Instructor: Bei Wang Phillips (beiwang AT sci.utah.edu,
WEB 4608)
Lectures: Tuesdays, Thursdays, 9:10am - 10:30am, WEB L120
Office Hours:
Bei Wang Phillips: Tuesdays 10:30 am - 11:30 am or by appointment (beiwang AT sci.utah.edu), WEB 4608
Course Description:
Data visualization is an integral part of data analysis; think about wine and cheese, they just go hand in hand.
In this course, we would discover how new and advanced data visualization tools offer analytics capabilities that can help us understand large and complex data.
Large and complex data arise from networks, high-dimensional point clouds, multivariate functions, heterogeneous personal data and ensembles; as such, this course is very much data-driven, as our topics are divided into modules which focus on particular data modality.
The objective of this class is to enable the students to become familiar with innovative techniques that combine data analysis with data visualization, from algorithmic and implementation perspectives.
This course in going to focus on using existing libraries and developing new tools to study large complex data, in particular, network data such as social and biological networks, heterogeneous personal data, high-dimensional and multivariate data that arise from science, social studies and business intelligence. The course is heavily project based, with multiple mini-projects and a collaborative final project.
Successful completion of the course will enable the students to pursue new research directions in data analysis and data visualization; and apply emerging and innovative techniques to data in various application domains.
Prerequisites: There are no formal prerequisites for this class. Students, however, will be expected to have basic knowledge of data structures and algorithmic techniques, bachelor-level knowledge in mathematics or computer science, and working knowledge of programming, ideally with
Python and/or C++.
The targeted audience for the class includes PhD students, master students and very-motivated upper level undergraduate students.
The students are not required to be majoring in Computer Science, but it is preferable that the students have some background in algorithms and/or other data science related courses, and have working knowledge of programming, ideally with
Python and/or C++.
(If you are not sure whether you are qualified to take this class, please email the instructor.)
Suggested Topics: The course will cover (but is not limited to) the following modules:
- High-dimensional data: machine learning and visualization.
- Visualizing large graphs and networks.
- Topological abstraction and summarization for large data.
- Personalized visualization: humanistic
approach to Data.
Assignments:
The students will be given mini-projects and a final project.
The students are encouraged to propose final project ideas and discuss them with the instructor, as early as possible.
Students are allowed to work in small groups (2 members) for the final project.
The students are encouraged to use tools and libraries to develop data visualization applications, in particular, D3.js and TTK.
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