CS 6965: Advanced Data Visualization

Combine Data Analysis and Machine Learning with Visualization

Fall 2021


Instructor: Bei Wang Phillips (beiwang AT sci.utah.edu, WEB 4608)

Lectures: Mondays, Wednedays, 1:25 PM - 2:45 PM, BU C 107.  

Office Hours: Mondays 2:45 PM - 3:45 PM or by appointment (beiwang AT sci.utah.edu)

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.

For Fall 2021, the course focuses on combining data analysis and machine learning with data visualization to obtain insights.

Learning Outcomes: 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.

Upon completion of CS 6965, students will be able to:

  • Use data analysis and machine learning to aid the development of visualization.
  • Implement visualization prototypes that use visualization to explain machine learning models.
  • Apply visualization solutions to real-world data.
  • Appreciate the innovative nature of research in visualization.
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, ParaView, and TTK.

Class Information

Class Information (detailed information accessible via Canvas):

Communication: Most communication from the instructor to the students is handled through the Canvas Announcement system and Piazza. When class material questions are sent to the instructor, we may isolate the question and post the response to Piazza (so that all can learn from both the question and the answer).

Required Textbook: There are no required textbook for this course. A list of mandatory and recommended readings will be provided.

Disability Notice

The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities.  If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 581-5020 (V/TDD).  CDS will work with you and the instructor to make arrangements for accommodations.

All written information in this course can be made available in alternative format with prior notification to the Center for Disability Services.