Veranstaltungsbeschreibung

335103a Big Data Scenarios Vorlesung

Zuletzt geändert:	06.06.2024 / Mullaewa
EDV-Nr:	335103a
Studiengänge:	Online-Medien-Management (Bachelor, 7 Semester) , Prüfungsleistung im Modul Big Data Scenarios in Semester 7 Häufigkeit: unregelmäßig Studienübergreifendes Angebot - Minors, Prüfungsleistung im Modul Big Data Scenarios in Semester 1 Häufigkeit: nur WS Wirtschaftsinformatik und digitale Medien (Bachelor, 7 Semester) , Prüfungsleistung im Modul Big Data Scenarios in Semester Häufigkeit: nur WS
Dozent:
Sprache:	Englisch
Art:	V
Umfang:	2 SWS
ECTS-Punkte:	4
Workload:	22,5 hours teaching time + project work, preparation and follow-up work: 127,5 hours = 150 hours
Inhaltliche Verbindung zu anderen Lehrveranstaltungen im Modul:	This lecture is part of a module. The second course belonging to this module is 335103b Big Data Project.
Prüfungsform:
Bemerkung zur Veranstaltung:	Englisch
Beschreibung:	The module “Big Data Scenarios“ introduces students to the analysis of large volumes of text data in different formats (structured, semi-structured, unstructured). The module consists of four elements: • The lecture introduces Big Data architectures, methods and concepts. To get an in-depth understanding of the introduced methods, they are applied in two types of labs: • tool-based labs, using state-of-the-art data science software (RapidMiner) and • method-based labs without any specific data science tool support. • Finally, students work in teams to implement a full big data analytics solution, applying the methods and tools, which they got to know in the labs. The module has no formal pre-requisites, but is addressed to bachelor students in their final semesters. No programming is required but good analytic skills, a high motivation and an interest to develop models.
English Title:	Big Data Scenarios - Lecture
English Abstract:	The module “Data Science Project“ introduces students to the analysis of structured data using Data Science algorithms. The module consists of three elements: • lecture: introduces data science methods, algorithms and concepts. • technology-based labs, using state-of-the-art data science tools or programming languages • a project to apply everything learned in a broader context The module is addressed to bachelor students in their final semesters. Good analytic and programming skills, a high motivation and an interest to develop models are required.
Literatur:	Kotu, Vijay, and Bala Deshpande. Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer. Morgan Kaufmann, 2014. EMC Education Services. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. John Wiley & Sons, 2015 Manning, Christopher D., and Hinrich Schütze. Foundations of statistical natural language processing. MIT press, 1999. D. Jurafsky, J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics (2nd ed.), Prentice-Hall, 2009. Weitere Literatur finden Sie in der HdM-Bibliothek.