Course SummaryProspective Students
Course Summary
(3 credits) This course introduces the principles, concepts, techniques, and tools for visualizing information in large complex data sets. Unlike scientific visualization, which focuses on the presentation of data that has a spatial or physical correspondence, data visualization focuses on mapping complex, abstract information to a physical representation. The development of effective visualization strategies is crucial for not only facilitating an understanding of large complex data sets but also for driving knowledge discovery and the decision-making processes in a given domain.
Overview
In this course, students will learn the key principles involved in data visualization and will explore a wide range of visualization approaches that can be applied to understanding complex data across different data types. Specifically, techniques for visualizing:
- one-dimensional data (e.g., temporal data)
- two-dimensional data (e.g., geospatial data)
- multidimensional data (e.g., mapping relational data in n-dimensional space)
- hierarchies and graphs (e.g., tree structures)
- Visual analytics for images and video datasets
- Visualization techniques for massive and large-scale datasets
- Building a data visualization pipeline
- Multimodal visualization techniques
- Domain-specific 3D image visualization techniques.
Emphasis will be placed on the identification of patterns, trends, and differences in visualizations of data from a variety of domains (e.g., science, business, engineering, computer vision (CV), etc.). In addition, students will gain hands-on experience with a variety of visualization tools including Qlik and Python.
Course Objectives
The objective of this course is to introduce students to the principles of data visualization and to provide them with hands-on experience with different tools and techniques for visualizing large complex data sets. After learning this material, students should be able to answer the following:
- What are the fundamental principles and concepts for data visualization of complex datasets?
- What criteria should be used to identify design problems in visualizations?
- What are some of the major temporal, geospatial, and topical visualization techniques?
- What techniques are helpful in visualizing one-dimensional, two-dimensional, or multidimensional data?
- What visualization techniques are effective for multimodal visualizations?
- What techniques are effective for visualizing massive and large-scale datasets?
- How are design decisions linked to the visualization design process?
- How can visualizations be used to guide effective data storytelling?
Course Materials
Required Textbooks
- Börner, K., & Polley, D. E. (2014). Visual insights: A practical guide to making sense of data. MIT Press.
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- ISBN: 9780262526197
- E-Book Option: There is an E-Book available for this edition of the textbook, but with limited licenses, at no cost as a Penn State Library E-Book. You can access the E-Book through the Library Resources link on the course navigation. You may choose to use the E-Book as an alternative to purchasing a physical copy of the text. For questions or issues, contact the University Libraries Reserve Help (UL-RESERVESHELP@LISTS.PSU.EDU). ***If you are using the E-Book, please be sure to close down and log out of the site when you are finished. Not doing so will prohibit other students from viewing the E-Book.
- ISBN: 9780262526197
- Labbe, P. (2019). Hands-on business intelligence with Qlik Sense: Implement self-service data analytics with insights and guidance from qlik sense experts. Packt Publishing.
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- ISBN: 9781789800944
- E-Book Option: There is an E-Book available for this edition of the textbook, but with limited licenses, at no cost as a Penn State Library E-Book. You can access the E-Book through the Library Resources link on the course navigation. You may choose to use the E-Book as an alternative to purchasing a physical copy of the text. For questions or issues, contact the University Libraries Reserve Help (UL-RESERVESHELP@LISTS.PSU.EDU). ***If you are using the E-Book, please be sure to close down and log out of the site when you are finished. Not doing so will prohibit other students from viewing the E-Book.
- Mazza, R. (2009). Introduction to information visualization. (1st ed.). Springer.
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- ISBN: 9781848002197
- E-Book Option: There is an E-Book available for this edition of the textbook, but with limited licenses, at no cost as a Penn State Library E-Book. You can access the E-Book through the Library Resources link on the course navigation. You may choose to use the E-Book as an alternative to purchasing a physical copy of the text. For questions or issues, contact the University Libraries Reserve Help (UL-RESERVESHELP@LISTS.PSU.EDU). ***If you are using the E-Book, please be sure to close down and log out of the site when you are finished. Not doing so will prohibit other students from viewing the E-Book.
- ISBN: 9781848002197
- Qlik Sense. (2023, May). Create apps and VisualizationsLinks to an external site.. QlikTech International AB.
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- Available at no cost to you via Qlik.com
Proctored Exams
The Midterm Exam and Final Exam are both proctored via Honorlock.
Grading and Examinations
| Assignment | Quantity | % of Final Grade |
| Individual Assignments | 6 | 20% |
| Discussion Forums | 5 | 10% |
| Course Project | 1 | 30% |
| Midterm Exam | 1 | 20% |
| Final Exam | 1 | 20% |
*Grades will be based on the following scale:
A = 95.0 – 100, A- = 90.0 – 94.9, B+ = 87.0 – 89.9, B = 84.0 – 86.9, B- = 80.0 – 83.9, C+ = 77.0 – 79.9, C = 70.0 – 76.9, D = 60.0 – 69.9, and F = Below 60.0
Class Participation (Discussion Forums)
Students will be evaluated based on their timely and meaningful responses to forum questions. Quality of interaction is always valued over quantity. In addition, students may earn participation points by sharing references to additional and relevant resources with the rest of the class via posting to the appropriate forum.
Assignments
During this semester, you will be asked to work on 6 assignments. Please note that all assignments are to be finished with individual effort only. Unless otherwise noted, there is no specific format or length expectation for the assignments. Quality of answers is valued. Students will be evaluated on:
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- timeliness (did you deliver it by the deadline?)
- completeness (did you do all that was asked?)
- responsiveness (did you do what was asked or did you deviate from the assignment?)
- thoroughness (did you answer thoughtfully and substantially, or just in a perfunctory manner?)
- professionalism (formatting, grammar, clarity)
*Note: assignments should be submitted in one PDF file using the filename:
<Surname_FirstInitial-Lesson1.PDF> (i.e., Smith_J-Lesson1.pdf)
Course Project
The idea behind the course project is to apply the knowledge and background you are learning this semester to a topic of broad interest and produce a high-quality, interactive visualization using good design principles and practices. Some aspects that you will be evaluated on are:
- Clarity of the purpose of the interactive visualization.
- The analytic questions/queries a person should be able to answer using the interactive visualization.
- The rationale behind the design principles and techniques used in creating the interactive visualization.
Exams
The midterm and final exams are administered in the eighth and final week of the course respectively and are timed, that will assess your understanding of the material covered in the course. You will have to choose a period in the exam week when you can devote up to an hour.
The exams are to be taken without collaboration with other students or other individuals. Late exams will not be accepted unless the two following criteria have been determined by the instructor:
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- there are mitigating circumstances, and
- you have been given permission prior to the due date of the exam.
Similar to other deliverables, you will be evaluated on:
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- timeliness (did you deliver it by the deadline?)
- completeness (did you do all that was asked?)
- responsiveness (did you do what was asked or did you deviate from the assignment?)
- thoroughness (did you answer thoughtfully and substantially, or just in a perfunctory manner?)
- professionalism (formatting, grammar, clarity)
Course Topics
- Data Visualization, Tools, Principles and Concepts
- Visual Design Best Practices: Principles and Problems
- Qlik for Data Visualization
- Python for Data Visualization
- One-Dimensional and Time-Series Data Analysis
- Interaction and Animation in Qlik Sense
- Model Visualization in Machine Learning
- Visual Analytics on Image and Video Datasets
- Visualization Techniques for Multimodal Data, and Massive and Large-Scale Datasets
- Domain Specific 3D Image Visualization
- Building a Data Visualization Pipeline
Prospective Students
For more information on this program, check out the Master of Data Analytics website!