Data Analysis and Visualization

Summary

In the course, students advance their knowledge and understanding of qualitative and quantitative methods and methodologies for data analysis. The course focuses on the use of data and visualization techniques to analyze complex phenomena and to build models to support research or decision making related to media technology.

Admission requirements

  1. Bachelor degree in media technology or in a related field with a minimum 180 European Credit Transfer System credits. Examples of related fields include: computer science, informatics, information systems, human-computer interaction, interaction design, media/IT management, game development, media and communication science and digital cultural studies.
  2. English proficiency equivalent to English 6 from a Swedish upper secondary school

Syllabus

Syllabus for students autumn 2022, autumn 2021

Course Code:
ME659E revision 1
Swedish name:
Dataanalys och visualisering
Level of specialisation
A1N
Main fields of study:
Media Technology
Language:
English
Date of ratification:
20 June 2019
Decision-making body:
Faculty of Technology and Society
Enforcement date:
30 August 2021

Entry requirements

  1. Bachelor degree in media technology or in a related field with a minimum 180 European Credit Transfer System credits. Examples of related fields include: computer science, informatics, information systems, human-computer interaction, interaction design, media/IT management, game development, media and communication science and digital cultural studies.
  2. English proficiency equivalent to English 6 from a Swedish upper secondary school

Specialisation and progression relative to the degree regulations

The course is part of the main area of study media technology and can be included in the degree requirements for a Degree of Master of Science in Media Technology (120 credits).

Purpose

In the course, students advance their knowledge and understanding of qualitative and quantitative methods and methodologies for data analysis. The course focuses on the use of data and visualization techniques to analyze complex phenomena and to build models to support research or decision making related to media technology.

Contents

  • qualitative and quantitative methods for data-driven analysis, such as data mining, network analysis, content analysis and digital ethnography,
  • processing and analysis of various forms of data,
  • data visualization and modelling,
  • statistical concepts and principles,
  • tools for data-driven analysis and visualization.

Learning outcomes

Knowledge and understanding
By the end of the course students should be able to:

  • describe applications, challenges and opportunities with different forms of data collection and analysis in digital environments,
  • describe how the data source and choice of method affect the possibilities for, and the quality of analysis.
Competence and abilities
By the end of the course students should be able to:
  • relate theoretical concepts to applied problems,
  • plan and perform qualitative and quantitative analyses in digital environments based on scientific and strategic objectives,
  • identify and apply different principles and tools for collecting, analyzing and visualizing data,
  • present and critically discuss data analyses and the consequences of methodological choices and conditions.
Evaluation abilities and approach
By the end of the course students should be able to:
  • argue for how different methods and methodologies can allow for a more complex understanding of phenomena in digital environments,
  • justify the choice of methodology, taking into account both concrete problems and broader scientific perspectives,
  • critically discuss applications of data-driven analysis with respect to ethical, integrity and social issues.

Learning activities

The course consists of lectures, seminars, workshops, laboratory and project work.

Assessments

Grading is based on oral and written examination, including active participation in seminars and project work.
The course is assessed based on the following:

  • Seminars (2.5 credits, UG)
  • Lab (2.5 credits, UA)
  • Written assignments (5 credits, UA)
  • Project (5 credits, UA)
For a pass (A-E): At least grade E on lab (2.5 credits), written assignments (5 credits) and project (5 credits) and grade G on seminar participation is required. Course grade is calculated based on the weighted average of assessed parts.

Grading system

Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U).

Course literature and other teaching materials

  • Jewitt, C. (Ed.). (2017). The Routledge handbook of multimodal analysis. New York: Routledge.
  • Kirk, A. (2016). Data Visualization. Los Angeles: SAGE Publications.
  • Rogers, R. (2019). Doing Digital Methods. Los Angeles: SAGE Publications.
  • Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press.
These will be additional readings.

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

Interim rules

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

Other Information

The syllabus is a translation of a Swedish source text.

Contact

The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information

Application

30 August 2021 - 07 November 2021 Day-time 100% Malmö This course is offered as part of a program

29 August 2022 - 06 November 2022 Day-time 100% Malmö This course is offered as part of a program