Charts and graphs have an indisputable aura of objectivity and yet, much like statistics, they have an immense power to either elucidate or mislead. What does it mean for an information graphic to be ‘honest’ with its data? And how can we as designers (and citizens) know when a representation is telling the ‘whole story’? In this course we will deal with the nuts and bolts of collecting & processing data and explore different ways of communicating its meaning in a quantitatively rigorous and visually engaging way.
Assignments will involve the use of scripting, databases, and other numerical tools to transform data into something that is understood rather than simply ‘seen’. Students are encouraged to consider data sources from their surroundings or the larger world and to break away from the screen-based status quo, eschewing the expected line graphs, pie charts, and tables in favor of unconventional visualizations of their own devising.
Objectives
- to give students an understanding of the process of acquiring, analyzing, refactoring, and visualizing data
- to develop an understanding of the building-blocks of visual data representation (bar charts, scatter plots, network diagrams, etc.), know when each is appropriate, and learn to avoid their associated pitfalls
- to discuss the epistemological issues raised by being an irrational primate attempting to make systematic sense of an unverifiable world
- to establish Hypothesis Testing as a working method for developing visual explanations and discovering the ‘story’ within a dataset
Prerequisites
- basic bitmap, vector, and (potentially) video-editing skills
- familiarity with statistical reasoning (mean, median, sorting, normalization, etc.)
- facility with a scripting language/data visualization library (d3, SciPy, R) or other data analysis tool (Mathematica, MATLAB, Excel)
- not required but helpful: knowledge of databases, server-side programming, interaction design, and animation (or audio)
Readings
Regular readings will be assigned covering both formal and conceptual issues involved in data science. We will discuss the readings in class in relation to the current assignment and each other’s coursework. Each student must submit 3 questions to the class website before 8 a.m. the day of class.
These questions will act as prompts for the in-class discussion, so anything that can be answered with a ‘yes’ or ‘no’ is probably not up to snuff. The questions should not be questions for the instructor but are intended for your fellow students. You must come to class prepared to discuss the texts.
Assignments
There will be 4 multi-week projects over the course of the semester. Though there will be a ‘final’ crit in class at the end of each of these, you may continue working on them throughout the semester. Your final grade will be based on the versions you turn in at the last class meeting on December 16th.
Presentations
Each student will give a presentation on an artist, designer, or technical topic (visualizations tools, algorithms, etc.) during the course of the semester. Presentations should be about 15 minutes long in the format of your choice (slideshow, website, mini-lecture). Students will also each participate in a group 'book club' discussion where they will collectively present slides and ideas from a book they all read.
You must submit an online summary of your presentation as part of your class documentation by the final day of class. This PDF or online summary should cover the main points of your presentation as well as including appropriate visuals and links to resources or additional information on your topic.
Presentation topics (and dates) will be chosen in the second week of class. One of your presentations must be drawn from a provided list of options while the other will be a topic of your own devising.
Grading
Research presentations: 15%
Participation in discussions & readings: 20%
Assignments 1–3: 40%
Final project: 25%
Attendance
All unexcused absences will adversely affect your grade. Three such absences will earn you an instant F.
Final-grade Archetypes
- Frequently late and/or absent, insufficient participation, little to no understanding of formal and quantitative practices: F
- Occasional lateness and more than one unexcused absence, basic understanding of subject matter: D
- Occasional lateness, demonstrated an understanding of subject matter, failed to take risks, work holds together, makes only obligatory contributions to discussions: C
- Always present, work in on time, demonstrated a solid understanding of subject matter, was able to seek out new design principles and technological approaches, work has good form and content (and took some risks), able to make interesting contributions to the class: B
- Always present, work in on time, demonstrated a solid understanding of data visualization, was able to explore new approaches, work has excellent form and content which took major risks, always makes interesting contributions to the class and frequently led class discussions: A
Additional information detailing Pratt Institute-wide academic policies can be found on the course Policy page