LV-Leiter/in | Univ.Prof. Dr. Axel Polleres, Mag. Elmar Kiesling, Ph.D. |
Planpunkte Master | Data Management and Analytics |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Fr, | 08.03.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 15.03.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 22.03.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 12.04.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 19.04.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 26.04.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 03.05.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 10.05.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 17.05.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 24.05.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 31.05.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 14.06.2024 | 10:00-12:00 Uhr | TC.3.05 (Lageplan) | |
Fr, | 21.06.2024 | 10:00-12:00 Uhr | P | TC.0.03 WIENER STÄDTISCHE (Lageplan) |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5934 |
Kontakt: | ||
In emails to the instructors, please use subject: "[Data Management and Analytics]" | ||
Inhalte der LV: | ||
Assuming familiarity with basic data management and storage techniques (such as ER models and SQL), which – if needed – will be repeated in a bridging course, this course shall teach you essentials of data management and analytics, from the concepts to their application on practical examples (for example applied to Web data but also to business scenarios).
Part A of the course will focus on advanced databases, storage and data management techniques, analytical queries using SQL and Relational Database Management Systems, but also discuss Document and GraphDatabases. Next, we will discuss how to make certain tasks scale with big data (i.e. high volume, high velocity or highly heterogeneous data). To this end, we will review traditional indexing techniques and methods to deal with concurrent data access and discuss trends in Data Management and Storage. In Part B, we will cover (Descriptive, Predictive and Prescriptive Analytics) Data Analytics techniques and discuss how these can be scaled. |
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Lernergebnisse (Learning Outcomes): | ||
In this course you shall
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Regelung zur Anwesenheit: | ||
According to the examination regulation full attendance is intended for a PI. Attendance of 80% of all classes is compulsory |
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Lehr-/Lerndesign: | ||
The covered topics will be discussed in 12 classes, each of which will consist of concepts delivered in the form of pre-watching videos or reading materials to be prepared by the students, which are then in the lecture applied in Jupyter notebooks. |
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Leistung(en) für eine Beurteilung: | ||
Grading Scheme: >= 90% ... Excellent (1) >= 80% ... Good (2) >= 70% ... Satisfacory (3) >= 60% ... Sufficient (4) < 60% ... Fail |
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