SEEDS: Systems enginEEring for Data Science
SEEDS: Systems enginEEring for Data Science
The specialisation "Systems enginEEring for Data Science" (SEEDS) is aimed at students on the Master's degree programme in Computing Science who are interested in the design and implementation of software- and data-intensive applications. Due to the advancing digitalisation in all areas of our society, complex software and data infrastructures have become an integral part of many companies and organisations. High-performance system architectures and data infrastructures are therefore required in practice. The SEEDS specialisation aims to train experts who are able to develop and operate these and critically evaluate the development techniques. The modules in this specialisation prepare students to design complex software and information systems and to implement and use them in a practical context. In addition, students are enabled to design, evaluate, further develop and apply methods and procedures for the targeted development of software- and data-centred systems.
Graduates with a specialisation in Systems Engineering for Data Science have not only acquired technical skills at all levels of modern software stacks, but have also gained insights into the legal and economic aspects of developing and operating complex data pipelines. The topics covered range from requirements and project management and the modelling of software systems to traditional database systems and new NoSQL technologies, as well as sensor/actuator systems, machine learning, big data analytics, data quality and data visualisation. Thanks to their sound conceptual training, graduates are therefore ideally equipped for a career in the field of full-stack development with a focus on data-intensive applications, software engineering and data science and fulfil requirement profiles that are in high demand across all industries.
To obtain the SEEDS certificate, successful participation in compulsory courses (totalling 18 CP), courses from the elective area (totalling 12 CP) and a seminar (6 CP) must be demonstrated.
The project group and the final thesis must be thematically related to the specialisation. (The contact person responsible for issuing the certificate will confirm whether this is the case for your chosen topic).
Compulsory courses (18 CP)
Successful completion of all of the following courses is compulsory:
- Information Management in Distributed Systems (Information Systems III) (inf109) (6 CP)
- Requirements Engineering & Management (inf108) (6KP)
- One of the following modules:
- Advanced Practical Course Databases (inf111) (6KP)
- Advanced Practical Data Science (inf1202) (6KP)
Elective area (12 CP) & seminars (6 CP)
In addition, successful participation in elective modules totalling 12 CP and 2 seminars totalling 6 CP is required:
- Data Science I (inf040)
- Special lectures (marked in the catalogue of modules as "Special topics from the field of ...")
- Data Science (inf1204)
- Information Systems I (inf170)
- Information Systems II (inf171)
- Software Engineering I (inf178)
- Software Engineering II (inf179)
- Data Challenge (inf541)
- Machine Learning I - Probabilistic Unsupervised Learning (phy730)
- Machine Learning II - Advanced Learning and Inference Methods (phy694)
- Seminar 2x3 CP (In the catalogue of modules "Current topics from ..."):
- Data Science I (TODO)
- Information Systems I (inf172)
- Information Systems II(inf173)
- Software Engineering I(inf180)
- Software Engineering II(inf181)
In individual cases, other courses with thematic relevance to the content of the specialisation can also be included. Please consult the contact person responsible for issuing the certificate.
The modules outside Computing Science must be studied as "Transdisciplinary modules" (formerly "NI modules") in the master's degree programme.
Outside the specialisation "Systems enginEEring for Data Science" (SEEDS), there are other courses with a thematic reference to the core content, in particular:
- Theoretical Computing Science:
- Cryptography (Inf493)
- Applied Computing Science:
- Computational Intelligence I (inf535)
- Management of Information Systems in Health Care (inf520)
- Computing Science:
- Hybrid Systems (inf300)
- Outside Computing Science:
- Information Technology Law (wir806)
- Data Science With Python (pb379) (enquired)