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)

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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 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix