Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• CDS DS 991: Computing & Data Sciences Research Rotation 1
  Experience with diverse research group projects and styles are an essential part of graduate training. To provide this training, graduate students are expected to complete a series of research rotations that provide them with the opportunity to (1) explore research groups where they may pursue future thesis work; (2) experience the diversity of sub-disciplines and research styles in computing and data sciences; and (3) expand their network of potential research collaborators. Before beginning a rotation, students are expected to discuss their plans with the faculty leadership of the BU research group they would like to join, leading to a clear definition of the scope and expected outcomes of the work pursued under the supervision of a named rotation advisor. While desirable, it is neither required nor expected that the rotation will result in completion of a substantial body of work. Research rotation credits will be noted on the student transcript as a 4-credit independent study course numbered as DS-991 (for Fall semester rotations) or DS-992 (for Spring semester rotations).
• CDS DS 992: Computing & Data Sciences Research Rotation
  Experience with diverse research group projects and styles are an essential part of graduate training. To provide this training, graduate students are expected to complete a series of research rotations that provide them with the opportunity to (1) explore research groups where they may pursue future thesis work; (2) experience the diversity of sub-disciplines and research styles in computing and data sciences; and (3) expand their network of potential research collaborators. Before beginning a rotation, students are expected to discuss their plans with the faculty leadership of the BU research group they would like to join, leading to a clear definition of the scope and expected outcomes of the work pursued under the supervision of a named rotation advisor. While desirable, it is neither required nor expected that the rotation will result in completion of a substantial body of work. Research rotation credits will be noted on the student transcript as a 4-credit independent study course numbered as DS-991 (for Fall semester rotations) or DS-992 (for Spring semester rotations).
• CDS DS 999: Continuing Study Zero Credit Full Time Certification
  Continuing Study Zero Credit Full Time Certification Course for PhD students.
• CDS DX 500: Data Science and AI Bootcamp
  Designed to provide an introduction to programming for learners of all backgrounds. Includes basic programming fundamentals and an introduction to cloud computing and data science tools. All learners are required to complete the Data Science Bootcamp two weeks prior to the start of class. The Bootcamp covers the introductory principles and helps to ensure that you are prepared to dive in on day 1 of the program.
• CDS DX 501: Mod 0 Orientation
  Orientation for CDS Online Programs.
• CDS DX 601: Mathematical Foundations of Machine Learning
  Module 1. This class focuses on the mathematical foundations of data science with a dual focus on linear algebra and probability. The linear algebra component will cover vectors, matrix, tensor, multiplication, inverse, determinant, trace, and norms (L1, L2, etc.). The probability component will cover random variables, distributions, expectation, marginal/conditional probability, independence, and correlation.
• CDS DX 602: Python Programming Toolkit
  Module 2. This course orients learners to navigate a programming environment setup and tools like the filesystem, command line, and notebooks. Also includes a review of fundamental components of the Python programming language, including data structures (scalars, vectors, arrays, dictionaries, etc.), installing and importing packages, control flow, loops, and functions.
• CDS DX 603: Machine Learning Fundamentals
  This module explores essential concepts and techniques in machine learning, including key topics such as linear methods (e.g., linear regression, lasso, ridge), tree methods (decision trees, random forests, boosting), and unsupervised methods like clustering. Learners will gain proficiency fitting various machine learning models, understanding regularization to prevent overfitting, and cross-validation to evaluate model performance and tune hyper-parameters.
• CDS DX 604: Data Management at Scale
  This module provides a comprehensive exploration of data handling techniques, leveraging case studies and real-world examples. Begins with Basic SQL syntax and data manipulation and progresses to cover advanced SQL topics, including SELECT statements, filtering, sorting, aggregation, grouping, and joins. Learners will gain proficiency in modifying data and working with database indexing, constraints, and views in SQL.
