Data Engineering Technologies (Online)

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The Data Engineering Technologies curriculum is divided into four semesters and takes two years to complete. Each semester is composed of core, methods, and foundation modules, leading to a master thesis that may be conducted in collaboration with an industry partner. The modules are grouped into four areas, including Core (45 CP), Methods (22.5CP), Foundations (22.5 CP) and Master Thesis (30 CP), as outlined in the Schematic Study Plan (see Figure 1). Detailed module descriptions in their latest version are available in the catalogue on CampusNet. The default module size is 7.5 CP, with smaller 2.5 CP modules being possible as justified exceptions, e.g., if the learning goals are more suitable for 2.5 CP and the overall student workload is balanced.

Semester 1

• Big Data Challenge for DET
• Data Analytics
• Database Management Tools in Python
• Mathematics for Graduate Students

Semester 2

• Ethics and the Information Revolution
• IT Law
• Data Security and Privacy
• Statistical and Machine Learning
• Advanced Databases
• Text Analysis and NLP

Semester 3

• Data Acquisition Technologies
• Image Processing
• Parallel and Distributed Computing
• Visual Communication and Data Storytelling

Semester 4

• Master Thesis

The program aims to provide an in-depth understanding of the essential aspects of data-based decision-making and the skills required to apply and implement these powerful methods successfully and responsibly. Apart from the necessary programming skills, this comprises:

• Methods of data acquisition both from the internet and from sensors;
• Methods to efficiently store and access data in large and distributed databases;
• Statistical model building including a wide range of data mining methods, signal processing, and machine learning techniques;
• Visualization of relevant information;
• Construction and use of confidence intervals, hypothesis testing, and sensitivity analyses;
• Know the legal foundations of data engineering;
• Methods to ensure data security and privacy;
• Awareness of the societal and ethical implications of digitization;
• Scientific qualification;
• Competence to take up qualified employment in Data Engineering;
• Competence for responsible involvement in society;
• Personal growth.

By the end of the online program students will be able to:

1. Critically assess and creatively apply technological possibilities and innovations driven by big data;
2. Acquire data from sensors and use microcontrollers to process data and transmit them to databases on servers or the internet in general;
3. Set up and use databases to efficiently and securely manage and access large amounts of data;
4. Apply statistical concepts and use statistical models in the context of data analytics;
5. Use, adapt and improve visualization techniques to support data-based decision-making;
6. Design, implement and exploit various representations of data for classification and regression including supervised machine learning methods and core ideas of deep learning;
7. Apply and critically assess data acquisition methods and analytical techniques in practical scenarios in organizations and companies;
8. Independently investigate complex problems and undertake scientific or applied research into a specialist area utilizing appropriate methods, also taking methods and insights of other disciplines into account;
9. Professionally communicate their conclusions and recommendations, the underlying information and their reasons to both specialists and non-specialists, both clearly and unambiguously based on the state of research and application;
10. Assess and communicate social, scientific and ethical insights that also derive from the application of their knowledge and their decisions;
11. Engage ethically with the academic, professional and wider communities and actively contribute to a sustainable future;
12. Take responsibility for their learning, personal development, and role in society, evaluating critical feedback and self-analysis;
13. Adhere to and defend ethical, scientific and professional standards.

The demand for Data Engineers is massive. Typical fields of work encompass the finance sector, the energy sector, the automotive and health industry as well as retail and telecommunications. Companies and institutions in almost every domain need:

• Experts for data acquisition who find out how to collect the data needed;
• Experts for data management who know how to store, enhance, protect and process large amounts of data efficiently;
• Experts for data analysis who evaluate and interpret the collected data correctly and can visualize the findings.
• Graduates of the program work as data engineers, data analysts, data managers, data architects, business consultants, software and web developers, or system administrators;
• An MSc degree in Data Engineering Technologies also allows students to move on to a PhD and a career in academia and research institutions.

The Career Service Center (CSC) helps students in their career development. It provides students with high-quality training and coaching in CV creation, cover letter formulation, interview preparation, effective presenting, business etiquette, and employer research as well as in many other aspects, thus helping students identify and follow up on rewarding careers after graduating from Constructor University. Furthermore, the Alumni Office helps students establish a long-lasting and global network which is useful when exploring job options in academia, industry, and elsewhere.