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 Demonstrate comprehensive knowledge of key concepts across the breadth of effective application and use of MIS and innovative information technologies in organizations.
2. P02 Demonstrate autonomy and responsibility in managing MIS projects and improving organizational processes
3. P03 Demonstrate comprehensive understanding of appropriate enterprise frameworks, theories from the MIS to research and assess contemporary issues in the field and related allied fields and disciplines
4. P04 Apply MIS knowledge to facilitate the acquisition, development, deployment, and management of information systems
5. P05 Apply MIS knowledge to the exploitation of opportunities created by information technology innovations ensuring the alignment between MIS strategy and organizational strategy
6. P06 Demonstrate ethical reasoning in relation to crucial MIS issues such as privacy, information security, and ethical use of information
7. P07 Apply appropriate technologies and techniques to the collection and analysis of organizational and environmental data to facilitate evidence-based decision-making
8. P08 Analyse organizational data to accurately identify organizational problems and propose solutions using MIS
9. P09 Apply effective communication skills consistent with the professional environment
10. P10 Apply effective collaboration skills consistent with the professional environment

Po-Lo Matrix