Data Engineering

Moreover, the Data Engineering program attracts students with diverse career goals, backgrounds, and prior work experience. Therefore, the program offers four focus tracks within which the students can choose to specialize further:

• Computer Science Track: In particular, Computer Science provides students with the skills to go beyond a mere usage of existing toolboxes and to develop innovative data analysis techniques of their own design.
• Geo-Informatics Track: It gives students an introduction to Geographic Information System techniques, principles of spatial analysis, and data mining with integration of remote sensing and GPS. It thereby provides an early exposure to earth science data and its handling.
• Bioinformatics Track and the analysis of biomedical data: Integration and model-based interpretation of high-throughput data are severe bottlenecks in biomedical and pharmaceutical research. Data Engineering prepares students for the novel computational challenges in these fields.
• Business & Supply Chain Engineering Track: Students can also choose the specialization track in Business & Supply Chain Engineering. A vast amount of data is collected as part of business processes in particular along supply chains. In this specialization track, students will concentrate on the full data analysis cycle including pre-processing of data, data analysis and deployment of model results within the business process.

These tracks are a preparation for the Advanced Projects within the Discovery Area and the Master Thesis.

Curriculum

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The Data Engineering graduate program is composed of foundational lectures, specialized modules, industry seminars and applied project work, leading to a master thesis that can be conducted in research groups at Constructor University, at external research institutes or in close collaboration with a company. The program takes four semesters (two years). The following table shows an overview of the modular structure of the program. The program is sectioned into five areas (Core, Elective, Methods, Discovery, and Career) and the Master Thesis. All credit points (CP) are ECTS (European Credit Transfer System) credit points. In order to graduate, students need to obtain 120 CP.

Core Area(45 CP)

This area is the centerpiece of the Data Engineering program. The nine mandatory modules in the Core Area cover essential methods of data engineering. They provide the foundations for further, more advanced courses and applied projects by introducing the fundamental concepts, methods and technologies used in data engineering. The modules are intensive courses accompanied by hands-on tutorials and labs.

To pursue a DE master, the following Core modules (40 CP) need to be taken as mandatory modules (m):

• CORE Module: Data Management and Databases (m, 5 CP)
• CORE Module: Data Analytics (m, 5 CP)
• CORE Module: Python Programming for Data Engineering (m, 5 CP)
• CORE Module: Cloud Computing (m, 5 CP)
• CORE Module: Data Pipeline Engineering (m, 5 CP)
• CORE Module: Data Security and Privacy (m, 2.5 CP)
• CORE Module: IT Law (m, 2.5 CP)
• CORE Module: Data Warehousing: Concepts and Technologies (m, 5 CP)
• CORE Module: Image Processing for Data Engineers (m, 5 CP)
   

Elective area (25 CP)

The Data Engineering program attracts students with diverse career goals, backgrounds, and prior work experience. Therefore, modules in this area can be chosen freely by students depending on their prior knowledge and interests. Students can choose to strengthen their knowledge in elective modules in the area of Computer Science, Geo-Informatics, Bio-Informatics, Business & Supply Chain Engineering and Mathematics. These modules can also serve as preparation for the Advanced Projects or the Internship within the Discovery Area and for the Master Thesis. 

The following mandatory elective (me) modules can be chosen in the Elective Area: 

• Big Data Challenge (me, 5 CP)
• Machine Learning (me, 5 CP)
• Data Acquisition Technologies and Sensor Networks (me, 5 CP)
• Principles of Statistical Modeling (me, 5 CP)
• Advanced Databases (me, 5 CP)
• Network Theory (me, 5 CP)
• Geo Informatics (me, 5 CP)
• Modeling and Analysis of Complex Systems (me, 5 CP)
• Management and Analysis of Biological and Medical Data (me, 5 CP)
• Data Mining (me, 5 CP)
• Data Analytics in Supply Chain Management (me, 5 CP)
• Modeling and Control of Dynamical Systems (me, 5 CP)

In addition to these elective modules, up to one relevant module from other graduate programs and up to one 3rd year modules from the undergraduate curriculum at Constructor University can be taken after consultation with the academic advisor and with the approval of the program coordinator. 

Particularly relevant elective modules from other graduate programs include

• Text Analysis and Natural Language Processing (DSSB, me 5CP)
• Deep Learning (CSSE, me 5CP)

Constructor University offers special remedial modules, which are recommended to refresh knowledge or to fill knowledge gaps, preparing students to successfully take the Data Engineering Core Area modules. Based on a placement test in the orientation week, the academic advisor will propose which of the modules are useful depending on prior knowledge of the student.
The following remedial modules can be chosen in the Elective area:

• Calculus and Linear Algebra for Graduate Students (me, 5 CP)
• Probabilities for Graduate Students (me, 5 CP) 

Constructor University offers special remedial modules, which are recommended to refresh knowledge or to fill knowledge gaps, preparing students to successfully take the Data Engineering Core Area modules. Based on a placement test in the orientation week, the academic advisor will propose which of the modules are useful depending on prior knowledge of the student.
The following remedial modules can be chosen in the Elective area:

• Calculus and Linear Algebra for Graduate Students (me, 5 CP)
• Probabilities for Graduate Students (me, 5 CP) 

Discovery area (15 CP)

This area introduces   the students to Current Topics and Challenges in Data Engineering already in the first semester with lectures   taught by faculty members and invited experts from external research institutes and companies, who present selected fields of their research and interest in data engineering. The students  then choose   projects or internships on such topics of current interest  in the second and third semester allowing them to apply  data engineering  methods to practical, real-life tasks.

The following module is mandatory (m) in the Discovery area: 

• Discovery Module: Current Topics in Data Engineering (m, 5 CP)
• The following modules are mandatory elective (me) in the Discovery area:
• Discovery Module: Data Engineering Advanced Project t I (me, 5 CP)
• Discovery Module: Data Engineering Advanced Project II (me, 5 CP)
• Discovery Module: Data Engineering Advanced Internship (me, 5 CP)

Career area (10 CP)

In this area students acquire skills to prepare them for a career as data engineers in industry. 
The following modules are mandatory (m) for the career area: 

• Career Module: Language (m, 2.5 CP)
• Career Module: Communication & Presentation Skills for Executives (m, 2.5 CP)
• Career Module: Academic Writing Skills / Intercultural Training (m, 2.5 CP)
• Career Module: Ethics and the Information Revolution (m, 2.5 CP)

Master thesis (30 CP)

In the fourth semester, students conduct research and write a mandatory master thesis guided and supported by their academic supervisor, worth of 30 credit points.