Skip to content

Latest commit

 

History

History
168 lines (141 loc) · 6.06 KB

syllabus.md

File metadata and controls

168 lines (141 loc) · 6.06 KB
layout title
page
Syllabus

Schedule Always, forever in progress...

Week 1 28-Jan-14

  • Introductions to each other.
  • Introduction to Data Epistemology
    • True vs. False Data
    • Objective vs. Subjective
    • Anecdotal vs. Empirical
    • Data as aesthetic of Truth
    • Visualization as Analog
  • Introduction to Class Tools
    • Git, Blog, etc.
  • Homework 1 and Project 1 Assigned

Week 2 4-Feb-14

  • Discuss Vilém Flusser Reading
    • Who is Vilém Flusser?
    • What insight does he provide for today's critique?
  • Project 1 Presentations
    • "How do we critique these?"
    • "How do we make them better?"
    • Review Critique Terms List
      • Term
  • Github / Blog Review
  • Mini Markdown Tutorial
  • YAIP - Yet Another Introduction to Processing
    • Data formats.
    • Data parsing.
    • Data structures.
    • Data analysis.
    • Output to PDF / Vector.
  • In class work with simple structured data set.

Week 3 11-Feb-14

  • Discuss Barthes Reading
  • Data Collection / Sampling
    • Basic Probability / Statistics Review
      • Probability
        • What is Probability?
        • "Law of Large Numbers"
      • Statistics
        • What is a "statistic"?
        • Population vs. Sample
        • Descriptive vs. Inferential
        • Distributions
          • Histograms
          • Center
          • Mean
          • Median
          • Mode
          • Expected Value
          • Spread
          • Variance
          • Standard Deviation
      • etc.
    • How do we analyze non-numerical data?
      • Observed features become numbers
        • Frequency, count, rates, qualities (number of red hens)
      • Standard static numerical analysis can then be applied.

Week 4 18-Feb-14

  • Big Data
    • What is it?
    • Why "big data" now?
    • Dense vs. sparse (metadata, etc)
    • Is "big data" more objective or just truthier (a million data points can't be wrong!)?
    • Rewriting histories (archive)
    • Big Data set examples
    • Big data set art / design / visualization examples.
  • Strategies for Dealing with Big Data in Processing
    • Memory Management
    • For loop optimization
    • Database use
      • sqlite
      • MySQL
      • MongoDB
  • Big Data Analysis
    • Correlation
    • Regression
    • Prediction
  • Assign Big Data Readings
  • Assign Big Data Project 2
    • Multidisciplinary teams are provided with a large data set (e.g. all Bitcoin transactions to date)
    • Analyze Data
    • Create their own algorithms to process/visualize it in order to achieve conceptual and/or aesthetic expression.

Week 5 25-Feb-14

  • Big Data II - Data mining
  • Data repression / abstraction
  • "Metadata"
    • What can you possibly learn from metadata?

Week 6 4-Mar-14

  • workshop big data projects
  • take stock of preparations for submission

Week 7 11-Mar-14

  • Big Data Project Critiques
  • Gather ideas for midterm projects

Week 8 18-Mar-14

  • Live Data
    • What is "real-time"? Why are we attracted to "real-time"?
  • Real-time data analysis
    • Running average
    • Running standard deviation
    • Global minima / maxima
    • Local minima / maxima
  • Processing
    • Real-time data analysis in Processing
      • Networked data streams
  • organic / connectivity / presences
  • Visualizing networks
  • "News" - "immediacy"
  • Project 3 - Live Data Project (Midterm) assigned
    • Students will choose a live data stream (Vine, Twitter), and create a screen-based live visualization of that data. Flash mobs?

Week 9 25-Mar-14

  • workshop Live data projects

Week 10 1-April-14

  • Midterm
  • Live Data Project Critiques
  • Outlining final projects
  • Gather ideas for final project
  • Workshop of final project ideas and work groups
  • Teams, data, everything is solved and everyone is in a group by the end of class?

Week 11 8-April-14

  • Present images / proposals of final projects to class
  • Enjoy Bagels and Coffee

Week 12 15-April-14

  • Enjoy Bagels and Coffee

Week 13 22-April-14

  • Enjoy Bagels and Coffee
  • In process Critiques

Week 14 29-April-14

  • CRIT WEEK, No Classes

Week 14 6-May-14

  • Final presentations & Critiques

The Art of Data Visualization VCD / ATS: 3032

Spring, 2014 Jan 28 — May 6, 2014, Tuesdays 9:00 AM - 4:00 PM, MacLean 917

Instructors: Alan Rhodes & Christopher Baker

In a studio workshop environment, an interdisciplinary co-taught team will focus on the design, development and creation of data visualization projects. Projects will be organized around conceptual areas including: Maximal & Cryptic Data (visualizing the impossible, invisible and incomprehensible), "Useful" Data (examine the idea of "useful" vs "not useful" data visualizations), Process Data (such as crowdsourcing, online collaboration and the work of artists like Aaron Koblin), Provocative Data (data and data gathering as both material and conceptual artistic content), Material Data (using data to inspire and generate abstract visual or sculptural forms), Translatable Data (translating data between medium(s) e.g. time to space, space to time, data to sound, sound to data, etc), Lonely and Hidden Data (learning what data to ignore and how to reveal hidden data). Contemporary platforms will be workshopped; these may include: Processing, D3 (http://d3js.org/), RGB+D, ManyEyes, Mondrian, ImageJ, and ImagePlot.

Students do not have to have had experience with specific coding; the course will seek to create a studio environment in which different techniques and backgrounds can be used to realize projects and, as well, teams of students with diverse skills formed. But, that said, a basic familiarity with the coding process (Java, HTML, other) will be very advantageous.

The instructional team, Christopher Baker (ATS) and Alan Rhodes (VCD), will draw on a diversity of skills and backgrounds in technology, design, video installation, interactive media, gallery, public, and internet art.