CSCI-P 583 Data Visualization
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
- Semesters offered: Spring (Check the schedule to confirm.)
- Equivalent(s): CSCI 55200 Data Visualization
Description
This course covers the theory, design, and application of scientific and information visualization, including algorithm design and implementation. Students learn to represent multidimensional data using computer graphics and other techniques, enabling users to interact with it. Projects span biomedical data analysis, scientific and engineering simulations, and visual web data mining.
Topics
Introduction and visualization design
- Theory, design, and application
- Scientific and information visualization
- Algorithm design and implementation
D3 programming tutorial
- D3 basics
- Application of D3
Multidimensional data visualization
- Computer graphics techniques
- User interaction
- Visual representation
- Interaction techniques
Hierarchical data visualization
- Representation and interaction
- Biomedical data analysis
Graph and network visualization
- Visual representation
- Scientific and engineering simulations
Geovisualization and interaction
- Representation
- Interaction techniques
Text data visualization
- Techniques
- Visual web data mining
Scientific visualization
- Basics
- Scalar algorithms and marching cube algorithm
- Data preprocessing and exploration
- Integration with existing data analysis pipelines
Volume rendering
- Volume rendering techniques
- Transfer function and vector field visualization
Critique and evaluation
- Assessing visualization techniques
- Analyzing perceptual and cognitive principles
Visualization innovation
- Developing comprehensive visualization solutions
- Creating interactive user interfaces
Learning Outcomes
- Assess various visualization techniques and algorithms for representing complex multidimensional data sets, identifying strengths, limitations, and trade-offs in different contexts. CS 4
- Analyze the perceptual and cognitive principles underlying effective visualization design, evaluating their impact on user comprehension, insight extraction, and decision-making processes. CS 1
- Evaluate the suitability of specific visualization approaches for different data types and research questions, justifying your choices based on the data characteristics and domain requirements. CS 7
- Critique existing visualization designs' effectiveness, clarity, and user experience, proposing refinements or alternative strategies to enhance communication and visual aesthetics. CS 6
- Formulate evidence-based assessments of the impact of visualizations on decision-making procedures in scientific, engineering, and biomedical contexts, considering factors such as accuracy, insight gain, and user engagement. CS 4
- Develop comprehensive visualization solutions that seamlessly integrate with existing data analysis pipelines, ensuring efficient data preprocessing, interactive exploration, and insightful representation. CS 2
- Innovate and implement interactive user interfaces that facilitate intuitive navigation, dynamic querying, and meaningful interaction with visual indications, contributing to enhanced user engagement and exploration of multidimensional data. CS 2
- Develop novel visualization techniques to address complex challenges in scientific data analysis, engineering simulations, or web-based data mining, employing computer graphics concepts and algorithmic innovations. CS 7
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.