337098f Übung Big Data & Web Analytics

Zuletzt geändert:	01.03.2024 / Kirenz
EDV-Nr:	337098f
Studiengänge:	Online-Medien-Management (Bachelor, 7 Semester) , Prüfungsleistung im Modul Online Marketing II in Semester 4 Häufigkeit: immer
Dozent:	Prof. Dr. Jan Kirenz
Sprache:	Vorlesung deutsch, Materialien englisch
Art:	-
Umfang:	1 SWS
ECTS-Punkte:	1
Workload:	1 ECTS, 30 hours. KMP
Inhaltliche Verbindung zu anderen Lehrveranstaltungen im Modul:	Complementary exercises course for the lecture Big Data and Web Analytics (337098e).
Prüfungsform:
Bemerkung zur Veranstaltung:	Deutsch
Beschreibung:	Building on the foundational knowledge provided in the "Big Data and Web Analytics" lecture, this complementary exercises course is structured to offer hands-on experience through applied cases and real-world scenarios. It is designed to facilitate the practical application of theories, tools, and techniques learned in the main course, focusing specifically on executing advanced web analytics and deploying generative artificial intelligence technologies in various digital marketing contexts. Students will engage in a series of exercises that mimic real-life digital challenges. These exercises will cover the entire spectrum of content from the main course, including web tracking, social media metrics analysis, and the use of generative AI for innovative digital solutions. Participants will work on applied cases involving Google Analytics, Google Ads, and the creation of generative AI-driven interactive dashboards using tools and Python, among others. The course also emphasizes prompt engineering with OpenAI's GPT models, allowing students to design, test, and refine AI-driven content strategies. By the end of this exercises course, students will be able to: - Implement Web Analytics Solutions: Apply web analytics tools and techniques in practical scenarios to gather and analyze digital data effectively. - Deploy Generative AI Applications: Utilize generative AI, including prompt engineering and OpenAI Assistants API, in the creation of content, improving customer engagement, and optimizing digital marketing strategies. - Analyze and Interpret Social Media Data: Conduct in-depth analyses of social media metrics to evaluate and enhance social media campaign performances. - Create Interactive Dashboards and Tools: Develop and deploy interactive dashboards and tools, integrating analytics and AI insights for data-driven decision making. - Solve Real-World Digital Marketing Problems: Apply theoretical knowledge to solve complex digital marketing problems, demonstrating a comprehensive understanding of the digital customer journey and the impact of technology in marketing. This course serves as an essential bridge between theoretical knowledge and practical application, enabling students to confidently apply their skills in the dynamic field of digital marketing and artificial intelligence. Through these applied exercises, students will not only reinforce their understanding of key concepts but also gain invaluable hands-on experience that prepares them for real-world challenges.
English Title:	Exercise Big Data & Web Analytics
English Abstract:	Building on the foundational knowledge provided in the "Big Data and Web Analytics" lecture, this complementary exercises course is structured to offer hands-on experience through applied cases and real-world scenarios. It is designed to facilitate the practical application of theories, tools, and techniques learned in the main course, focusing specifically on executing advanced web analytics and deploying generative artificial intelligence technologies in various digital marketing contexts. Students will engage in a series of exercises that mimic real-life digital challenges. These exercises will cover the entire spectrum of content from the main course, including web tracking, social media metrics analysis, and the use of generative AI for innovative digital solutions. Participants will work on applied cases involving Google Analytics, Google Ads, and the creation of generative AI-driven interactive dashboards using tools and Python, among others. The course also emphasizes prompt engineering with OpenAI's GPT models, allowing students to design, test, and refine AI-driven content strategies.