DIG 3620 Digital Data and Methods - RE-SIT EXAM
DIG 3620 Digital Data and Methods - RE-SIT EXAM
The course was last completed in the spring of 2022. A re-sit examination will be offered in the autumn of 2022, and the last time in the spring of 2023.
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.
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
The student should be able to:
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…describe the process of collecting data from online and social media
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…construct personas based on data gathered and analyzed
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…apply tools such as network analysis and other visual representations of data from social media
The student should be able to:
- …identify and actively avoid pitfalls of data-driven analysis
- …convincingly present findings
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.
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
Higher Education Entrance Qualification
Covid-19
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.
Teaching
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.
Students are expected to have taken classes in statistics and have a working knowledge of MS Excel.
Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 100 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) Comment: Term paper Exam code: DIG 36201 Grading scale: ECTS |
Activity | Duration | Comment |
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Teaching | 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. |
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.