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Kursusnavn (dansk):Critical Big Data Management: Second Part of Specialisation 
Kursusnavn (engelsk):Critical Big Data Management: Second Part of Specialisation 
Semester:Efterår 2018 
Udbydes under:cand.it., Digital Innovation & Management (dim) 
Omfang i ECTS:15,00 
Kursussprog:Engelsk 
Kursushjemmeside:https://learnit.itu.dk 
Min. antal deltagere:15 
Forventet antal deltagere:
Maks. antal deltagere:100 
Formelle forudsætninger:This course constitutes the second part of the specialisation in Big Data. Only students who have followed the first part, Big Data Processes, can take the course. 
Læringsmål:After the course, the students should to be able to;
- Identify and describe data sources, storage and cleaning techniques and potential analytical approaches necessary to achieve particular goals.
- Analyze and critically reflect upon big data processes from data collection, storage and cleaning to basic analytical approaches
- Reflect upon and articulate ethical issues involved in management, cleaning and analysis of various data types and data sources
- Apply the basic relevant requirements from the General Data Protection Regulation to practical problems in big data management projects
- Demonstrate a basic understanding of legal reasoning about fairness, accountability and transparency in big data management infrastructures
- Translate and apply critical theoretical concepts to practical problems in big data management
- Reflect upon the societal issues linked to big data management more broadly 
Fagligt indhold: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. Just a few years ago most data could be extracted, transferred and loaded into data warehouses where it could be analyzed off-line. Traditional relational approaches to data management could fit all forms of problems. Today, large volumes of data are being captured online, through sensors, outside the scope of traditional database systems. Making sense, possibly in real-time, of this data is a key challenge for many organizations, institutions and governments so that they can understand and adapt quickly to changing conditions. For example, 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). At the same time, availability of low-cost sensing and network connectivity technologies drives a myriad technical innovations affecting every aspect of life through data-based services. For example, step counters and mobile phones can be connected with Amazon Echo voice-activated assistant to help manage healthy living for a family 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.

There is no longer one approach that can fit all data management problems. For each problem, IT specialists have to decide on appropriate models and systems to handle the relevant data. Big data denotes the processes involved in making data from various data sources available for advanced analytics. 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 introduce modern approaches to organizing and making sense of large, fast growing and diverse data sets. We will cover the principles of big data analysis, and illustrate a 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 technical skills necessary for assessment of 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. 
Læringsaktiviteter:

The course will be based on lectures, discussion sessions and practical assignments with a range of different data sets and data management systems. More specifically, the class relies on three projects where students from the Critical Big Data Management Course will work together with students from the Technical Big Data Management Course offered on the SDT program to produce joint output that will form the portfolio portion of the written exam submission.

Students are required to submit three reports as output from the three collaborative projects completed throughout the course. These reports will form the portfolio submitted as part of the exam.

As part of their thesis preparation, students must work to find a supervisor before the end of the semester. Students must have a draft of a thesis project agreement before the end of the semester. 

Obligatoriske aktivititer:Der er ingen obligatoriske aktiviteter. Vær venlig KUN at ændre denne tekst når der er obligatoriske aktiviteter./
There are no mandatory activities. Please, change this text ONLY when there are mandatory activities. 
Eksamensform og -beskrivelse:C: Skriftlige arbejder uden mundtlig eksamen., (7-scale, external exam)

The examination consists of written work. The exam includes an individually written final paper on a chosen topic and a portfolio consisting of the group output of the mandatory assignments.  

 
Undervisere
Følgende personer underviser på kurset:
NavnStillingUndervisertypeIndsats (%)
Irina Shklovski Lektor(ITU) Kursusansvarlig 55
Christopher Gad Lektor, Linjeleder(ITU) Underviser 15
Katja de Vries Postdoc(ITU) Underviser 30
Oliver Kirsten Meinved Hjælpelærer(ITU) Hjælpelærer 0