Instructor Information

Jesus Caban

Work Phone: 301-319-3759

Dr. Caban is the Chief of Clinical and Research Informatics at the National Intrepid Center of Excellence (NICoE) at Walter Reed National Military Medical Center (WRNMMC). He manages a team of scientists, engineers, researchers, and developers that provide enterprise applications, business intelligence, machine learning, metrics, data, and analytical support to the NICoE Intrepid Spirit Network across 10+ military treatment facilities (MTFs).

Trained as a computer scientist and with over thirteen years of experience in inter-disciplinary clinical and informatics research, he has authored over 50 scientific papers related to clinical informatics, machine learning, artificial intelligence, visualization, and predictive models.

During the last few years he has served in different roles including the Chair of the DHA Enterprise Intelligence and Analytics IPT, the Chair of the American Medical Informatics Association (AMIA) Visual Analytics working group, and the Vice Chair of the 2016 IEEE Visualization conference.

In 2018, he received the Defense Health Agency (DHA) Innovation Award and the Association of Military Surgeons of the United States (AMSUS) HealthIT award for his contribution to the Military Health System (MHS). In 2019, the TBI Portal – one of the enterprise applications that he has managed since its inception -- received the Federal HealthIT Innovation award.


He's committed to educating and training our next generation in informatics, ML/AI, and data science. He's an adjunct faculty at the Uniform Services University (USUHS) where he mentors PhD students interested in clinical informatics and he's also an adjunct faculty at Johns Hopkins University - Applied Physics Lab where he teaches two of the core classes of the Data Science graduate program. 

Websites: 

https://www.linkedin.com/in/jesuscaban/ 

Richard Takacs

Course Information

Course Description

This course explores the underlying theory and practical concepts in creating visual representations of large amounts of data. It covers the core topics in data visualization: data representation, visualization toolkits, scientific visualization, medical visualization, information visualization, flow visualization, and volume rendering techniques. The related topics of applied human perception and advanced display devices are also introduced. Prerequisite(s): Experience with data collection/analysis in data-intensive fields or background in computer graphics (e.g., 605.667 Computer Graphics) is recommended.

Course Goal

To learn practical concepts of graphic design related to data visualization and interactive design. This course will explore the underlying theory and practical concepts in creating visual representations of heterogeneous data. It will cover the core topics in data visualization including data representation, design principles, color, interaction, network visualization, cartography, volume rendering, and visual analytics. The new knowledge acquired during the semester will be used to solve practical data visualization and visual analytics problems.

Course Objectives

  • By the end of the course, students will be able to:

    1. Describe the foundations of the human visual perception and how it relates to creating effective information visualizations.

    2. Understand the design principles for creating effective visualization tools

    3. Evaluate different visualization techniques and identify potential misleading charts and visualizations

    4. Demonstrate familiarity of the visual design process by developing interactive data visualization tools

    5. Understand different data types including tabular, hierarchical, geospatial, textual, and scalar and related

      visualization techniques to each data type

    6. Show familiarity of existing data visualization tools and programming libraries

When This Course is Typically Offered

Course is offered every fall and spring semeter.  

Syllabus

  • Introduction to Data Visualization
  • Introduction to Visualization Techniques
  • Human Visual Perception
  • Visualization Design Principles
  • Color in Visualization
  • Interactive Visualization
  • Trees, Graphs, and Network Visualization
  • Maps and Cartography Visualization
  • Text Visualization
  • Temporal Visualization
  • Scientific Visualization
  • Scientific Visualization II
  • Isosurfaces and Flow Visualization
  • Display Systems and Evaluation

Student Assessment Criteria

Class Preparation and Participation 20%
Course Projects 50%
Final Project 30%

The course will consist of 14 modules. Each module will include a video lecture, corresponding slides, reading assignment, and an online discussion. The grading of this course will be based on:

Item

Class Participation 20%
Course Projects 50%
Final Project 30%

Projects are due according to the dates posted in your Blackboard course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. I will post grades 1-2 weeks after assignment due dates. The code (if any) for your programming assignments must be submitted and should include enough documentation so we can read your code and understand the approach you are following.

In written sections and the final paper, we generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

Final grades will be assigned according to the following scale:

100–98 = A+ 97–94 = A 93–90 = A− 89–87 = B+ 86–83 = B 82–80 = B− 79–70 = C <70 = F

Computer and Technical Requirements

Students should be able to develop basic software applications in modern programming languages (e.g. Python, R, or Javascript) and should be familiar with the basic concepts of data processing and statistics.

Textbooks

Textbook information for this course is available online through the MBS Direct Virtual Bookstore.

Course Notes

There are notes for this course.

(Last Modified: 07/09/2021 02:28:34 PM)