Nr. | LV-Typ(en) | LV-Titel | |
5678 | PI | Social Media Analytics
Anmeldung über LPIS vom 21.02.2024 14:00 bis 28.02.2024 23:59 Abmeldung über LPIS vom 21.02.2024 14:00 bis 01.03.2024 23:59 |
LV-Leiter/in | Dipl.-Ing. Christian Hotz-Behofsits, Ph.D. |
Planpunkte Bachelor | SBWL Kurs III - Handel und Marketing SBWL Kurs IV - Handel und Marketing SBWL Kurs V - Handel und Marketing Kurs III - Handel und Marketing Kurs IV - Handel und Marketing Kurs V - Handel und Marketing |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Mo, | 04.03.2024 | 11:00-18:00 Uhr | TC.3.11 (Lageplan) | |
Mo, | 11.03.2024 | 11:00-18:00 Uhr | TC.5.12 (Lageplan) | |
Mo, | 18.03.2024 | 11:00-18:00 Uhr | TC.3.08 (Lageplan) | |
Di, | 19.03.2024 | 15:00-16:30 Uhr | TC.5.16 (Lageplan) | |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5678 |
Kontakt: | ||||||||||||||||||||||
christian.hotz-behofsits@wu.ac.at | ||||||||||||||||||||||
Inhalte der LV: | ||||||||||||||||||||||
The rapid proliferation of the Internet and related services, especially social networks and user-generated content, has resulted in incredible amounts of digital information. This is better known as "Big Data" and enables new ways of marketing. However, analyzing massive amounts of data requires specialized tools and methods. Industry managers and researchers increasingly turn to technological innovations that make decision-making effective in Big Data. The course covers three topics. Each of the three topics is covered in a separate block. Your progress will be assessed through theoretical and practical assignments you can complete at home. |
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Lernergebnisse (Learning Outcomes): | ||||||||||||||||||||||
The goal of this course is to provide students with an understanding of how big data can be used for customer analytics (e.g., posting behavior), contextual marketing (e.g., creating personalized recommendations), and online communications management (e.g., reputation management, influencer marketing, and social media crises). Participants are shown how to analyze text social media posts in practical sessions. In this context, the language of Twitter, Reddit, and Amazon reviews is recognized, and moods and emotions are extracted. It will also show how these modern technologies can support management decision-making and optimize customer experience in a data-driven world. After the course, you will be able to:
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Regelung zur Anwesenheit: | ||||||||||||||||||||||
You must attend at least 80% of all class sessions to pass the course (whether online or physical). In the case of online sessions, we expect active participation and a camera turned on. Students are allowed to miss a total of two sessions. Missing one session will not require additional direction; on the second missed session or more, written completion of a replacement assignment will be required. In the case of technical difficulties, evidence of the technical problem (e.g., screenshots) is required. |
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Lehr-/Lerndesign: | ||||||||||||||||||||||
Learning methods include classical knowledge transfer, inquiry, and independent development of topics and issues. In addition, practical examples are demonstrated, which the students adopt. |
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Leistung(en) für eine Beurteilung: | ||||||||||||||||||||||
Grading is based on the following components: Practical (practical assignments), theoretical assignments (theoretical assignments), and in-class participation.
Practical Assignments: practical assignments must be completed for each block. For example, students must complete small assignments. This part is to demonstrate the practical skills learned in this course. Theoretical Assignments: For each block, students have to answer theoretical questions.
These grading components are added together to calculate the final grade, which is based on the following grading scheme:
To successfully pass this course, your cumulative points must exceed 60 points. |
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