Summer 2024 - IAT 882 G100

Special Topics II (3)

Data Science for Interactive Systems

Class Number: 3932

Delivery Method: In Person


  • Course Times + Location:

    May 6 – Aug 2, 2024: Mon, 2:30–5:20 p.m.



Title:  Data Science for Interactive Systems  

Description: Analytical approaches examining user log data to understand how interactive systems meet their users' goals are covered. The data preprocessing techniques, feature engineering for summative and temporal characteristics, statistical, data mining and machine learning techniques used to derive insights are compared, focusing on their benefits and pitfalls. Strategies for devising logging mechanisms for the evaluation of the interactive systems are discussed. The connection of developed data models to theoretical frameworks is emphasized to guide the design of the data capture mechanism and interpretation of the results.

Prerequisites: Prior research methods course (e.g. IAT 804, 802) and computing background (e.g. IAT806, or prior CS or related degree) are required. If you do not have graduate-level methods course experience, discuss your background with the instructor.



Additional Information:

Data science is concerned with deriving insights from data to increase our understanding and support processes in many different domains. It lies at the intersection of computer science, statistics, and machine learning. This course will examine how data science can help us evaluate, understand, and design systems that interact with people as they work, play and learn. We will pay attention to the sources of the data, the purpose of the interaction with the system, and the data analysis goals. We will address steps in the data analysis pipeline from gathering and cleaning the data, engineering data features most suitable to answer (research) questions, and reviewing the most commonly used methods and techniques to answer different questions.  We will stress the importance of theory in making the decision along the analytical pipeline.

The course will take an applied focus, introducing methods and techniques through concrete examples derived from published research. Given our focus on interactive systems, we will spend more time on temporal techniques, complementing statistical approaches learned in qualitative research methods. Machine learning approaches will be covered from the practitioner’s perspective, highlighting their applicability and suitability for answering questions in interactive systems research.

The course will be the most beneficial for students interested in complementing the traditional research methods with analytical approaches used in data science. The goal is to provide students with a powerful channel of evidence to support their evaluation, design, and understanding of various interactive systems, such as learning systems, healtcare support systems, multimodal interfaces, virtual reality, etc. Ideally, students would bring their application domain data from the environments they will be working on. Students who do not have such datasets from their respective labs will be using the synthetic data from the learning domain provided by the instructor. 


  • Assignments, discussions, participation 25%
  • Paper 50%
  • Exam 25%


Any information in this outline can be updated as the course is being developed.



Skiena, Steven S. The Data Science Design Manual. Springer International Publishing AG, 2017. (available online via SFU library)

Additional research papers will be  selected for each topic.


Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:


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