Course Syllabus

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Data Visualization

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Semester & Location:

Fall 2022 - Stockholm
Type & Credits:

Elective Course - 3 credits

Major Disciplines:

Business, Computer Science, Information Science
Prerequisites

One course in mathematics at university level
Faculty Members: Angie Dag Morozov (current students please use the Canvas Inbox)
Program Director:

Natalia Landázuri Sáenz, PhD
Academic Support:

csc-engr@disstockholm.se
Time & Place:

Mondays and Thursdays 10:05-11:25
Room: 1D-411  Learning Lab

Course Description

Visual representations of vast and complex arrays of data can change the way we understand and respond to everyday life. Interactive maps of the USA presidential election have allowed us to track vote counting and predict potential outcomes in real time. Visualizations of changes in temperature over time in relation to production of greenhouse gases and natural events have allowed us to understand human impact on climate change and simulate potential future scenarios. Genomic data visualizations continuously help to unveil DNA mutations directly related to certain diseases. Using a hands-on approach, this course utilizes computational tools to transform raw data into interactive visual representations that are easy to access, understand, analyze and reflect upon with the goal to provide insight and augment the cognitive capacity of both domain experts and lay people.
In this course, students will learn theory and state-of-the-art design techniques to visually extract insight from data. Students will learn the required technical skills to create powerful interactive visualizations using the most power tools in the visual analytics industry such as Tableau and Qlikview. This course is an introduction to the field that does not require programming knowledge. The course is for everyone interested in efficiently extracting insight from data through visual analysis.

Learning Objectives

The goal of the course is that students would develop the below listed competences and have fun while doing it:

Design competence

Collaborate as groups to create fully-functioning information visualization systems that facilitate actionable insight through interactive data transformations, visual mappings, and view transformations.

  • Know the visualization pipeline: raw data --> data models --> visual structures --> view transformations --> analysis and insight
  • Learn to respect the reader: How to design for a target audience and user tasks
  • Know how the human vision works
  • Learn to work with color
  • Know principles of graphic design and human perception
  • Learn the basics of data science
  • Learn how to prepare the data for use in visualisations
  • Develop skills in data modeling and curation 
  • Understand morals and ethics of data in acquisition and use.
  • Master data mapping to visual structures
  • Learn the grammar of graphics
  • Learn how to design interactive view transformations using software tools
  • Learn to iteratively redesign your visualisation based on feedback from user testing
  • Develop individual visual language

Defence competence

Defend your design choices through deep domain knowledge

  • Choosing the correct tool: Learn to assess the need and usefulness of different tools to aid working with data and visualizing it

Critique competence

Constructively criticize other information visualization systems using domain theory and practices.

  • Know how the data can be misused and how to avoid deception
  • Know the ways charts may lie and develop skills in calling BS on charts

Demonstration competence

Explain and demonstrate your information visualization systems to wide audiences, from novices to experts.

  • Develop speaking skills in front of an audience
  • Learn how to publish your work online
  • Develop skills in storytelling with data

Evaluation competence

Know what makes a data visualisation good and be able to assess how good the works of others are

  • Learn to perform testing with users: how to facilitate the testing and how to take notes

Faculty

Angie is a d3.js data graphics developer and head of software at Gapminder Foundation in Stockholm, Sweden, where he is responsible for the various software efforts around project gapminder.org/tools and developing the interactive data pictures. Previously built data-intense visualisations for oil platform safety monitoring (ABB Research, Sweden), user interfaces for online payment aggregator (Robokassa, Russia), control systems and operator user interfaces for the ore processing factory (Realtime Software, Russia). Holds academic degrees of M.Sc. in Human computer interaction with a minor in Innovation and Entrepreneurship (graduated 2014 from KTH, Sweden and Aalto, Finland), M.Eng. Industrial Automation and Control Systems (graduated 2011 from URFU, Russia). With DIS since 2022.

