DIG 3620 Digital Data and Methods

DIG 3620 Digital Data and Methods

Course code: 
DIG 3620
Course coordinator: 
Håvard Huse
Course name in Norwegian: 
Digitale data og metoder
Product category: 
Bachelor of Digital Communication and Marketing - Programme Courses
2022 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course will teach you new methods to collect and analyze data from social media and the internet. New tools of analysis will help you uncover patterns in big data, and allow you to construct meaningful descriptions of stakeholders in the form of personas.

Deriving insights is an increasingly important success factor in today's communication environment. Measuring, analyzing, and implementing data into decision-making processes create effective and efficient PR and marketing campaigns. With societal issues increasingly occupying management's agenda, communication departments are increasingly tasked to gather and develop insights into complex problems. On the agency side, value is increasingly created through creative and strategic planning, which itself is dependent on creating insights.

In this course, the general objective is to provide students studying digital communication and marketing the adequate competence to use these insight- and data-driven methods/tools. Data is collected from online sources such as social media, blogs, forums and websites. This will give students the ability to understand audiences, to become skilled in developing persuasive and customized messaging, to select the best communication channels for messages, and ultimately, to achieve optimal results. The emphasis of the class will be on application and interpretation of the results, providing input for making real life business and communication strategy decisions. We will focus less on the mathematical and statistical properties of the techniques used to produce these results, and more on the methods used in analysis of the data itself.

Learning outcomes - Knowledge

The student should be able to:

  • …describe and compare the data generated online and in social media
  • …reflect on the quality and usefulness of data generated online and in social media
Learning outcomes - Skills

The student should be able to:

  • …describe the process of collecting data from online and social media

  • …construct personas based on data gathered and analyzed

  • …apply tools such as network analysis and other visual representations of data from social media

General Competence

The student should be able to:

  • …identify and actively avoid pitfalls of data-driven analysis
  • convincingly present findings
Course content

1. Introduction: Digital data and personas

You will learn about types of digital data and the associated opportunities and challenges these data present. You will also be familiarized with personas, the technique we will employ in presenting our findings.

2. The explorative phase in data-driven analysis

Data-driven analysis presents different challenges than traditional data collection. You will become cognizant of, and actively avoid, these weaknesses and obstacles inherent to this methodology. 

3. Data collection

You will learn to collect data from social media and the limits there of. Data from blogs, forums, and other sources will also be covered.

4. Analyses

In this part of the course, you will become acquainted with and work with practical tools. This will allow you to uncover how your topic of interest is discussed online and in social media, who the influencers are, what the networks look like, and what the trends are.

5. Presentation of insights

You will learn how to effectively create and present you findings visually in a convincing way while using personas. 

Teaching and learning activities

The course will combine formal lectures with workshops. The course will consist of the following elements: 

  • Lectures that aim to provide basic knowledge of the topics and theory
  • Tutorials and workshops on using different software solutions for analyzing and visualizing insights
Software tools

Higher Education Entrance Qualification


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.


Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

Students are expected to have taken classes in statistics and have a working knowledge of MS Excel. 

Exam category: 
Form of assessment: 
Written submission
Group/Individual (1 - 3)
1 Semester(s)
The portfolio consist of three parts:
Part 1 - description of explorative phase of project
Part 2 - description of analysis and data collection
Part 3 - poster; visualization of findings
Students will be able to receive feedback on part of the portfolio during the semester, before the complete portfolio is handed in at end of course.
Exam code: 
DIG 36201
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Portfolio assessment
Total weight: 
Student workload
39 Hour(s)
Prepare for teaching
60 Hour(s)
Group work / Assignments
80 Hour(s)
Work on the portfolio.
Feedback activities and counselling
21 Hour(s)
Read and implement suggestions from feedback on parts of the portfolio.
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.