EPN

MOKV3500 Data visualization Course description

Course name in Norwegian
Datavisualisering
Study programme
Bachelorstudium i medier og kommunikasjon / Bachelorstudium i journalistikk
Weight
15.0 ECTS
Year of study
2021/2022
Curriculum
FALL 2021
Schedule
Course history

Introduction

In this course students learn how to create data driven, interactive content for web and mobile using free open source web technology. The course includes cartography, statistics, source criticism, applied mathematics, programming and visualization. The course is suited for web journalism and other kinds of web content creation.

Recommended preliminary courses

The course does not require previous experience with programming, but participants should have an interest for data and programming. Using your own laptop and having administrative rights to this laptop is highly recommended.

Required preliminary courses

No pre requirements, but we want to inform that the course has progression provisions as described in the general part of the program plan.

Learning outcomes

Knowledge

The student knows:

  • cartography
  • correct use of different types of graphs
  • How to search public databases
  • data sources and source criticism
  • basic statistical methods

Skills:

The student can:

  • Find and refine data while maintaining the integrity of the dataset
  • Use mathematics and programming to visualize data
  • Produce user-friendly interactive maps and charts to communicate a message efficiently

General skills:

The student can

  • Analyze data from various sources
  • Produce digital stories for multiple platforms
  • Analyze works within the field
  • Communicate fact based and quantitative sources in a visual form.

Teaching and learning methods

This course has a high degree of project based activities. The course is organized as weekly seminars with practical challenges. Students will have readings and exercises to do between classes. Work will be performed individually, in groups and students will be required to have a presentation.

Course requirements

The course has three required assignments consisting of one individual assignment, one group presentation and one final assignment to be done individually or in groups.

  1. Individual assignment: Datamap. Students have to demonstrate the skills needed to make a data driven map.
  2. Group presentation: Students have to demonstrate analytical and critical skills by analyzing a data driven and visualized web product. The presentation should last about 5 minutes not including Q&A.
  3. Project plan for final exam assignment. The plan should be at least 5 pages in length including images, sketches etc. The written part should be 700-1000 words.

The assignments will be marked as pass or fail by the teacher and all assignments must be handed in within the set deadline and passed before the student can take the exam.

Students who don't get their assignments approved on their first attempt can hand in a revised version once.

Assessment

The exam consists of two parts. 

Part 1 is a group or individual project made by groups of 1-3 students. The work must include a written article with at least 2 graphics elements based on structured datasets. At least one element must use the technology d3.js. The other element can be made with any tools we have used in class. All members of the group will get the same grade grade for part 1.

Part 2 is a individual written reflection paper where the student should demonstrate the learning outcomes for the project and the course. The paper should be about 2000 words in length. The assessment of the reflection paper can result in the student’s final grade being adjusted one step up or one step down. Font and font size: Arial/Calibri 12 points. Line spacing: 1.5.

The student must pass all parts of the exam to get an overall pass. 

Permitted exam materials and equipment

All utilitites allowed as long as rules for source reference are followed.

Grading scale

Graded scale A-F.

Examiners

The exam assignment is assessed per group by two sensors. For at least 25 percent of assignments one sensor must be external.

Course contact person

Gaute Heggen