Veranstaltungsbeschreibung

335103b Big Data Projekt

Zuletzt geändert:	06.06.2024 / Mullaewa
EDV-Nr:	335103b
Studiengänge:	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 7 Häufigkeit: nur WS
Dozent:
Sprache:	Englisch
Art:	P
Umfang:	2 SWS
ECTS-Punkte:	6
Workload:	45 hours teaching time + project work, preparation and follow-up work: 185 hours + exam preparation: approx. 70 hours = 300 hours
Inhaltliche Verbindung zu anderen Lehrveranstaltungen im Modul:	This course is part of a module. The second course belonging to this module is 335103a Big Data Scenarios - Lecture
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
English Abstract:	The module “Big Data Scenarios“ introduces students to the analysis of unstructured text data. The module consists of three elements: • lecture: introduces Big Data architectures, methods 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:	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.