New Spring 2025 CDS Courses Announced
Boston University’s Faculty of Computing & Data Sciences is pleased to unveil an exciting lineup of three new courses for the upcoming Spring 2025 semester. The new course offerings span across a wide range of data science disciplines, covering everything from machine learning to behavioral studies and algorithms.
Learn about our new computing and data sciences courses and the faculty leading the charge.
CDS DS 593 - Special Topics in DS Methodologies - Spring 2025 topic: Privacy in Practice
Alishah Chator
Postdoctoral Researcher
About the course: Want to learn how to take control of your data? Worried about surveillance or intrusive technologies? DS 593: Privacy in Practice is a new course aiming to be an approachable introduction to the world of privacy. You’ll be brought up to speed on the latest technologies, debates, and issues that define the state of privacy today. Additionally, you will get hands-on experience with cutting-edge privacy tools. Interested undergraduate and master's students are encouraged to enroll!
In Spring 25, this course satisfies:
- BS in DS In-the-Field – Algorithmics in the Field
- MS in DS A4 Social Impact or A5 Security and Privacy
For more information, including prerequisites and class times, click here.
CDS DS 497 - Special Topics on Social and Behavioral Sciences
Professor Joshua Peterson
Assistant Professor of Computing & Data Sciences
About the course: Application of the scientific method to understanding the human mind began only a century ago, and has been largely limited to observations of small groups of volunteers in university laboratories. Fast-forwarding to today, the internet has dramatically increased the scale of human data that we can access and analyze. In this course, we explore how data science can be leveraged to help us better understand human cognition and behavior. Students will learn to employ tools to collect and analyze real datasets of human behavior, including web experiments, statistics, visualization, cognitive modeling, and machine learning. We will also discuss a number of ethical issues surrounding human data. The course is designed to be accessible to undergraduate students in the social sciences. Basic knowledge of algebra, a previous course in introductory statistics, and previous exposure to programming is required.
In Spring 25, this course satisfies:
- BS in DS In-the-Field – Data Science in the Field
For more information, including prerequisites and class times, click here.
CDS DS 592 - Special Topics in Mathematical and Computational Sciences
Professor Aldo Pacchiano
Assistant Professor of Computing & Data Sciences
About the course: This course introduces the study, design and analysis of algorithms for sequential decision making with a particular focus on bandit algorithms and other topics in statistical learning theory. Designed for upper undergraduate and graduate students, the course covers foundational concepts and cutting-edge research in multi-armed bandits, linear bandits, and contextual bandits. Students will gain an understanding of fundamental algorithmic principles in sequential decision making such as optimism, multiplicative weights as well as bandit algorithms such as UCB, EXP3, OFUL. Additionally, the class will cover bandit problems in the general function approximation regime via the study of algorithms such as SquareCB and statistical dimensions for function approximation, including the eluder dimension, dissimilarity dimension, and decision estimation coefficient. Finally, the course will also explore miscellaneous yet essential topics such as online model selection, and offline estimation. Through a combination of theoretical insights and practical applications, students will gain a comprehensive understanding of how to design, analyze, and implement algorithms for sequential decision-making tasks.
In Spring 25, this course satisfies:
- BS in DS Methodology – Advanced DS Methods
- MS in DS A3 Machine Learning and AI
For more information, including prerequisites and class times, click here.