Course Syllabus

 

Data Visualization

"Geospatial data collection for your adventures" field study. Picture by Mikayla Lin from her student blog, used with permission.

 

 

Semester & Location:	Spring 2024 - Stockholm
Type & Credits:	Elective Course - 3 credits
Major Disciplines:	Business, Computer Science, Information Science, Communication design
Prerequisites	One course in mathematics at university level
Faculty Members:	Angie Dag Hjort (current students please use the Canvas Inbox)
Program Director:	Natalia Landázuri Sáenz, PhD
Academic Support:	academics@disstockholm.se
Time & Place:	Mondays and Thursdays 10:05-11:25. Room: 1D-509

 

Course Description

Welcome to our elective course focusing on creating insightful works of visual communication. Throughout the classes and assignments we'll dive into crafting charts, diagrams, and maps. We will learn how a single well-designed visual piece can effectively present complex data-driven ideas, tell a story or entertain with its artistic touch. Such works help us grasp abstract, multidimensional concepts that might otherwise be challenging to grok. Creating such works is not a magic but rather a craft that has its roots in user-centered design. If you're keen on effectively conveying your data and ideas, mastering this craft is great to have in your arsenal. On the other hand, poorly designed visuals, as we will also explore, can confuse or mislead. Learning from bad examples will arm you against the BS in our data-driven world. 

The work done in this course is intended to serve as a portfolio for the future careers the students choose for themselves. The course is for everyone interested in extracting insight from data through visual analysis, regardless of their intended field of expertise. Let's make cool charts!

Course flexibility

This course was created very flexible to support the diversity of the class, as the students come from different walks of life, academic majors, technical skills and professional intentions. If you've ever been to a Swedish buffet dinner you've seen how there is a large selection of foods (smörgåsbord), and you would pick the ones you like till you get full. Full of knowledge and skills for making data graphics in our case. Students are expected to build their own plates of homework assignments, online tool-learning tutorials, active participation in 24 live sessions and 2 field studies. There is also an actual pizza dinner planned in this course, not a metaphorical one.

We will use the tools ranging from pens and papers to the advanced software used in business analytics to programming libraries used by leading professionals in data science, generative art and data communication.

Knowing how to code is not required, as you can pick up the necessary coding skills on the way or opt for using no-code tools for your course projects. That said, students who will chose to try any coding will be encouraged, supported and rewarded for the struggle.

Values

Curiosity: have a thing you want to learn? Let's discuss, it could qualify for one of the assignments.
Honesty: overslept the class, no need to come up with an excuse, just say like it is.
Respect: attack the work, not the author. Assume good intention. Use steelman argument.
Collaboration: most exercises can be done in pairs, even quizzes. Help each other.
Engineer's mentality: identify problems and search for solutions. If it's made by a human, then you, another human, can understand and apply/improve/fix it. Simplify complex systems using "model thinking".
Doing brain-heavy things is cool. Like math and stuff.
Practicing feminism: Examine Power, Challenge Power, Elevate Emotion & Embodiment, Rethink Binaries & Hierarchies, Embrace Pluralism, Consider Context and Make Labour Visible
Taking pride in what you do. One good work is better than 3 half-ass works. If you don't care about your work why should i?
Authenticity
Being stoked on life, remembering to celebrate

Learning Objectives

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

Design competence

Lean to create data graphic works that facilitate actionable insight for their intended users

• 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 visualizations
• 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 visualization based on feedback from user testing
• Develop individual visual language
• Choosing the correct tool: Learn to assess the need and usefulness of different tools to aid working with data and visualizing it

Defense competence

Defend your design choices through deep domain knowledge

• Learn to build an argument in support of one or the other design

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 visualization 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 visualizations 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

Reading chapters and research papers are assigned as needed in certain quizzes and homeworks. The field is constantly evolving, so there is no fixed list. The lectures of this course cover selected chapters and sometimes provide a compressed summary of the following books:

 

Lecture slides are published immediately after each session. If i forget to publish please make noise! Link to lecture slides is in the course home page

Software and tools

Students will need to bring their own laptops 

Students will need to use smartphones in mobile mapping workshop and where geographic data collection is needed

Additionally, it is sometimes helpful but not required to have a personal digital pad with a drawing pen and software

Software recommendations: this is just for reference at this point. You will encounter things when you need them in assignments.

For drawing on a pad: Procreate has worked well in the past. You may find a free alternative too.