Readings

Books

  1. Introduction to information visualization, Riccardo Mazza - Read chapter 1, Introduction to Visual Representations. Entire book.
  2. Information Visualization, in Handbook of Human Factors and Ergonomics, ch43 - Chris North. Chapter 43.
  3. Ware, Colin. Information visualization: perception for design. Morgan Kaufmann, 4th ed., 2020. (Chapters 2 - 4)

Papers

  1. The Eyes Have It, a paper by Ben Shneiderman. It is in Chapter 8 of The Craft of Information Visualization: Readings and Reflections by Bederson, Benjamin B and Ben Shneiderman, 2003
  2. The Challenge of Information Visualization Evaluation by Catherine Plaisant. AVI '04: Proceedings of the working conference on Advanced visual interfaces, May 2004 Pages 109–116 
  3. Other papers may be assigned as needed

 

Software tools to be used in the course (preliminary)

Students will need to bring their own laptops (preferably) or be able to access the computers on campus

Excel and Google spreadsheets

Sublime text + Advanced CSV plugin or ModernCSV

Recommended for digital souvenir assignment: Mapbox studio and OSMAnd

Recommended for the course big project: Tableau public (optional alternatives Qlikview, Infogram, ChartBlocks)

If you chose to use D3 or Vega — more power to you. You will be encouraged and supported.

D3 Example Image 

Example of an interactive visualization: the relation of Marvel characters in movies. Source: Nadieh Bremers Block

 

Field Studies

Possible visits can include:

Approach to Teaching

We will use various learning methods, including interactive lectures, class discussions, critical analysis of reading material, field studies, and project-based learning to build a final project. All sessions combine active learning with short presentations from the lecturer and from students as well. We will learn through exercises with existing visualizations. We will have analytical tasks and present results for critical and constructive feedback. Furthermore, students will create their own visualizations and provide constructive peer feedback to each other. We will have short quizzes to discuss the reading material for each session. The pace and specific activities planned for certain days may change depending on the interest of the students.

Expectations of the Students

  • Students should participate during lectures, peer-led oral presentations, discussions, group work and exercises.
  • Laptops may be used for note‐taking, fact‐checking, or assignments in the classroom, but only when indicated by the instructor. At all other times laptops and electronic devices should be put away during class time.
  • Reading must be done prior to the class session. A considerable part of the class depends on class discussions.
  • Students need to be present, arrive on time and participate to receive full credit. The final grade will be affected by unexcused absences and lack of participation. The participation grade will be reduced by 10 points (over 100) for every unexcused absence. Remember to be in class on time!
  • Classroom etiquette includes being respectful of other opinions, listening to others and entering a dialogue in a constructive manner.
  • Students are expected to ask relevant questions in regards to the material covered.

Evaluation and Grading 

To be eligible for a passing grade in this class, most of the assigned work must be completed, but not all. The course is packed with assignments, large and small. Many require multiple days to collect the data. The grading system allows you to not do all of the assignments and still ace the course. See the grading system and plan ahead what to do and what you can skip.

All assignments come to a total of 115 points, but for example if you get at least 101 that's an A- already

percentage, at least points, at least
97% 109 A+ (unofficial level)
93% 104 A
90% 101 A-
87% 97 B+
83% 93 B
80% 90 B-
77% 86 C+
73% 82 C
70% 78 C-
67% 75 D+
63% 71 D
60% 67 D-
below 60% 67 F

 

Students are expected to turn in all the assignments on the due date. If an assignment is turned in after the due date, the grade of the assignment will be reduced by 10% for each day the submission is late.

The factors influencing the final grade and the proportional importance of each factor is shown below:

Assignment

Points

Percentage

Active participation in classes and in-class exercises

23

20%

Reading quizzes

20

17%
Individual assignments 32 28%

Course big group project 

40

35%

Academic Regulations  

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:

 

DIS - Study Abroad in Scandinavia - www.DISabroad.org

Course Summary:

Date Details Due