Stud.IP Uni Oldenburg
University of Oldenburg
18.10.2021 18:35:00
inf607 - Business Intelligence II (Complete module description)
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Module label Business Intelligence II
Module code inf607
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Applicability of the module
  • Master Applied Economics and Data Science (Master) > Data Science
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons
Marx Gomez, Jorge (Authorized examiners)
Lehrenden, Die im Modul (Authorized examiners)
Prerequisites
Skills to be acquired in this module
Current module provides advanced business intelligence, data science with focus on enterprises and strong emphasis on big data and data analytics. Students of the course are provided with knowledge, which reflects current research and development in a data analytics domain.

Professional competence
The students:
  • name and recognize the role of data analytics / data science as past of a daily business process in a particular company
  • able to organize from managment perspective data analytis project
  • being able to analyse advantages and disadvantages of different approaches and methods of the data analytics and being able to apply them in simple case studies
  • obtain theoretical knowledge about data collection and modelling processes, including state of the art approaches and available best practices


Methodological competence
The students:
  • being able to execute typical tasks of data analytis, and also being able to proceed deeper with respect to different approaches and methods
  • gain a hans on experience and being able to understand advantages and disadvantages of different methods and being able to use obtained knowledge


Social competence
The students:
  • build solutions based on case studies given to the group, for example design of regression model based on provided dataset
  • discuss solutions on a technical level
  • present obtained case studies solutions as part of the exercises


Self-competence
The students:
  • critically review provided offered information
Module contents
After current course students will get advanced knowledge in the domains such as business intelligence and data analytics. Besides that, students will have a chance to have a deeper look into related technical fields such as InMemory Computing, Data Mining and Machine Learning, Big Data Processing with Distributed Systems (e.g. Apache Hadoop / Spark) from both, research and practical, perspectives. Students will be provided with real-world experience gather from business intelligence and data science related projects. Materials of the course are believed to be justified with current demands of data analytics market. Thus, providing students with relevant knowledge in order to give them advantages in future job.
Reader's advisory
  • Jürgen Cleve, Uwe Lämmel (2014): "Data mining" (Deutsch)
  • Max Bramer (2013): "Principles of data mining" (English)
  • Ian Witten, Eibe Frank, Mark Hall (2011): "Data mining : practical machine learning tools and techniques" (English)
  • Jure Leskovec, Anand Rajaraman, Jeffrey Ullman (2014): "Mining of massive datasets" (English)
Links
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Modullevel / module level AS (Akzentsetzung / Accentuation)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method SE nach Ankündigung zu Beginn der Veranstaltung (2 SWS V + 2 SWS Ü oder Blockseminar)
Vorkenntnisse / Previous knowledge
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
2 SuSe 28
Seminar
2 SuSe 28
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
At the end of the lecture period
Written exam (max. 120 min. )