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Veranstaltungsbeschreibung

335103a Big Data Scenarios Vorlesung

Zuletzt geändert:03.01.2020 / von Carlsburg
EDV-Nr:335103a
Studiengänge:
Dozent:
Sprache: Deutsch
Art: V
Umfang: 2 SWS
ECTS-Punkte: 4
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
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.
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