Universität Wien

040310 KU Modelling and Handling of Large Databases (MA) (2024S)

6.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
MIXED
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

"Students of the Master Program Business Analytics, that have not passed the exam "Programming for Business Analytics" yet, can be manually registered after successful examination. Please contact the lecturers or Carina Boier (carina.boier@univie.ac.at)"

  • Tuesday 05.03. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 06.03. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 13.03. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 19.03. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 20.03. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 09.04. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 10.04. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 16.04. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 17.04. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 23.04. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 24.04. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 30.04. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 07.05. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 08.05. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.05. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.05. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 21.05. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 22.05. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 28.05. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 29.05. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 04.06. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 05.06. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 11.06. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 12.06. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 18.06. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 19.06. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 25.06. 09:45 - 11:15 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 26.06. 11:30 - 13:00 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01

Information

Aims, contents and method of the course

The course introduces central methods to understand modeling and daily usage (CRUD operations) of databases and the characteristics of computer-based information systems. This knowledge will be applied to aggregate information and to facilitate and accelerate decision-making processes. Particular attention will be paid to conceptual and logical databases design, data analysis using SQL, algorithms for selecting materialized views, databases systems technology (indexes, star query optimization, physical design, query rewrite methods to use materialized views), and tools for managing a large amount of data. The student will acquire knowledge of the fundamental concepts to design and use database models. Furthermore, the student will learn to generate reports, data cubes and evaluate the performance of the modeled key processes to improve them. Beyond the relational part, we will have a look at NoSQL databases, such as Graphdatabases, Textdatabases and Document oriented databases. If there is enough time, the students will use process mining based on event-logs to model business scenarios.

This course consists of lectures, homework, and exams.

Generic goals:
- Improvement of programming skills
- Understanding of interplay in Business Administration and databases.

Students of the Master Business Analytics program who have not yet passed the exam "Introduction to Programming with Python" can be admitted to the course on a daily basis after successfully passing the exam. Please contact the course management for this purpose.

Assessment and permitted materials

Midterm test (30%)
Final test (30%)
Homework/Exercises/Project work (40% )

Minimum requirements and assessment criteria

In total, 100 points can be achieved. Grades are assigned as follows:
1 (very good) • 100-87 %
2 (good) • 86-75 %
3 (satisfactory) • 74-63 %
4 (sufficient) • 62-50 %
5 (not enough) • 49-0 %

Examination topics

Slides and topics covered in the lectures.

Reading list

- Ramakrishnan and Gehrke. Database Management Systems. 3rd Edition
- Garcia-Molina, Ullmann. Database Systems 2nd Edition. Prentice Hall
- Paulraj Ponniah, Data Warehousing Fundamentals for IT Professionals, Second Edition, Wiley
- Wil van der Aalst, Process Mining - Data Science in Action, Second Edition, Springer

Association in the course directory

Last modified: We 28.02.2024 09:45