Official course description:

Basic info last published 7/11-18
Course info
ECTS points:
Course code:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
MSc. Master
M.Sc. in IT, Digital Innovation & Management
Course semester
Forår 2019
28 January 2019
31 May 2019

The goal of the course is to make students able to manage and use data sets, e.g. by learning about tools for data interpretation and visualisation, and to reason about the use of data in larger contexts.


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 data sets, 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 covers topics such as storing and querying data in databases, describing and exploring data with visualisations inference and prediction with statistical models, business value of big data & analytics, human vs. algorithmic decision-making, privacy, surveillance and GDPR.

Formal prerequisites
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.
Intended learning outcomes

After the course, the student should be able to:

  • Analyse and discuss the technological trends that underlie Big Data
  • Analyse and discuss how organisations can use analytics to gain critical insights
  • Design, conduct and report results of analytics and visualisation in a specific case
  • Reflect upon the role of personal data in Big Data processes
  • Analyse and discuss the potential pitfalls of Big Data processes