Digital Data Analysis (Autumn 2020)

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, 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

Course literature

The course literature is published in the course page in LearnIT.

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:
A: Written exam on premises, Internal (7-point scale)
Exam variation:
A11: Written exam on premises. Open book exam.

Time and date