New Spring 2024 CDS Courses Announced
Boston University’s Faculty of Computing & Data Sciences is pleased to unveil an exciting lineup of six new courses for the upcoming Spring 2024 semester. The new Spring course offerings span across a wide range of data science disciplines, covering everything from Machine Learning to the intricacies of computational biology and mathematics.
Learn about our new computing and data sciences courses, the faculty leading the charge, and discover the inspiration behind each of these new courses.
CDS DS 592 - Randomized Algorithms
Professor Moon Duchin
“My course will look at randomized algorithms from the point of view of data-rich applications. One of the sources of inspiration is the "Top Ten Algorithms of the Twentieth Century" list by the IEEE -- a kind of greatest hits compilation with crowd-pleasers like Quicksort, Metropolis, and the Simplex algorithm.”
About the course: A little randomness is a surprisingly powerful ingredient in algorithm design. Sometimes this amounts to producing approximate solutions to a problem where exact global solutions are forbiddingly hard to find, and other times the randomness itself—as a probability distribution—is the goal. This course will present some classic and some cutting-edge randomized algorithms, with an emphasis on discrete and graph-based problems.
In Spring 24, this course satisfies:
- BS in DS Methodology Track for Advanced Data Science Methods
- MS in DS A1 Competency
- PhD in DS Competency in Efficient & Scalable Algorithms, Optimization Algorithms, and Mathematical Foundations
For more information, including prerequisites and class times, click here.
CDS DS 593 - Data Engineering at Scale
Professor Langdon White
Clinical Assistant Professor of Computing & Data Sciences + BU Spark! Technical Director
“Some systems are all about data. Think about trying to track the pandemic: there's no sales to be made, no games, no GRWM videos. Instead, we need to gather data, process it, publish it, notify close contacts, look for patterns, etc. Well, this class, Data Engineering at Scale, will teach you how to build a system like that for a global audience with global scale.”
About the course: This course is designed to immerse you into the fascinating world of large-scale data management, processing, and analytics. Throughout this course, students will focus on a mythical but powerful application called the "Epidemic Engine". The Epidemic Engine, while hypothetical, embodies the principles and challenges of real-world data engineering systems that power today's most innovative technologies, from social networks to streaming platforms to cutting-edge AI research.
In Spring 24, this course satisfies:
- BS in DS Methodology Track for Scalable and Trustworthy DS & AI
- MS in DS A2 Competency
- PhD in DS Competency in Programming & Software Design and Large-Scale Data Management
For more information, including prerequisites and class times, click here.
CDS DS 596 - Special Topics in Natural, Biological and Medical Sciences
Professor Brian Cleary
Assistant Professor of Computing & Data Sciences + Biology + Biomedical Engineering
“This course is designed to give upper level undergraduate students a foundation for applying data science in biological research by exploring both computational methods and the biological contexts and questions in which they are employed.”
About the course: This course establishes a foundation in applied statistics and data science in biology for those interested in pursuing data-driven research. The course will develop the foundations of and illustrate major methods applied in modern biological problems and data sets. Students will explore application of these methods in the context of gene regulatory networks, genotype to phenotype mapping, chromatin structure analysis, single-cell biology, and quantitative biological imaging.
In Spring 24, this course satisfies:
- BS in DS Data Science in the field track
- MS in DS A1 Competency
For more information, including prerequisites and class times, click here.
CDS DS 598 A1 - Introduction to Reinforcement Learning
Professor Xuezhou (Jack) Zhang
Assistant Professor of Computing & Data Sciences
“The course is designed as a non-math-heavy introduction to reinforcement learning (RL), with the goal of allowing the students to grasp a high level understanding of the methodology of RL.
The hope is that by the end of the semester, students will understand the underlying mechanisms of popular RL applications like AlphaGO and ChatGPT, and be able to approach new problems from an RL perspective.”
About the course: This course introduces students to the field of Reinforcement Learning (RL). Students will learn the basic concepts in reinforcement learning, classic algorithms (model-based, value-based, policy-based) and explore modern challenges (exploration, partial observability, multi-agent RL).
In Spring 24, this course satisfies:
- BS in DS Methodology track in Applied and Use-Inspired DS & AI
- MS in DS A3 Competency
- PhD in DS Competency in Predictive Analysis & ML
- PhD in DS Subject core in Data Mining & Machine Learning
For more information, including prerequisites and class times, click here.
CDS DS 598 B1 - Deep Learning for Data Science
Professor Thomas Gardos
Associate Professor of the Practice of Computing & Data Sciences
“At a high level, students will take away three things: one, practical experience in training and inference using deep learning frameworks, two, an understanding of fundamental concepts in order to apply first-principles thinking, and last but not least, an understanding of potential ethical challenges.”
About the course: In this course, students will gain an understanding of the fundamentals in deep learning and then apply those concepts in exercises and applications in python. You’ll learn the foundations of training and inference, then become familiar with canonical network architectures including convolutional neural networks and transformers. We’ll look at generative pre-trained Transformer models and pros and cons of prompt engineering versus finetuning.
Finally, students will be able to apply many of the techniques they learned in a final class project.
In Spring 24, this course satisfies:
- BS in DS Methodology track in Applied and Use-Inspired DS & AI
- MS in DS A3 Competency
- PhD in DS Competency in Predictive Analysis & ML
- PhD in DS Subject core in Data Mining & Machine Learning
For more information, including prerequisites and class times, click here.
These courses are now on the schedule and ready for registration and enrollment, please direct any questions to the CDS advisors. Follow the same steps to enroll you normally do.
CDS DS 594 - Spark! Data Visualization X-Lab Practicum
Professor Anthony Chamberas
Adjunct Lecturer at CDS
“In this course students will learn skills for presenting data visually. We will leverage design practices, statistical analysis and problem solving to effectively communicate complex data science outcomes to a variety of audiences. In addition to lectures on data visualization topics, students will also work on industry projects, putting their learnings into practice, while learning data visualization tools along the way.”
About the course: The Data Visualization X-Lab Practicum offers students an opportunity to learn data visualization skills through course and project-based work. This course provides an accurate experience of solving real-world problems with data visualization, and the various tradeoffs that need to be considered. Whether it's how to efficiently use color and space, effectively understand the profile of a dataset or cautiously avoid bias, this course will provide students with a solid understanding of applicable data visualization practices.
For more information, including prerequisites and class times, click here.