and more, which is very nice!
There are also weekly classes where some material is discussed [I did not attend, so can't say anything, someone else please fill in ;-)]
SS20: Attendance of 50% of classes is required now. During class, there is an exercise to be done after an "impulse talk". The deadline for the in-class exercises is 23:55 of the same day (there is no requirement to finish in class). The in-class exercises account for 30% of the grade, if attended. The other 70% of the grade consist of the DataCamp exercises.
Some knowledge in R will make the start easier, but there are at least two courses, namely "Introduction to R" and "Intermediate R" that will get you started from zero. These will teach you about the basic variables, data structures, etc., as well as programming, control flow, functions, etc. in R. More in-depth R classes follow then and should deepen your understanding.
No exam, grading based exercises completed in DataCamp.
Dauer der Zeugnisausstellung
Verbesserungsvorschläge / Kritik