| Kursusnavn (dansk): | Critical Big Data Management |
| Kursusnavn (engelsk): | Critical Big Data Management |
| Semester: | Efterår 2016 |
| Udbydes under: | cand.it., Digital Innovation & Management (dim) |
| Omfang i ECTS: | 7,50 |
| Kursussprog: | Engelsk |
| Kursushjemmeside: | https://learnit.itu.dk |
| Min. antal deltagere: | 1 |
| Forventet antal deltagere: | 0 |
| Maks. antal deltagere: | 100 |
| Formelle forudsætninger: | . |
| Læringsmål: | Identify and discuss data types, data provenance and data interpretation approaches
- Discuss big data management processes from data collection and storage to different analytical approaches
- Analyze and critically reflect on potential pitfalls and benefits inherent in big data processes
- Demonstrate ability to translate theoretical and societal concerns with big data processes into practical and technical terms
- Reflect upon and articulate ethical and privacy issues involved in data collection, storage and analysis of various data types and data sources |
| Fagligt indhold: | Please note, that due to technical challenges, changes may occur before the start of the semester (week 35) – this applies to all sections of the course description.
Big data offers opportunities and risks, requiring deep technical knowledge as well as critical skills to analyse the quality and impact of any approach or solution. Making sense of data is a key challenge for many organizations, institutions and governments so that they can understand and adapt quickly to changing conditions. A hospital could incorporate GPS data about the actual location of its ambulances and helicopters with data about the mission these vehicles are involved in, as well as the emergency calls and current status in various emergency rooms in order to take decisions in real-time when faced with an emergency call (also in the face of large-scale disasters). Step counters and mobile phones can be connected with Amazon Echo voice-activated assistant to help manage family life while potentially allowing a range of vendors to ensure in-store availability of groceries and other staples the family might need when they need it. While both these scenarios evoke a future of convenience, they also raise issues about privacy and the influence such data services might have on everyday lives.
Big data denotes the processes involved in making data from various data sources available for advanced analytics. There is no longer one approach that can fit all data management problems. For each problem, IT specialists with different backgrounds must work together to decide on appropriate models and systems to handle the relevant data. In this course, we will address the critical issues that emerge in the course of collection, management, processing and analytics of large-scale data. We will discuss modern approaches to organizing and making sense of large data sets. We will cover the principles of big data analysis, and illustrate a group-based hands-on approach to big data modeling and management while addressing the increasingly important societal issues these principles and approaches address and problematize. Students will be introduced to basic quantitative methods and technical skills to be able to assess current approaches to big data management and analytics as well as critical theoretical tools for identification and discussion of potential pitfalls, obstacles and opportunities that working with data and analytics may bring up. Students will work in mixed groups with students from the Technical Big Data Management course on joint projects bringing together critical and technical skills to achieve big data management goals. |
| Læringsaktiviteter: | • In the lectures students will get acquainted with the theoretical approaches to big data types, affordances and interpretations, providing the student with critical big data terminologies.
• In the research methods workshop students will be introduced to basic quantitative research methods and more general concepts of research as practice.
• The outputs from each practical assignment project will form the foundation for the final exam. |
| Obligatoriske aktivititer: | The course includes three mandatory practical assignments with a range of different datasets and data management systems. More specifically, the required assignments are outputs of three mandatory projects where teams of DIM students will work together with SDT students from the technical big data management course. |
| Eksamensform og -beskrivelse: | C: Skriftlige arbejder uden mundtlig eksamen., (7-scale, external exam) Hand-in consisting of an individual written paper.
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| Litteratur udover forskningsartikler: | . |
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Kursusoversigt