Nr. | LV-Typ(en) | LV-Titel | |
5777 | PI | SEA and SEO Marketing
Anmeldung über LPIS vom 16.02.2024 14:00 bis 22.02.2024 23:59 Abmeldung über LPIS vom 16.02.2024 14:00 bis 17.03.2024 23:59 |
LV-Leiter/in | Mag.Mag. Martin Reisenbichler, Bakk.phil. |
Planpunkte Bachelor | SBWL Kurs III - Digital Marketing Course III - Digital Marketing Kurs III - Digital Marketing |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Mi, | 20.03.2024 | 08:00-12:30 Uhr | LC.2.064 PC Raum (Lageplan) | |
Do, | 21.03.2024 | 09:00-13:30 Uhr | EA.5.044 (Lageplan) | |
Mi, | 17.04.2024 | 08:00-12:30 Uhr | D2.-1.019 Workstation-Raum (Lageplan) | |
Do, | 18.04.2024 | 08:00-12:30 Uhr | D2.0.038 (Lageplan) | |
Mi, | 15.05.2024 | 09:00-13:30 Uhr | LC.-1.038 (Lageplan) | |
Do, | 16.05.2024 | 08:00-12:30 Uhr | D3.0.218 (Lageplan) | |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5777 |
Kontakt: | ||
martin.reisenbichler@wu.ac.at | ||
Inhalte der LV: | ||
In this course, we cover two main operational fields of companies in digital marketing: SEO (Search Engine Optimization), and SEA (Paid Search Engine Advertising) - both billion dollar industries and an absolute mainstay in practical marketing for companies. In SEO and SEA, content writing and optimization is a very important factor for improving performance and acquiring customers. We aim at providing students with the following knowledge and skill set:
That enables students to prevail in that highly competitive field by using the practical skills gained in the course, as well as by being capable of applying thorough quantitative analyses, domain-related programming skills and analytic and generative methods using R and Python. |
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Lernergebnisse (Learning Outcomes): | ||
When you successfully complete the course you should have:
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Regelung zur Anwesenheit: | ||
Students are expected to attend all 6 units of the course and are expected to participate in class. In exceptional cases (e.g., sick leave), students might inform the lecturer and are allowed to miss at most one unit. That means that the extent of compulsory attendance is 5 units. The course takes place on campus and in presence mode. |
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Lehr-/Lerndesign: | ||
We use a combination of material presented by the lecturer supported by practical examples, R and Python code, and data. In addition, students get the opportunity for hands on optimization and analyses using state-of-the-art methods and tools in each unit. |
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Leistung(en) für eine Beurteilung: | ||
Students' performance is assessed based on various tasks listed below:
The grading scheme is as follows:
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Teilnahmevoraussetzung(en): | ||
Generally, the course is structured in a way that students with no prior programming skills are able to follow and successfully complete the course. However, prior quantitative knowledge and programming skills might be beneficial for students. |
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