Syllabus
Registration via LPIS
Day | Date | Time | Room |
---|---|---|---|
Wednesday | 11/27/24 | 12:00 PM - 01:30 PM | TC.1.01 OeNB |
Wednesday | 12/04/24 | 08:00 AM - 11:00 AM | TC.4.03 |
Wednesday | 12/11/24 | 08:00 AM - 11:00 AM | TC.4.03 |
Wednesday | 12/18/24 | 08:00 AM - 11:00 AM | TC.4.03 |
Wednesday | 01/08/25 | 08:00 AM - 11:00 AM | TC.4.03 |
Wednesday | 01/15/25 | 10:00 AM - 01:00 PM | TC.4.03 |
Wednesday | 01/22/25 | 10:00 AM - 01:00 PM | TC.4.03 |
Wednesday | 01/29/25 | 10:00 AM - 01:00 PM | TC.4.03 |
This class covers the most important digital marketing & social media instruments and aims to teach students the skills needed to run and optimize digital marketing campaigns (with the help of AI).
After successful completion of the course, the learned is expected to be able to:
- Use digital marketing jargon and comfortably participate in discussions with digital marketing professionals.
- Explain how digital marketing & social media instruments work, and how marketers use them.
- Understand how data is crucial to the success of digital marketing, and know about various biases that come with that data.
- Analyze profitability of digital marketing campaigns.
- Get a glimpse of how generative AI changes digital marketing.
You need to attend at least 80% of all classes to pass the course, which means effectively that you can miss not more than one lecture.
The course uses a combination of case discussions, group projects, and lecture-style sessions.
Workload: The course has 5 ECTS, which translates into a workload of 125–150 hours, 22.5 hours of which are contact hours in the classroom. That means that you’ll need to work for about 100 hours outside the classroom. The workload that you can anticipate for each grading component is approx. proportional to the weight that this grading component has for your final grade (i.e., expect a workload of approx. 10 hours for an assignment that has a 10% weight).
Weights for each grading component (plus links to assignments and group project):
- Group project: 40%
- Individual Assignments:
- Individual Assignment 1: 20%
- Individual Assignment 2: 20%
- Individual Assignment 3: 15%
- In-class participation: 5%
Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.
We are happy to answer your questions, so feel free to send us a short email if you would like to talk to us in person. We will also try to be available in the classroom after each class or during the breaks of each class.
Please do not expect that we answer long emails with numerous questions to topics of the class! It is usually much more productive to personally meet and discuss your questions.
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