Preliminary public information for, subject to change:
Preliminary info last published 18/03-19

Big Data Processes

Course info
Language:
English
ECTS points:
7.5
Course code:
KBBIDAP1KU
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
Master of Science in Information Technology (Digital Innovation and Management)
Staff
Course semester
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
Abbreviation
20201
Exam
Abstract

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.

Description

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.

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
Ordinary exam
Exam type:
D: Written report with oral defence, external (7-trinsskala)
Exam variation:
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted. The group has a shared responsibility for the content of the report.
Exam description:

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.