IT-Universitetet i København
  Tilbage Kursusoversigt
Kursusnavn (dansk):Big Data Processes 
Kursusnavn (engelsk):Big Data Processes 
Semester:Forår 2018 
Udbydes, Digital Innovation & Management (dim) 
Omfang i ECTS:7,50 
Min. antal deltagere:15 
Forventet antal deltagere:
Maks. antal deltagere:90 
Formelle forudsætninger:Foundations in Development of IT or similar.

This course is part of the specialisation in Big Data and is intended for students in their second or third semester. We strongly recommend that DIM students do not begin a specialisation in their first semester. 
Læringsmål:After the course the students should be able to:

1. Analyse and discuss the technological trends that underlie Big Data
2. Analyse and discuss how organisations can use analytics to gain critical insights
3. Design, conduct and report results of analytics and visualisation in a specific case
4. Reflect upon the role of personal data in Big Data processes
5. Analyse and discuss the potential pitfalls of Big Data processes 
Fagligt indhold:Businesses as well as governmental and non-governmental organisations increasingly employ processes for collecting, storing, managing, and analysing big data. Such big data processes, based on the discovery of meaningful patterns in large datasets, can be used to explain complex phenomena or to build predictive models about human behaviours. In this class, we will review the technological trends that underlie the advent of big data and engage in hands-on big data processes, ranging from the collection of data to extracting insights from it. Furthermore, we will discuss the economic potentials of big data processes and their limitations from technical, organisational, and ethical points of views. 

The course comprises of 12-14 weeks of teaching consisting of lectures and exercises.

The lectures will focus on gaining a theoretical and methodological understanding of Big Data processes by reading and discussing relevant literature. In addition, you will be introduced to demonstrations and worked examples of analytics and visualization. Moreover, we will also reflect upon the epistemological, ethical, and political premises and consequences of Big Data practices in different societal sectors.

During the exercises you will work in small groups on hands-on tasks. The problems can revolve around a specific type of analytics or visualization (e.g., exploratory data analysis, classification, clustering), a specific application domain (e.g., marketing, financials, transportation), or a particular challenge of Big Data processes (e.g., handling of personal data). Furthermore, you will practice communicating and presenting your results and reflections during the exercises in order to prepare for the oral exam.

During the course you will learn to apply a number of software tools for analytics and visualization, such as Tableau and R. 

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:D2G Aflevering med mundtlig eksamen der supplerer projekt. Delt ansvar for projekt., (7-scale, external exam)

Reports are handed in by groups and students are examined as a group and evaluated on the basis of demonstration of fulfilment of the intended learning objectives for the course.

The hand in should be 15 pages + 2 pages per group member. Each group should have 3-5 members.

Duration of the exam: 30 minutes per group incl. assessment and feedback

Form of group exam: Group exam
See Study Guide -> Exams -> Course Exams -> Exam Forms for more information.  

Litteratur udover forskningsartikler:Textbook:
- Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.

Recommended additional books:
- Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
- Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. Norton & Company.
- Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.