• CDS DX 699: AI for Leaders
  Module B. This course helps learners appreciate the impact of AI on all industries. Additional topics include incorporating AI into an overall business strategy, navigating ethical and regulatory considerations, explaining AI-driven decisions to stakeholders, emerging AI technologies and their potential impact, basic concepts of bias and fairness in AI, and privacy, e.g., General Data Protection Regulation (GDPR), personalization, etc. This course will be taught over 2 semesters, in a 699A1 and 699B1.
• CDS DX 701: Responsible and Ethical Data Science
  This module explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. Emphasizes the socially constructed nature of science, shedding light on biases and inequalities. Learners explore the societal and cultural implications of AI and machine learning technologies, analyzing potential biases and disparities in data and algorithms. Practical skills are developed through the application of fairness metrics to evaluate and mitigate bias in algorithmic decision-making. Privacy, ethical considerations surrounding personal data, and navigating key regulatory frameworks related to AI and ML, such as GDPR, California Consumer Privacy Act, and other data privacy laws, are integral components.
• CDS DX 702: Experimental Design and Causality
  This module focuses on the essential distinction between predictive modeling and causal inference in data science. Learners gain an understanding of situations where predictive models may fall short in revealing underlying causality. Real-world examples underscore the potential pitfalls of relying on simple correlations, emphasizing the necessity of experimentation. Learners will gain an understanding of the foundational principles of causal inference, including potential outcomes and counterfactuals. The module explores the principles and applications of A/B testing for methodically assessing the impact of interventions and changes. Students develop practical skills, designing and implementing basic A/B tests, selecting appropriate metrics, and determining sample sizes.
• CDS DX 703: Advanced Machine Learning & AI
  This module explores cutting-edge machine learning techniques and introduces the foundational concept of neural networks, emphasizing their potential as universal function approximators. Exposes learners to optimization algorithms, with a focus on practical implementation. Learners will be exposed to state-of-the-art models such as transformers and large language models (LLMs), exploring their roles in natural language processing and beyond to interpret data. Learners also engage in a mini project, applying learned concepts in a hands-on manner.
• CDS DX 704: AI in the Field
  This module explores the specific data science challenges and opportunities in industries such as finance, health care, and e-commerce. Learners will grasp the unique data sources and data collection techniques relevant to their chosen sector, exploring and applying machine learning models to the industry's needs. Learners can further specialize by choosing a mini-concentration, such as finance, health care, or e-commerce, allowing for the application of techniques such as customer segmentation, churn prediction, and recommendation systems. A mini-project in the chosen sector rounds out the practical application of analytical skills gained in this module.
• CDS DX 799: Data Science Capstone
  The entire online Master of Data Science program is designed to provide opportunities to put knowledge into practice. Each learner will complete a semester-long data science analysis project from a range of industries or disciplines. Learners will demonstrate the ability to apply a wide range of data science skills acquired throughout the program, including data analysis, modeling, programming, and data management. Learners will effectively communicate project objectives, methodologies, and results to both technical and non-technical audiences. They will also have the ability to develop an impactful data science project that can be showcased to potential employers or collaborators.
• CDS EA 500: Program Orientation
  Orientation for Enterprise AI
• CDS EA 501: Enterprise AI Bootcamp
  The Bootcamp covers the introductory principles and helps to ensure that you are prepared to dive in on day 1 of the program.
• CDS EA 603: Foundations of Machine Learning for Enterprise Intelligence
  This course covers the full machine learning workflow in a business context, from training traditional models like regressions and random forests to understanding the foundational concepts of modern neural networks and deep learning. The course also introduces the business applications and opportunities in other key AI modalities, including Computer Vision and Speech AI.
• CDS EA 604: Data Engineering & Strategy for AI
  This course covers building the robust data foundation that enterprise AI requires. You will learn to manage data at scale using SQL and NoSQL databases, and master modern Vector Databases essential for Retrieval-Augmented Generation (RAG) and other semantic AI applications.
• CDS EA 620: Designing & Building LLM Applications
  A hands-on course focused on the complete toolkit for customizing Large Language Models. You will master the three core methods for adapting LLMs to enterprise tasks: prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning.