Data Science

This course will offer a mathematical and practical perspective on artificial neural networks for machine learning. Students will learn about the most prominent network architectures, such as for example, feedforward, recurrent, convolutional, and attention-based neural networks. This course will also teach students optimization and regularization techniques used to train them — such as back-propagation, stochastic gradient descent, dropout, pooling, and batch normalization. Connections to related machine learning techniques and algorithms will be explored. In addition to understanding the mathematics behind deep learning, students will have the opportunity to train neural networks for a wide range of real-world applications.

This course introduces the core principles, models, and real-world applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in the context of modern data science. Students will explore NLP tasks, build deep learning models for language understanding and generation, and apply LLMs to solve problems such as information extraction, conversational AI, summarization, and data querying. Students will interact with state-of-the-art LLMs using industry-standard APIs, gaining practical skills in prompt design, system integration, and application development. The course also includes a critical focus on LLM trust, safety, and ethical deployment, preparing students to responsibly build and evaluate generative AI systems. Through projects, students will emerge with both conceptual understanding and practical fluency in applied NLP and LLM development.

This course introduces foundational concepts and solution strategies for designing intelligent systems composed of multiple autonomous agents. Students will learn about agent-based modeling, coordination, communication, and decision-making in cooperative and competitive environments. The course introduces core theories and algorithms for multi-agent decision-making. It also covers how contemporary AI techniques—such as deep learning and generative models—can be applied to enhance multi-agent system capabilities. Through hands-on projects, students will gain practical experience in analyzing and designing multi-agent systems for real-world applications.

This course introduces the emerging techniques and infrastructures for big data management and analytics including parallel and distributed database systems, map-reduce, Spark, and NoSQL infrastructures, data stream processing systems, scalable analytics and mining, and cloud-based computing. Query processing and optimization, access methods, and storage layouts developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies.

This course trains students in data visualization, the graphical communication of data and information for presentation, confirmation, and exploration. Students learn the stages of the visualization pipeline, including data characterization, mapping data attributes to graphical attributes, user task abstraction, visual display techniques, tools, paradigms, and perceptual issues. Students evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and public-domain visualization tools.

This course provides an introduction to the core concepts in Data Science and Artificial Intelligence. It covers a broad range of methodologies for working with and making informed decisions based on real-world data. Core topics include Python programming, data cleaning and preparation, statistics, data analytics, machine learning, natural language processing, data modeling, visualization, and business intelligence. In addition, the course emphasizes responsible and ethical considerations in the use of AI. Through hands-on activities and real-world data sets from diverse domains, students will practice using modern tools and techniques to explore data, gain insights, and understand how DS and AI systems are built and applied. 

This course focuses on model- and data-driven approaches in Data Science. It covers methods from applied statistics, optimization, and machine learning to analyze and make predictions and inferences from real-world data sets. Topics covered in this course include a brief overview of statistics and linear algebra, followed by introductory machine learning methods such as linear and nonlinear regression, classification, decision trees, and dimension reduction techniques. Data exploration, data cleaning, feature engineering, and the bias-variance tradeoff will also be covered. Students will utilize various techniques and tools to explore and understand real-world data sets from various domains.

This course covers a broad range of computational methods to make informed decisions on large and/or high-dimensional data sets following the data science pipeline. Core topics include collecting data via APIs, processing and managing large-scale data, cloud computing, and applying machine learning and deep learning toolkits to extract insights. The goal is to aid decision-making in different domains. Students will learn these skills by working on projects using real-world data sets.

Instances of this course will explore advanced and emerging topics in Data Science that are not covered by the current regular Data Science offerings. Content and format will vary to suit the interests and needs of the faculty and students. This course may be repeated by students for credit as topics change.

The focus of this class will be on statistical learning - the intersection of applied statistics and modeling techniques used to analyze and to make predictions and inferences from complex real-world data. Topics covered include: regression; classification/clustering; sampling methods (bootstrap and cross validation); and decision tree learning. Students may not receive credit for both MA 463X and MA 4635.