Master’s in Data Science Courses & Curriculum | Northwestern SPS - Northwestern School of Professional Studies

Course Schedule, Part-time Online Program

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

Online Sync Sessions are an integral part of the online learning experience. Additional information about learning concepts and assignments may be discussed and sync sessions offer valuable opportunities for students to interact with their faculty and peers during the term. We encourage all students to attend live, but if they are unable to, sync sessions will be recorded and posted within Canvas to allow for an asynchronous model of success as well.

MSDS 490-DL : Special Topics - Recommender Systems

Description

This course equips students with knowledge and hands-on experience in designing, implementing, and evaluating recommender systems. Through a blend of theoretical foundations and practical applications, students will explore data preprocessing, matrix factorization, embedding-based models, sequential recommendation models, and deep learning techniques. Leveraging industry-standard frameworks, the course emphasizes scalability, explainability, and ethical considerations in real-world scenarios. Weekly assignments build on recent research and prepare students to tackle complex challenges in the field of recommender systems.

Recommended prior courses: MSDS 410-DL Supervised Learning Methods
and MSDS 411-DL Unsupervised Learning Methods.

Prerequisites: (1) MSDS 420-DL Database Systems or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.