For data preparation and easy graphing: Excel and Google spreadsheets

For working with csv files: Sublime text + Advanced CSV plugin or ModernCSV

For geographic data collection: OSMAnd

For assignments with maps: Mapbox studio 

No-code tool for data visualisation: Tableau public

Low-code tool for data visualisation: Observable plot

Coding library: D3.js

Code editors: Observable

Collaboration platform: Observable

 

Field Studies

The course offers 2 field studies

• Geospatial data collection for your adventures
  We do a 5-7km hike in the forest and learn to use mobile mapping tool OSMAnd
• A study visit to Visualization Studio VIC at KTH (Royal Institute of Technology)
  We attend an immersive universe-exploration experience in stereo glasses, learn about interactive view transformations and play with the video games made by KTH master students in computer graphics

Approach to Teaching

We will use various learning methods, including interactive lectures, class discussions, online video tutorials, case studies, field studies, and project-based learning to build a final project. There is however not a lot of required reading in this course. Many live sessions combine active learning with short presentations from the lecturer and from students as well. Students will have to seek themselves and take online tutorials in order to build their tool-using skills. The meta-skill of knowing where to look for information and how to practice the craft without detailed instructions is a large part of this course. Using this knowledge, students will create their own visualizations and provide constructive peer feedback to each other. We will have short quizzes to discuss the reading material. The pace and specific activities will vary depending on the interest of the students. Although it is hard to tailor the course for each student individually, there are elective assignments that would fit some of the major learning styles. 

Expectations of the Students

• Students should participate during lectures, peer-led oral presentations, discussions, group work and exercises.
• Students are expected to think critically, ask relevant questions in regards to the material covered and be proactive: pull the knowledge out of the teacher's head instead of teacher trying to push that knowledge into the students' heads.
• Laptops may be used for note‐taking, fact‐checking, or assignments in the classroom, but only when it helps the engagement with the course material being currently covered. At all other times laptops and electronic devices should be put away during class time.
• Students need to be present, arrive on time and participate to receive full credit. The final grade will be affected by absences and lack of participation (see the green and blue sections of the point system). 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. Assume good intention of your opponent, find common shared values, use the steel-man argument technique.

Evaluation and Grading 

Throughout the course students collect points, which are then converted into grades. The points are awarded for completion of surveys and quizzes, individual and group assignments, attendance and active participation in class. And most importantly for the progression in skills along 7 different tracks. Make sure you read the tracks description in the "Assignments" section.

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

Assignment	Points
⚪️ Surveys	1
🟢 Attendance	28
🔵 In-class optional participation	5
🟠 Individual assignments (side quests)	23
🟤 Learning with suggested online tutorials	19
🔴 Big course project in groups (main quest)	21
🟣 Reading quizzes	15
⚫️ Skill tracks	38
sum	150

 

The final grade is calculated based on the accumulation of points. If a student gets at least 90 points that's an A- already. Conversion from points to grade % is a simple one to one. Anything in the range of 100–150% grade is a pass with unofficial distinction.

Note that students are not required to do every single assignment in this course. It so happens that due to a very high diversity among student study majors, intentions and skills there is no single exam test or a set of assignments that would fit every student. Hence the point system, which allows a large degree of flexibility. Students are expected to build their own learning tracks through the course and pick the assignments useful to them.

Some 40% of the assignments are still mandatory, as indicated by * in the list of assignments. Doing every single assignment is not advised, it is better to do fewer at a higher quality and try to get good points for those.

 

percentage, at least	points, at least	GRADE
150%	150	maximum possible amount of points for the course from the table above
100%	100	A+ (unofficial distinction, Linked-in recommendation)
93%	93	A
90%	90	A-
87%	87	B+
83%	83	B
80%	80	B-
77%	77	C+
73%	73	C
70%	70	C-
67%	67	D+
63%	63	D
60%	60	D-
below 60%	below 60	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 can be reduced by 10% for each day the submission is late.

 

From previous experience: a student who attended most of the classes, did all assignments with fair diligence, and participated at an average level of effort would get a B in this course. B+ would signify a modestly higher quality of assignments or level of participation and when both were present it landed at A-. Hardworking students who demonstrated interest in the subject and growth of skills in multiple tracks got a solid A. Example

Academic Regulations

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

• Course Enrollment and Grading
• Attendance
• Coursework, Exams, and Final Grade Reports

 

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