DMST 3900

Data Visualization - Spring 2012

Catalog Description:

This course explores the creation of informational graphics for visual unpacking of relationships within and among data sets. Students learn to visualize large data sets as a means of revealing and exploring patterns of information. Creating interactive visualizations are also covered, allowing for deep and participatory engagement with information. The resulting mediums include print and web. PREREQ: DMST 2100 or instructor approval. 4.000 Credit Hours

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Contact information

Associate Professor Chris Coleman

Office: Room 216A Sturm Hall

Office Hours: Chris - Tuesdays and Thursdays 12pm-2pm or by appointment


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Course Overview

From its beginnings in the simple bar graph, data visualization has evolved into one of the best ways to help people understand complex sets of data. Properly formulated, visuals can unpack information so that people from across disciplines can relay and share ideas without first needing to learn discipline-specific vocabularies and processes. Long the domain of data heavy sciences, visualizations have now begun to help us see patterns in languages, identify the flow and changes of cultural shifts, and even come to better understand the complex networks of media organizations. The course will expose students to a wide spectrum of data visualizations and discuss how they become useful in communicating ideas and concepts. The course will cover both static and dynamic interactive visualizations. We will team up with a community not-for-profit partner to do some applied work, creating visualizations for their use so that they can better understand how they are serving their communities.

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Course Objectives

By the end of this course you will:

  • Think critically about data visualization
  • Have knowledge of the history and future of data visualization
  • Consider all facets of commercial and creative data visualization in developing your work
  • Have working knowledge of Processing as a tool for data viz creation
  • Understand the process of planning and executing data viz work
  • Understand working with large and small datasets
  • Create meaningful content laden visualizations

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  • The required text for this course is "Visualizing Data: Exploring and Explaining Data with the Processing Environment" by Ben Fry. It is available in the DU Bookstore
  • You will need at least 8GB of portable storage (ipods, portable hard drive, etc.)
  • You must have or purchase a sketchbook approx. 8.5"x11"
  • Other reading materials and examples will be supplied digitally on this website

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This class will combine individual work in the lab with individual and group instruction. Students must come to class prepared to work. Showing up without necessary files or equipment is the same as not attending. Although students may also use their home computers to work on projects, this is not a valid reason to not attend. It will be necessary to work outside of class to complete all projects and assignments. A minimum of eight hours per week of work outside of class is suggested to get an average grade of a C. Computer failure, equipment malfunction, and file corruption are not accepted as excuses for late or unfinished work so BACK UP YOUR WORK. The computer labs are used by many students, so the labs are in high demand. Budget time accordingly as "unavailable computer time" will also not be accepted as an excuse. Participation in all class discussions and critiques as well as constructive use of lab time is considered in the final grade for each project. At any time in the creation process students should be able to produce notes, drawings,charts etc from their sketchbooks, as well as discuss and articulate the nature of their work to their peers as well as to the instructor.

Attendance is mandatory. Attending class is the responsibility of the student. Lectures and demonstrations may be given or changed without notice and every class will start with professional examples of relevant work so punctuality is essential. An individual who is absent, late or sleeps during class will be responsible for getting the information missed. Students will be allowed two (2) absences without penalty. Any absence in excess of two will result in a 10% grade reduction of the final grade for the course per absence. All absences will be counted. A student who misses 15 minutes or more of a class (late or leave early) is considered absent. A student who sleeps will be considered absent. A student who will acquire absences due to a University sponsored activities must provide necessary documentation from the appropriate office prior to the absence to make any special arrangements for missed work.

For any absence due to religious beliefs, written notification should be provided in the first two weeks of the quarter; the student is responsible for any missed work. Any special medical or personal problems that occur, where absenteeism will exceed the allowed two, will require verification by a physician or emergency medical association (a letter from Student Affairs merely explains an absence, and will not qualify as an excuse). These situations may require course withdrawal or "Incomplete" status on the final grade. Six absences mandate an automatic grade of "F." Three late arrivals (less than 15 min.) will equal one absence.

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Grades will consist of the following:

  • Assignments @ 40%
  • Reading Responses @ 10%
  • Project 1 @ 20%
  • Project 2 @ 20%
  • Participation @ 10%

Graduate Students will be required to submit additional Assignments, more complex Projects, and additional Reading Responses as part of their grade calculation.

Projects and assignments will be graded on the following basis, listed in order of importance.

  • Development, creativity and originality of concept or problem solution
  • Technical development and demonstration of skills
  • Craftsmanship and presentation of work
  • Participation in classroom discussions and critiques in connection with the work

Your grade will be calculated according to the following standards:

  • A = Excellent (100-90%)- work pushes far beyond the project stipulations and shows clear evidence of extreme time, dedication, care and thought about the project as evidenced in effective execution of original/thoughtful ideas.
  • B = Good (80-89%)- work exceeds the basic criteria, provides creative solutions to the problems and shows technical proficiency. Student has made the project "theirs" in that they do not need to explain project stipulations before showing the work.
  • C = Average (70-79%)- work fulfills all requirements, does not expand on techniques shown in class, ideas are close derivations of popular culture.
  • D = Unsatisfactory (60-69%)- work might meet basic criteria but in a careless and/or thoughtless way. Technical proficiency is rudimentary and no chances were taken.
  • F = Failure (0-59%)- the work does not meet the basic criteria.

Late projects will be penalized a letter grade for every class period they are late. Turning a project in after the beginning of the critique counts as one class day late.

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Lab Rules

It is your responsibility to adhere to all rules regarding the use of the DMS labs and equipment. You will be given a sheet stating all rules. Please see Elizabeth Harris in the DMS office if you need a form to access the DMS lab.

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Solutions to assignments you submit will be your own work. A student who is discovered to have plagiarized another's work will immediately receive a grade of F for the course, and a recommendation for disciplinary action will be forwarded to the Dean of Students.

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While you are not required to purchase the software that we are using, not having the software is no excuse for failing to complete your projects. It is your responsibility to work out times when you can use the DMS labs or to make other arrangements for doing your work. Please do not download and/or install trial versions of this software or any other onto campus computers. Processing is freely available and available for all major operating systems. You will also do some work in Adobe Illustrator and Photoshop.

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Course Outline

(The following schedule is open to revision at any point in the quarter.)

03/27 - Introduction, Intro to Data Viz

03/29 - Data Mapping, Assignment 1

04/03 - Data over Time, Assignment 2

04/05 - Data Connections, prepping Data, Assignment 3

04/10 - Project 1 assigned, DOM visit

04/12 - Real world Data, scatterplot

04/17 - Present ideas and initial plots for Project 1

04/19 - post processing techniques

04/24 - Work Day

04/26 - One-on-One Pre-Crit

05/01 - Project 1 Due

05/03 - basic interactive viewing modes, Assignment 5

05/08 - Reading responses, adding GUIs Assignment 6

05/10 - Project 2 assigned, shifting data sets

05/15 - Present ideas for Project 2

05/17 - Shifting Modes

05/22 - Packaging for web or stand-alone apps

05/24 - Tips and tricks, work day

05/29 - Work Day

05/31 - Project 2 Pre-Crit

06/09 - Project 2 Due: Digital Delivery

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- Lecture One

- Lecture Three

- Lecture Four

- Lecture 11