Official course description:

Full info last published 15/05-22
Course info
Language:
English
ECTS points:
7.5
Course code:
BADIDAA1KU
Participants max:
75
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
Bachelor
Programme:
BSc in Digital Design and Interactive Technologies
Staff
Course manager
Associate Professor
Course semester
Semester
Efterår 2022
Start
29 August 2022
End
31 January 2023
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

The objective of the course is to learn how to analyze and visualize quantitative data produced by digital platforms. The course is also intended to be an introduction to the R language for data analysis and to the basics of the Grammar of Graphics (as implemented in the R package ggplot) for data visualization.

Description

Quantitative data analysis is an essential part of user research and it can provide  valuable understanding  of users behavior. Information gathered through digital data is going to be more and more relevant with data analytics playing a central role in any type of business intelligence. In the larger context, students need a versatile toolbox for exploring users’ behavior, to develop a representative understanding of users’ practices. This toolbox needs to cover qualitative and quantitative methods for collecting and analyzing data about user behavior and practice. The course is divided into three sections: data analysis, data visualization, analysis of network data.  

The course will provide an introduction to the R programming language for statistical analysis. It will also introduce specific R packages for data visualization (ggplot) and for the analysis of network data (igraph).

The course will cover:

  1. Digital Data, types, format and data structures
  2. Introduction to R
  3. Data visualization with R/ggplot
  4. Graphs and network analysis for social media data.

Formal prerequisites

This  course is a 3rd semester course on the BSc Digital Design and Interactive Technologies. Students are expected to be familiar with quantitative methods and descriptive statistics.

Intended learning outcomes

After the course, the student should be able to:

  • Use R to analyze and visualize quantitative data produced in a variety of contexts (e.g. surveys, social media, location data)
  • Use the R package ggplot to visualize data
  • Produce interactive visualizations of the data
  • Visualize interaction happening on social media through network analysis
  • Understand the data visualization produced
  • Reflect on the consequences of data visualization
Learning activities

14 lectures + 14 exercise sessions with ad-hoc exercises.

During the course the students will: 

  • Use R to analyze and visualize quantitative data produced in a variety of contexts (e.g. surveys, social media, review data, lovation data)
  • Use the R package ggplot to visualize data
  • Visualize interaction happening on social media through network analysis
  • Understand  the process of data visualization and how to explore data with it
  • Reflect on the consequences of data visualization

Course literature

The course will be based on selected chapters of "R for Data Science" by Hadley Wickham and Garret Grolemund, available online: https://r4ds.had.co.nz/


Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 20%
  • Lectures: 30%
  • Exercises: 30%
  • Exam with preparation: 20%
Ordinary exam
Exam type:
C: Submission of written work, Internal (7-point scale)
Exam variation:
C1G: Submission of written work for groups
Exam submission description:
The students will work in small groups (2-3 people) and present a report analyzing a dataset provided by the teacher. The students will have the opportunity to choose between multiple datasets, but they will have to perform a fixed set of data analysis mapped on the ILOs.
Group submission:
Group
  • 2-3

Time and date