Specialisation Data Management and Analytics

About

In the interdisciplinary context of Data Science and Big Data, the Data Management and Analytics specialisation covers scalable and distributed data management systems, information retrieval and data mining algorithms, as well as information visualisation and human-data interaction techniques. You will study algorithms, techniques, architectures and methods for the management, processing and interaction with both structured and unstructured data. The acquired theoretical knowledge will be applied in the design of applications that extract insights from streams as data such as for instance produced by Internet of Things devices. 

In Detail

The Data Management and Analytics specialisation within the 2-year MsC in Computer Science covers the interdisciplinary aspects of interactive data science and big data management, including scalable and distributed data management systems, information retrieval and data mining algorithms as well as information visualisation and human-data interaction techniques. Our goal is to prepare students for the future challenges in managing and analysing the rapidly growing amounts of data that is produced manually by humans as well as automatically generated by, for example, sensors in emerging Internet of Things solutions, data capturing on the Web or as an outcome of scientific experiments. Thereby, we focus on the scientific aspects and concepts for scalable data management solutions, information retrieval and data mining as well as different information visualisation and interaction techniques rather than on existing mainstream technologies, and provide students the necessary education for a future career as data scientists and data engineers.

As a student in the Data Management and Analytics specialisation, you will study, design and develop big data solutions for the storage, processing, analysis and interaction with big and complex data. Thereby, a special focus is on the system aspects of solutions for big data management and analytics. You will study specialised systems and algorithms for the development of data-intensive applications at scale that store their data in a distributed manner as, for example, used by players such as Google or Facebook as well as needed for future applications in the emerging field of the Internet of Things. You will further learn machine learning and information retrieval techniques to search for information in large collections of documents (e.g. as applied in Google's search algorithms) and study the theory as well as practical aspects for the automatic detection of structure in big and complex data. This knowledge about methods and techniques for the machine-based analytics of large datasets will play a major role in future business solutions since it is not enough to capture the data without having the tools and skills to further process, analyse and get new insights from these datasets. Given that many analytical tasks cannot automated, you will also learn about state-of-the art information visualisation and interaction techniques which keep the human in the loop, augment the human capabilities and enable the exploratory analysis of big and complex datasets. You will further learn how to design interactive visualisation solutions for the presentation of known data-supported facts to support decision making processes as well as the delivery of existing knowledge.

 

Data Management and Analytics is a specialisation in our 2-year MsC in Computer Science of 120 ECTS. Students of this specialisation need to succeed for the carefully designed core of 30 ECTS that is common to all four specialisations, the 24 ECTS of four mandatory courses within this specialisation, at least 6 ECTS of electives within the specialisation, an additional 30 ECTS of electives from this or any of the other specialisation, and for a research training of 6 ECTS and a master's thesis of 24 ECTS.

The following is the list of mandatory courses:

Scalable Data Management Systems (foundational, 6 ECTS, sem 1)
In the course Scalable Data Management Systems we study specialized systems and algorithms developed to support data-intensive applications at scale. The course takes a principled approach and covers aspects from distributed databases, MapReduce derivatives, and other relevant systems. Course topics include data partitioning, distributed query planning, and scalable transaction management.
Information Retrieval and Data Mining (foundational, 6 ECTS, sem 2)
Information Retrieval covers the essential theory and practice of automated document search, explaining the technology that drives, for example, Google. It is related to Data Mining, the theory and practice of automatically understanding the structure in data. This combined course covers a range of topics from practical issues such as how to index very large bodies of data space-efficiently, to how to detect the significance of words and other forms of meaning in structured and unstructured documents.
Information Visualisation (foundational, 6 ECTS, sem 2)
In this course students learn about the representation (abstraction) and presentation of data in terms of different visualisation techniques supporting the exploratory analysis for scientific discovery as well as the design of tools for the presentation of large datasets. The theory further covers specific elements of human perception and colour theory and we discuss different design principles and interaction techniques for human-in-the-loop data exploration, underlined by various case studies. The theory is applied and further deepened in a group assignment where interactive visualisation solutions are designed and implemented for different big and complex datasets.
Advanced Topics in Big Data (foundational, 6 ECTS, sem 2)
In this seminar, we review and discuss research papers in the domain of big data through which the student gains knowledge about the recent developments in the fields of big data and its underlying technologies with a focus on data management, retrieval and human-data interaction as well as the corresponding human-in-the-loop data management aspects. Students are assigned a research paper and learn how to critically analyse the assigned material, as well as how to formulate their evaluation by writing scientific reports and giving oral presentations.

