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Kursusnavn (dansk):Critical Big Data Management with Specialisation Project 
Kursusnavn (engelsk):Critical Big Data Management with Specialisation Project 
Semester:Efterår 2015 
Udbydes under:cand.it., Digital Innovation & Management (dim) 
Omfang i ECTS:15,00 
Kursussprog:Engelsk 
Kursushjemmeside:https://learnit.itu.dk 
Min. antal deltagere:
Forventet antal deltagere:
Maks. antal deltagere:100 
Formelle forudsætninger:
Læringsmål:- Identify what data types result from various online activities and how these may be collected, stored and managed
- Describe concepts such as databases, available types, their function and their limitations
- Discuss big data processes from data collection and storage to different analytical approaches
- Analyze and critically reflect on potential pitfalls and benefits inherent in big data processes from data collection and storage to different analytical approaches
- Identify data sources, storage techniques and potential analytical approaches necessary to achieve particular goals.
- Reflect upon and articulate ethical and privacy issues involved in data collection, storage and analysis of various data types and data sources
-Based on the project work, be able to describe and discuss a master thesis synopsis 
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. 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:

Students will be engaged in a series of short exercises intended to help them develop the necessary technical skills and three group projects in collaboration with the Technical Big Data Management course students.

As part of their thesis preparation, students must find a supervisor no later than November 13th. Students must meet with their supervisor at least twice during the semester and before December 11th.
Students must have an approved thesis project agreement before start of next semester.

Deadline for approval of project agreement corrected October 6  

Obligatoriske aktivititer:Students will submit three required assignments as output from the three collaborative projects completed throughout the course in order to be eligible for the exam. 
Eksamensform og -beskrivelse:D2G Aflevering med mundtlig eksamen der supplerer projekt. Delt ansvar for projekt., (7-scale, external exam)

Hand-in for groups with following oral exam supplemented by the hand-in (D2G). The hand-in is drafted by groups of one to four students. Duration of exam: 30 minutes per student incl. assessment and feedback.  

Litteratur udover forskningsartikler:
 
Undervisere
Følgende personer underviser på kurset:
NavnStillingUndervisertypeIndsats (%)
Irina Shklovski Lektor(ITU) Kursusansvarlig 100