Exploring Data: Visualization

Summary:

Visualization of data has become a mainstay in everyday life. Whether reading the newspaper or presenting viewgraphs to the board of directors, professionals are expected to be able to interpret and apply basic visualization techniques. Technical workers, engineers and scientists, need to have an even greater understanding of visualization techniques and methods. In general, though, the basic concepts of understanding the purposes of visualization, the building block concepts of visual perception, and the processes and methods for creating good visualizations are not required even in most technical degree programs. This course provides a “Visualization in a Nutshell” overview that provides the building blocks necessary for effective use of visualization.

What you will learn:

  • Decision support techniques: which type of visualization is appropriate
  • Appropriate visualization techniques for the spectrum of data types
  • Cross-discipline visualization methods and “tricks”
  • Leveraging color in visualizations
  • Use of data standards and tools
  • Capabilities of visualization toolsThis course is intended to provide a survey of information and techniques to students, giving them the basics needed to improve the ways they understand, access, and explore data.

Course Outline:

  • Overview
    • Why Visualization?
      • The Purposes for Visualization: Evaluation, Exploration, Presentation
  • Basics of Data
    • Data Elements – Values, Locations, Data Types, Dimensionality
    • Data Structures – Tables, Arrays, Volumes
    • Data – Univariate, Bivariate, Multi-variate
    • Data Relations – Linked Tables
    • Data Systems
    • Metadata – Vs. Data, Types, Purpose
  • Visualization
    • Purposes – Evaluation, Exploration, Presentation
    • Editorializing – Decision Support
    • Basics – Textons, Perceptual Grouping
    • Visualizing Column Data – Plotting Methods
    • Visualizing Grids
      • Images, Aspects of Images, Multi-Spectral Data
      • Manipulation, Analysis, Resolution, Intepolation
    • Color – Perception, Models, Computers and Methods
    • Visualizing Volumes – Transparency, Isosurfaces
    • Visualizing Relations – Entity-Relations & Graphs
    • Visualizing Polygons – Wireframes, Rendering, Shading
    • Visualizing the World – Basic Projections, Global, Local
    • N-dimensional Data – Perceiving Many Dimensions
    • Exploration Basics – Linking, Perspective and Interaction
    • Mixing Methods to Show Relationships
    • Manipulating Viewpoint – Animation, Brushing, Probes
    • Highlights for Improving Presentation Visualizations
    • Color, Grouping, Labeling, Clutter
  • Tools for Visualization
    • APIs & Libraries
    • Development Enviroments
      • CLI
      • Graphical
    • Applications
    • Which Tool?
    • User Interfaces
  • A Survey of Data Tools
    • Commercial
    • Shareware & Freeware
  • Web Browser-based Visualization
    • Intro –Why Visualize on the Web
    • Data Driven Documents D3.js: Web Standards: Foundation of D3 (HTML, SVG, CSS, JS, DOM)
    • Demos and Examples
    • Code Walk-through
    • Other Web Tools
    • Demos and Coding
    • Walk-throughs

Scheduling:

This course is not on the current schedule of open enrollment courses. If you are interested in attending this or another course as open enrollment, please contact us at (410)956-8805 or at ati@aticourses.com and indicate the course name and number of students who wish to participate. ATI typically schedules open enrollment courses with a lead time of 3-5 months. Group courses can be presented at your facility at any time. For on-site pricing, request an on-site quote. You may also call us at (410)956-8805 or email us at ati@aticourses.com.

Instructors:

  • Ted Meyer is currently a data scientist at the MITRE Corporation with a 30 year interdisciplinary background in visualization and data analysis, GIS systems, remote sensing and ISR, modeling and simulation, and operation research. Ted Meyer has worked for NASA, the National Geospatial-Intelligence Agency (NGA), and the US Army and Marine Corps to develop systems that interact with and provide data access to users. At the MITRE Corporation and Fortner Software he has lead efforts to build tools to provide users improved access and better insight into data. Mr. Meyer was the Information Architect for NASA’s groundbreaking Earth Science Data and Information System Project where he helped to design and implement the data architecture for EOSDIS.

  • Ivan Ramiscal is a lead software systems engineer at the MITRE Corporation specializing in data visualization, the development of sentiment elicitation and analysis tools and mobile apps. He worked closely with the University of Vermont Complex Systems Center’s Computational Story Lab to design and develop the sentiment analysis tool Hedonometer.org; he co-invented and created the SpiderView sentiment elicitation system, and teaches data visualization development with D3 and Ruby at the MITRE Institute.

    Contact these instructors (please mention course name in the subject line)

Request On-Site Quote