36 courses with the subject DS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DS 1001Foundation of Data Science3
Effective Date 01/01/2026 Introduction to core data science concepts and skills, including computing environments, visualization, modeling, and bias analysis. Think like a Data Scientist as you engage through lectures, discussions, labs, and guest talks while applying learning in a guided semester-long project. Concludes with an independent project to reinforce and extend skills. Requisites Must be a 1st Year student to enroll.
Effective Date 10/02/2024 Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be covered in depth. Additionally, project management skills such as Git and Github will be covered. Requisites Students can’t enroll if previously taken any of the following courses: CS 1110, CS 1111, CS 1112, CS1113, CS 1120, PHYS 1655.
Effective Date 08/01/2022 This course will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and evolve to cloud base systems such as AWS or Google Cloud. This progression will include topics such as data lakes and other non-SQL applications as appropriate.
Effective Date 08/01/2022 The course is designed to not only teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Examples might include tailoring messages based on the audience or shaping visualizations to follow a story-line. Content on the development of interactive plots and dashboards will also be included. Requisites Students must have completed CS 1110 or CS 1111 or CS 1112 or CS 2110 or DS 1002 or PHYS 1655 or CS 1113
Effective Date 09/19/2022 Explores principles and applications of data ethics within a broader social framework that prioritizes conversations about policy, regulatory frameworks, accountability, transparency, and governance models. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities.
Effective Date 08/01/2026 Will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and foundation knowledge in terms of computational environments. The content will lay the groundwork for more advanced Systems Domain courses in the major. Requisites Must be a declared Data Science major
Effective Date 08/01/2026 Designed not only to teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Lays the foundation for more advanced topics in the Data Design domain. Content on the development of interactive plots and dashboards will also be included. Requisites Must be a declared Data Science major
Effective Date 08/01/2026 Explores principles and applications of data ethics within a broader social framework. Works to lay foundational knowledge for more advanced courses in the Value domain of the major. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities. Requisites Must be a declared Data Science major
Effective Date 08/01/2024 Covers the fundamentals of probability theory & stochastic processes. Become conversant in the tools of probability. Clearly describe & implement concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, basic inference, sampling distributions, discrete & continuous time Markov chains, & Brownian motion. Illustrate most topics with both analytic & computational solutions. Requisites Must be a declared Data Science major
Effective Date 08/01/2026 Engage with and train in the use of key concepts in machine learning and math: OLS estimator for regression; logistic regression & maximum likelihood estimator; multiple linear regression; principal components analysis & multiple correspondence analysis; neural networks; logarithms; probability distributions; integrals; multivariate optimization; matrix notation; eigenmath, and matrix decomposition; infinite power series & Taylor series. Requisites Declared Data Science Major AND completed DS 2026 and ONE of the following: MATH 1190 or MATH 1210 or MATH 1310 or APMA 1090. Students cannot enroll if they previously completed DS 3025.
Effective Date 05/01/2023 This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues surrounding the field of data science and how these issues originate and extend into society more broadly. Requisites Declared DS Minor AND have taken one of the following courses: DS1002 (formerly DS2001), CS 1110, CS 1111, CS 1112, CS1113. CS 2110, or PHYS 1655.
Effective Date 08/01/2026 Exposes students to foundational knowledge in the area of analytics, especially as it relates to machine learning. The focus is on methods needed to prepare data for machine learning models, how to evaluate the output of ML models and engineering features. Requisites Must be a declared Data Science major
Effective Date 03/11/2024 Moves deeper into current best practices around data engineering in industry. Topics will review basic data collection, ingestion, processing, and storage, moving beyond to data governance, security, pipeline orchestration, monitoring and maintenance, optimization, and documentation. Relies heavily on DevOps principles of automation, continuous improvement, and an understanding of the entire software/data lifecycle.
Effective Date 08/01/2026 Comprehensive exploration of the multifaceted aspects of data creation, emphasizing the symbiotic relationship between design and data. Students will gain insight into the intentional and unintentional mechanisms that contribute to data creation, including human input, technological processes, environmental factors, and systemic influences.
Effective Date 08/01/2024 Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, bootstrap methods, ROC curves, shrinkage, large sample theory, prediction optimality versus estimation optimality. Requisites Must be a declared Data Science major
Effective Date 01/01/2025 The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will work in groups and can either choose a project provided by SDS faculty or can propose a project for approval. Upon completion of the course students will be required to present their results and publish project content to an open forum. Requisites Declared DS Minor AND have completed/or taking concurrently one of the following courses: DS 3001 or APMA 3150 or STAT 3080 or CS 4774 or STAT 5630 or PSYC 5710 or DS 2006 or DS 3005 or DS 3006
Effective Date 08/01/2026 Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.
