| 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
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Dozent:
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Sprache:
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Englisch
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Art:
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P
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Umfang:
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2 SWS
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ECTS-Punkte:
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6
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Workload:
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45 hours teaching time
+ project work, preparation and follow-up work: 185 hours
+ exam preparation: approx. 70 hours
= 300 hours
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Inhaltliche Verbindung zu anderen Lehrveranstaltungen im Modul:
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This course is part of a module. The second course belonging to this module is 335103a Big Data Scenarios - Lecture
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Prüfungsform:
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Bemerkung zur Veranstaltung:
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Englisch
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Beschreibung:
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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.
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English Title:
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Big Data Scenarios
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English Abstract:
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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.
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Literatur:
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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.
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