Big Data Analytics

Faculty

Faculty of Agricultural Science and Landscape Architecture

Version

Version 13.0 of 09/06/2019

Code of Module

44M0139

Modulename (german)

Big Data Analytics

Study Programmes
  • Agrar- und Lebensmittelwirtschaft (M.Eng.)
  • Angewandte Nutztier- und Pflanzenwissenschaften (M.Sc.)
Level of Module

4

Mission Statement

The progressive digitization of products, services and processes is creating ever-increasing amounts of data. At the same time, the data is very diverse in its kind. In analyzing data, companies have great potential for better and more sustainable decisions. However, at the same time the quantity and diversity poses great challenges for the companies. In this module, students learn methods and technologies in order to successfully cope with these challenges.

Content

Principals of Big Data Analytics Management of Big Data Use of Big Data

Learning Outcomes

Knowledge Broadening
The students know the essential characteristics of big data, and are aware of the challenge of analyzing big data. They are familiar with methods and technologies for dealing with big data.
Knowledge Deepening
Students know the differences between structured and unstructured data. In addition to the relational database, students are familiar with new forms of databases that can be used with unstructured data.
Instrumental Skills and Competences
Students use technologies from the big data environment to manage and evaluate large amounts of data. They classify unknown data based on important characteristics and select meaningful procedures and technologies for processing and analysis.
Communicative Skills and Competences
The students extract important insights from a confusing amount of data to answer questions and prepare the results in an appropriate way. They present analysis results in an appropriate manner and aligned to the respective target group.
Systemic Skills and Competences
The students use methods and tools in the company to generate added value from previously unknown data.

Mode of Delivery

Lectures, exercises

Expected Knowledge and/or Competences

It is recommended the module "Information Management" of the Bachelor degree. Alternatively or in addition, the module "Applied Analytics" from the Bachelor is useful. The event can also be attended without prior knowledge. In this case, an independent training by means of provided materials is required.

Responsible of the Module

Meseth, Nicolas

Lecturer(s)

Meseth, Nicolas

Credits

5

Concept of Study and Teaching
Workload Dozentengebunden
Std. WorkloadLehrtyp
10others
40others
5others
Workload Dozentenungebunden
Std. WorkloadLehrtyp
30others
45others
20others
Recommended Reading

Wird in der Veranstaltung bekannt gegeben.

Graded Exam
  • Portfolio exam
  • Two-Hour Written Examination
  • Viva Voce
Ungraded Exam

Regular Participation

Assessment Methods Remark

Grading in the portfolio:Case study, written (FSS) - 70%Answer-Voting Procedure (AWV) - 15%Answer-Voting Procedure (AWV) - 15%Regular participation (RT) - ungraded

Duration

1 Term

Module Frequency

Only Summer Term

Language of Instruction

German and English

 

Contact

Hochschule Osnabrück
University of Applied Sciences
Albrechtstr. 30
49076 Osnabrück
Phone: +49 (0)541 969-0
Fax: +49 (0)541 969-2066
E-Mail: webmaster@hs-osnabrueck.de

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UNIVERSITY OF APPLIED SCIENCES

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