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Course Description

The course will cover foundational ideas in data science in terms of how predictive models are developed based on data collection and the application of machine learning, with an emphasis on how they are used in the solution of real-world problems. We utilize readily available software applications for generating the machine learning, so we are not heavily into computer coding. We will teach what you need to know. The focus is mostly on how the decisions made in the development of these models influence the experience that people have when they use them. For example, if you have bought anything on Amazon you have seen they have a “recommender system” that tells you “people who bought what you are buying also bought this …“. Well, that is part of the user experience, and depends a lot on the data collected and the internal decisions at Amazon about what to do with it. There are human factors implications for this and these are the topics we’ll explore. At the end of the course, you’ll be able to recognize when these artificial intelligence applications are working, understand what went into them that makes the work the way they do, what limitations they have, and how you as a HF professional can contribute to the design to make them more useful to people.

This is an online/virtual synchronous course that follows the published schedule of course meetings and requires attendance at all sessions. Tufts virtual courses are designed to provide high-quality, flexible, and interactive courses to Tufts and visiting students. For more information about virtual course policies and expectations, please visit 

Basic Enrollment Requirements: None.

Refund Policy: The refund policy for Courses at Tufts offerings is dependent on the offering type: whether the offering is a course, workshop or short course, or in-demand offering. Please refer to the section details to confirm the type of offering as well as any exceptions to the standard refund policy. The refund policies for each offering type are viewable here:

Remission Eligible: Yes; first day of term; all university policies apply.

Affiliated With:

School of Engineering