This is a specialization of the University of Twente’s Master’s programme Computer Science.
Information technology is becoming increasingly embedded in our society. This also means that it is getting easier to collect more data about ourselves and our environment. The science concerned with the ‘discovery’ of information from large volumes of unstructured data is called ‘data science’. It has high practical relevance, as the generation and application of information is an important economic activity. Data science techniques can be used in information systems to maintain an information model of the dynamic environment, e.g. based on real-time sensor data. These information models, in turn, can be used to offer tailored services to the users in the environment. Isn’t that smart?
The Data Science & Technology specialization at the University of Twente provides basic courses for understanding data science, smart services, and how these fields are related through modern information systems. It also offers advanced courses that address the challenges of this cross-disciplinary field, including big data processing, real-time analytics, information quality, and information system and service design.
What is Data science & Technology?
Services are a powerful abstraction of technical and business systems that facilitate the use of such systems without knowledge of their internal workings. The service concept is widely used for understanding and building complex technical systems in many areas.
An important class of services is that of information systems. Information systems capture, transmit, store, retrieve and display information. They support processes in business organizations and daily activities of human users. These information systems may comprise many subsystems that are geographically distributed and without centralized control. As such, they can be considered as a network of loosely coupled services, each corresponding to a subsystem and contributing to some target composite service. Information systems in today's organizations deal with large volumes of data, including structured data, sensor data, multimedia data, and geographic data. Managing these large volumes, aggregating data from different sources and extracting useful information are increasingly strategic capabilities for these organizations. Such capabilities have only become possible with the development of new techniques and tools. These techniques and tools are produced by what nowadays is known as the data science field.
The challenges of data science
Data science tackles the challenges of big data, real-time analytics, data modeling and smart information use. Scientific and economic progress is increasingly powered by our capabilities to explore big data sets. A key challenge of data science is to use big data sets of varying quality that are readily available, instead of small datasets that require careful, manual work. As a student participating in the DS3 track, you will work with data created every hour, minute, second and millisecond, rather than data that require (laborious) manual annotation and manual cleaning. These big data sets are typically acquired by the unobtrusive monitoring of large populations of users in an everyday setting – rather than by monitoring small groups of carefully selected subjects in a laboratory setting. Data acquired by unobtrusive monitoring can be used in information systems to make a variety of smart services possible, based on real-time data analytics, complex event processing and context-aware process adaptation. The methodological challenges of big data analysis and smart services come with a number of technical challenges, and the need for developing new methods, models and tools. The challenges are:
- To process datasets that are too big to be handled by a single machine or by traditional tools within a reasonable amount of time;
- To process streaming data for real-time monitoring and tracking of events and real-time identification of trends;
- To extract reliable conclusions and models from unreliable data, and from data integrated from multiple sources of varying quality;
- To combine the above in smart services that bring added value to end users at the right time and at the right place.
combination of scientific fields
The Data Science & Technology Master’s specialization at the University of Twente connects the important fields of data science and smart services via information systems. With data science, you will learn how to dig for value in data by analyzing various data sources. With smart service engineering, you will learn how to design services that effectively use system capabilities to satisfy dynamic user needs and requirements. Information systems that can use the results of data science to get more value out of data may turn current services into smart services. Already, we can see many applications of this in pervasive health, well-being, compliance management, intelligent transportation, logistics, business intelligence etc.
distinguishing with other programmes
The Data Science & Technology Master’s specialization at the University of Twente distinguishes itself from similar specializations at other universities by offering:
- A unique combination of expertise in computer science, data science, and service science;
- Collaboration with leading international companies, like Google, Twitter and Yahoo;
- A local infrastructure for the analysis of very large datasets, accessible to students;
- Challenging big data and data analytics applications in smart services for pervasive health, logistics, and other areas.