Data Management and Analytics is a specialisation in our 2-year MsC in Computer Science of 120 ECTS. Students of this specialisation need to succeed for the carefully designed core of 30 ECTS that is common to all four specialisations, the 24 ECTS of four mandatory courses within this specialisation, at least 6 ECTS of electives within the specialisation, an additional 30 ECTS of electives from this or any of the other specialisation, and for a research training of 6 ECTS and a master's thesis of 24 ECTS.

The following is the list of electives within this specialisation. 

Cloud Computing and Big Data Processing (deepening, 6 ECTS, sem 1)
Cloud Computing and Big Data Processing is an advanced two-part course that introduces the foundational concepts and techniques of these fields, and motivates and illustrates their use within related frameworks and tools. Topics for Cloud Computing include security, consistency, reactivity, and decentralization (blockchain). Big Data Processing topics include cluster computing, fault tolerance, and data locality, partitioning and streaming. Knowledge of JavaScript and Scala is a plus.
Statistical Foundations of Machine Learning  (deepening, 6 ECTS, sem 2)
Machine Learning is applicable to many real-world tasks, and mainly consists of learning correlations in data, or between inputs and outputs. This course explains all the fundamental aspects of the most common Machine Learning approaches, to allow the students to perfectly understand how to design well-behaving Machine Learning systems, accurately measure their performance, and use the result of the learning procedure as efficiently as possible. This course also explains how various algorithms work, such as least-squares regression, neural networks or decision trees.
Advanced Databases (deepening, 5 ECTS, sem 1)
This course covers advanced concepts from the database domain which facilitate the integration of databases in object-oriented and distributed software systems, such as active databases which replicate data and maintain its consistency. The course also discusses the management of non-relational non-relational data types, such as temporal, graph, and spatial data. The course covers the theoretical foundations as well as their application in innovative domains.
Database Systems Architecture (deepening, 5 ECTS, sem 1)
From searching on the web to booking a hotel: modern applications inherently need to store, process, and retrieve data. The goal in this course is to get a fundamental insight into the implementation aspects of systems designed to manage and process large amounts of data. Our objective in this respect is two-fold. (1) To gain the background required to design and implement future data management and processing systems and (2) to gain an understanding of how performance of practical data management systems can be tweaked.
Next Generation User Interfaces  (deepening, 6 ECTS, sem 1)
After attending the course on Next Generation User Interfaces, the student has an understanding of the interaction principles introduced by new devices such as smartphones, multi-touch tables or gesture-based interfaces as well as the theoretical background behind these interaction principles. The student is able to reflect on the qualities and shortcomings of different interaction styles, while placing the user at the core of the interface design process. The theory is applied in a group project where students design and develop their individual next generation user interface.

Data Management and Analytics is a specialisation in our 2-year MsC in Computer Science of 120 ECTS. Students of this specialisation need to succeed for the carefully designed core of 30 ECTS that is common to all four specialisations, the 24 ECTS of four mandatory courses within this specialisation, at least 6 ECTS of electives within the specialisation, an additional 30 ECTS of electives from this or any of the other specialisation, and for a research training of 6 ECTS and a master's thesis of 24 ECTS.

Click here to consult the official program overview for the complete list of electives.

The following elective can only be chosen with the approval of the examination commission. It is intended as a boosting course for students who did their bachelor at an institution where there was less emphasis on web technologies.

Web Technologies (boosting, 6 ECTS, SEM1)
In this course, we investigate the origins of hypermedia and the World Wide Web and discuss current and future developments on the Web. We have a detailed look at the architecture of the Internet and various protocols such as the Hypertext Transfer Protocol (HTTP) as well as HTML5, JavaScript, CSS3, Web 2.0, the Semantic Web, web search, and security and privacy. The theory is applied in various exercise sessions as well as in a rich internet application that is developed as part of the course.