DATA VISUALIZATION

Course Description

This course provides a comprehensive understanding of transforming data into visuals by introducing participants to important principles of analytical design and practical data visualization techniques for the exploration and presentation of univariate and multivariate data. Data visualization is covered as one of the most effective tools to explore, understand, and communicate patterns in quantitative information. The course provides a broad understanding of techniques and algorithms of turning data into readable visuals. Upon completion of the course, students learn about data visualization processes including data modeling, data aggregation and filtering, mapping data attributes to graphical attributes, and visual encoding. Students also learn to assess the effectiveness of different visualization designs, and critically evaluate each design decision.

DATA VISUALIZATION

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Reference Books & Course Materials

1. 01 Tamara Munzner, Visualization Analysis and Design, First Edition, CRC Press, 2014.
2. 02 Edward R. Tufte, The Visual Display of Quantitative Information, Second Edition, Graphics Press, 2001.

Learning Outcomes

1. L01 Explain Tufte’s criteria for visualization aesthetics and criticize flawed graphics in terms of data-ink ratio, lie factor and chartjunk. SOLO 4.5
2. L02 Describe the human visual system and explain how perceptual priors shape the interpretation of a visual stimulus. SOLO 3.5
3. L03 Classify datasets, items and attributes by dataset type, data type and semantics, and differentiate structured from unstructured data. SOLO 3.5
4. L04 Select appropriate marks and channels for given attributes and construct the corresponding visualizations in Python. SOLO 3.5
5. L05 Design geospatial visualizations and evaluate the suitability of map projections, choropleths, proportional symbol maps and cartograms. SOLO 4.5
6. L06 Analyze network and hierarchical data and compare explicit and implicit tree layouts such as treemaps, sunburst and icicle plots. SOLO 4
7. L07 Apply filtering, aggregation and clustering techniques and implement text visualizations across the levels of the text unit hierarchy. SOLO 4
8. L08 Develop data-driven narratives and judge the appropriateness of author-driven and reader-driven storytelling genres for a given audience. SOLO 5

Program Outcomes

1. P01 Be able to understand and apply security protocol and tools to security challenges faced in organizations
2. P02 Be able to design security software to combat security issues
3. P03 Be able to identify, categorize, and develop security solutions for computer orientated challenges.
4. P04 Be able to demonstrate autonomy and responsibility in managing computer security projects
5. P05 Be able to follow the state of the arts concepts in computer technology security
6. P06 Be able to design, implement, and evaluate a computational system to meet desired security needs within realistic constraints
7. P07 Be able to use appropriate security techniques, protocols, skills, and tools necessary for securing computer systems
8. P08 Be able to apply effective communication skills consistent with the professional environment
9. P09 Be able to apply effective collaboration skills in teamwork consistent with the professional environment
10. P10 Be able to apply appropriate security technology and techniques to facilitate a safe operation in an organization

Po-Lo Matrix