Effective Date 08/01/2026 Will allow students to take the knowledge gained throughout the major and deploy a data driven system. Students will work in groups and will need to propose their own projects. Upon completion of the course, students will be required to present their results and publish project content to an open forum. Requisites Must be a declared Data Science major
Effective Date 08/01/2026 Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic dashboards for effective data communication with end-users. Covers theoretical concepts and hands-on implementation to provide a comprehensive understanding of the full design process. Requisites Declared Data Science Major and taken one of the following courses: DS1002 (formerly DS2001), CS 1110, CS 1111, CS 1112, CS 2110, or PHYS 1655.
Effective Date 08/01/2026 Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine learning methods. Covers definitions, decision support, trust, and ethical considerations, and the latest advances in creating reliable and transparent AI models.
Effective Date 03/29/2024 Dives into how computers can analyze large chunks of text, like reviews, articles, and even books. We’ll start by transforming this text into a format that computers can understand. Then, we’ll use special tools and techniques to uncover interesting patterns and hidden ideas within the text. Students will be exposed to contemporary topics in Natural Language Processing that can help build toward further student in Large Language Models.
Effective Date 03/27/2024 Understand Deep Learning covering neural networks, activation functions, and optimization algorithms. Gain experience with TensorFlow and PyTorch, mastering key techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Explore transfer learning, reinforcement learning, and natural language processing (NLP), along with industry applications and ethical considerations.
Effective Date 03/27/2024 Introduces image formation, color spaces, and edge detection algorithms. Through hands-on projects utilizing industry-standard libraries like OpenCV, TensorFlow, and PyTorch, students will explore techniques including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Deep Learning architectures optimized for Computer Vision tasks such as object detection, facial recognition, and image segmentation.
Effective Date 03/11/2024 Hands-on practice at building a sensor-data network from scratch. Students will work with a variety of physical and remote devices to build and deploy a constellation of sensors, then build the tooling for ingesting, aggregating, and processing data in near real-time. Special attention will be spent on system visibility, troubleshooting sensors, identifying data bottlenecks, and optimization.
Effective Date 03/12/2024 Explores new models of database design: graph, vector, and ledger. These have become required infrastructure in service of social media (graph databases), Large Language Models (vector databases), and cryptocurrency (ledger databases). Will learn their basic operations with an eye toward other purposes as well as the key advantages and drawbacks of these data models. Center on student projects built using one of these databases.
Effective Date 03/29/2024 Produce a series of data design projects. Whether fascinated by trends in customer reviews or captivated by the hidden narrative within a novel, pursue your own visually stunning projects. Imagine crafting an animation that reveals the emotional flow of a book or designing an interactive infographic that brings a social media dataset to life. Centered on being both an analyst and artist, transforming data into captivating narratives.
Effective Date 03/12/2024 Introduces complex interplay between technology, regulation, and data science and exposes regulatory realities confronting the field. Read and parse regulatory texts. Navigate the international technology regulatory landscape, identify key actors, and appreciate how rules governing different kinds of data, platforms, copyright and intellectual property, and digital services and markets shape data science and AI/ML development practices.
Effective Date 03/12/2024 Familiarizes students with the social dimension of our data-driven world. Will use key texts, interdisciplinary scholarship, and case studies to explore the interlinked nature of “the social” and “the technical” in data science and in society writ large. Will examine the role of societal norms, narratives, and representation in data collection and analysis. Unpack the economic and political drivers of data-intensive systems.
Effective Date 01/31/2024 Apply intellectual curiosity around data science to a broad range of compelling contexts. Requisites Must be a declared Data Science major
Effective Date 01/31/2024 Topics may include statistical methods, algorithm development, imaging, and mathematical modeling. Requisites Must be a declared Data Science major
Effective Date 01/31/2024 Topics may include data architecture, database theory, high performance computing, distributed systems, cloud architectures, and security. Requisites Must be a declared Data Science major
Effective Date 01/31/2024 Topics may include communication, visualization, human-computer interaction, and computer vision. Requisites Must be a declared Data Science major
Effective Date 02/12/2025 This course provides selected special topics in data science. Requisites Completed MATH 3351 AND MATH 4040 with grades of C- or greater.
Effective Date 05/01/2026 Study under the direction of a faculty member. Students must obtain approval on a syllabus from a faculty advisor to approve and direct the independent study. Student must be in good academic standing to enroll in this Independent Study course.
Effective Date 05/01/2026 Research under the supervision of faculty member. Student must obtain approval on the description and responsibilities of the research through a syllabus from a faculty advisor. Student must be in good academic standing to enroll in this Research course.
