Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
960028 VU IT und Data Science Grundlagen (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
GEMISCHT
Hierbei handelt es sich um ein kostenpflichtiges Angebot der Universitätslehrgänge/Zertifikatskurse des Postgraduate Center. Bitte beachten Sie, dass für die Teilnahme eine Zulassung zum Universitätslehrgang/Zertifikatskurs erforderlich ist. Weitere Informationen zu den Angeboten des Postgraduate Center finden Sie unter: https://www.postgraduatecenter.at/
Details
Sprache: Deutsch
Lehrende
Termine
Zur Zeit sind keine Termine bekannt.
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
There is no written examination. The participants are expected to study set reading materials and the contents covered in class in order to successfully complete the assignments. Further information will be provided in Moodle well in advance.
Mindestanforderungen und Beurteilungsmaßstab
Three written assignments in Moodle. Grading Key:
1 (excellent) 25-23 points
2 (good) 22.9-20 points
3 (satisfactory) 19.9-16 points
4 (sufficient) 15.9-13 points
5 (insufficient) < 13 points
1 (excellent) 25-23 points
2 (good) 22.9-20 points
3 (satisfactory) 19.9-16 points
4 (sufficient) 15.9-13 points
5 (insufficient) < 13 points
Prüfungsstoff
The participants are expected to study set reading materials and the contents covered in class in order to successfully complete the assignments.
Literatur
The reading list including relevant journal articles and book chapters will be assigned in connection with the session topics and made available through Moodle.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 09.08.2024 13:06
o Data Science and data-driven research
o Machine learning
o Database systems
o Programming concepts
Basics of programming:
o Unix Shell (based on the Carpentries curriculum)
o Git and GitHub (based on the Carpentries curriculum)
o Working with Python (based on the Carpentries curriculum)
Learning Outcomes:
Understand the most important concepts in Data Science, Machine Learning, and Database Systems; Describe the basic concepts of programming; Know the basics of structured programming (e.g. data types, control structures, subroutines, functions …); Describe the most important programming languages and their different aspects; Choose a suitable Integrated Development Environment (IDE) for developing own programs; Work in the Unix Shell using the most important commands for the file system and data files; Use Git/GitHub for setting up own projects, share them and find other projects; Develop small programs in Python using basic data types, control structures, functions and software libraries