Big Data Analytics

Faculty of Agricultural Science and Landscape Architecture

Version 13.0 of 09/06/2019

44M0139

Big Data Analytics

• Agrar- und Lebensmittelwirtschaft (M.Eng.)
• Angewandte Nutztier- und Pflanzenwissenschaften (M.Sc.)

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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.

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

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.

Lectures, exercises

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.

Meseth, Nicolas

Meseth, Nicolas

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Wird in der Veranstaltung bekannt gegeben.

• Portfolio exam
• Two-Hour Written Examination
• Viva Voce

Regular Participation

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

1 Term

Only Summer Term

German and English