| Class Schedules Index | Course Catalogs Index | Class Search Page |
| Data Science | |
| DS 1000T | Non-UVA Transfer/Test Credit (1 - 10) |
| Elective credit for incoming students who have taken a data science course. | |
| DS 1001 | Foundation of Data Science (3) |
| Offered Spring 2025 | Exposes students to the emerging field of Data Science, its domain areas, and popular applications. Topics include analytical methods, ethical issues associated with the field, engineering, and systems necessary to support data-related work, and design principles commonly seen in data communications and human center design. Students learn from leaders in the field through a series of guest lectures and work through discussion examples. |
| DS 1002 | Programming for Data Science (3) |
| Offered Spring 2025 | 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. |
| DS 2000T | Non-UVA Transfer/Test Credit (1 - 10) |
| For incoming students who took a data science course equivalent to an elective course. | |
| DS 2002 | Data Science Systems (3) |
| Offered Spring 2025 | 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. |
| DS 2003 | Communicating with Data (3) |
| Offered Spring 2025 | 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. |
| DS 2004 | Data Ethics (3) |
| Offered Spring 2025 | 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. |
| DS 2022 | Systems I: Intro to Computing - Major (3) |
| 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. Course was offered Fall 2024 | |
| DS 2023 | Design I: Communicating with Data - Major (3) |
| Offered Spring 2025 | 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. |
| DS 2024 | Value I: Ethics & Policy in Data Science - Major (3) |
| 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. | |
| DS 2026 | Computational Probability (3) |
| 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. Course was offered Fall 2024 | |
| DS 3000T | Non-UVA Transfer/Test Credit (1 - 10) |
| For incoming students who took a data science course equivalent to an elective course. | |
| DS 3001 | Foundations of Machine Learning (3) |
| Offered Spring 2025 | 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. Course was offered Fall 2024, Spring 2024, Fall 2023, Spring 2023, Fall 2022, Spring 2022, Fall 2021, Spring 2021 |
| DS 3021 | Analytics I: Foundations of Machine Learning (3) |
| Offered Spring 2025 | 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. |
| DS 3022 | Data Engineering (3) |
| 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. | |
| DS 3025 | Mathematics for Data Science (4) |
| Offered Spring 2025 | 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, eigen-math, and matrix decomposition; infinite power series & Taylor series. |
| DS 3026 | Principles of Inference and Prediction (3) |
| 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. | |
| DS 4002 | Data Science Project (3) |
| Offered Spring 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. Course was offered January 2025, Fall 2024, Spring 2024, January 2024, Fall 2023, Spring 2023, January 2023, Fall 2022, Spring 2022, Janiuary 2022, January 2021 |
| DS 4021 | Analytics II: Machine Learning (3) |
| 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. | |
| DS 4022 | Data Science Project - Major (3) |
| 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. | |
| DS 4023 | Data Design II: Interactive Applications (3) |
| 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. | |
| DS 4024 | Value II: Explainable AI (3) |
| 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. | |
| DS 4121 | Foundations of Text Analytics (3) |
| 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. | |
| DS 4125 | Introduction to Deep Learning (3) |
| 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. | |
| DS 4126 | Computer Vision (3) |
| 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. | |
| DS 4220 | IoT and Sensor Data (3) |
| 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. | |
| DS 4221 | Advanced Databases (3) |
| 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. | |
| DS 4320 | Data by Design (3) |
| 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. | |
| DS 4329 | Data Design Studio (3) |
| 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. | |
| DS 4422 | Technology Regulation and Data Science (3) |
| 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. | |
| DS 4423 | Data, Technology, and Society (3) |
| 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. | |
| DS 4520 | Data Science Applied (1 - 4) |
| Apply intellectual curiosity around data science to a broad range of compelling contexts. | |
| DS 4522 | Topics in the Analytics Domain (1 - 4) |
| Topics may include statistical methods, algorithm development, imaging, and mathematical modeling. | |
| DS 4523 | Topics in the Systems Domain (1 - 4) |
| Topics may include data architecture, database theory, high performance computing, distributed systems, cloud architectures, and security. | |
| DS 4524 | Topics in the Design Domain (1 - 4) |
| Topics may include communication, visualization, human-computer interaction, and computer vision. | |
| DS 4527 | Topics in the Value Domain (1 - 4) |
| Topics within data policy, ethics, and social impact. | |
| DS 4559 | New Course: Data Science (1 - 4) |
| Offered Spring 2025 | This course provides selected special topics in data science. |
| DS 4993 | Independent Study in Data Science (1 - 4) |
| Offered Spring 2025 | Reading and research under the direction of a faculty member. Students must obtain approval from a faculty advisor to approve and direct the independent study. Final approval by the Director of Undergraduate Programs is also required. |
| DS 5001 | Exploratory Text Analytics (3) |
| Offered Spring 2025 | Introduction to text analytics with a focus on long-form documents, such as reviews, news articles, and novels. Students convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and narrative structures. The focus is on unsupervised methods to explore cognitive and social patterns in texts. |
| DS 5002 | How to Train your LLM: Engineering LLMs for Custom Tasks (3) |
| Offered Spring 2025 | Train your own LLM for a custom task. Learn about the LLM lifecycle from architecture, to pre-training, to supervised finetuning, to deployment, to model editing/updating, including discussing LLM limitations. End up with your own trained LLM, a HuggingFace model card you can show off in technical interviews, and a plan for how to stay up to date with this fast-moving field. |
| DS 5003 | Healthcare Data Science (3) |
| Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and safety data; and unstructured data, such as clinical text data; biomedical sensor data; and biomedical image data. Querying with SQL, data visualization with Tableau, and analysis and prediction with Python. Course was offered Fall 2024 | |
| DS 5008 | Data, Arts Administration, and Policy (3) |
| Aims to facilitate understanding and discussions on the impact of data and digital technologies on culture and propose solutions to the challenges affecting established cultural practices and concepts. Gain insights into analytical skills in the context of cultural data, enhancing ability to contribute meaningfully to arts management. Broadens perspectives and encourages the responsible use of data in the cultural domain. Course was offered Fall 2024 | |
| DS 5050 | Deep Learning in Environmental Science (3) |
| Offered Spring 2025 | Equips students with some of the most used deep learning architectures. Explore feed-forward networks, convolutional neural networks, UNETs, encoders-decoders, generative adversarial networks and transformers. Analyze tools of explainable AI. Focused on climate applications, apply these techniques to real-world data, solving problems in prediction, pattern recognition, and data-driven insights. |
| DS 5100 | Programming for Data Science (3) |
| Offered Spring 2025 | An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations. |
| DS 5110 | Big Data Systems (3) |
| Offered Spring 2025 | Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) and may extend to structured data (text, images, time series). AI and machine learning practitioners build and deploy data science projects on Amazon Web Services unifying data science, data engineering, and application development. |
| DS 5111 | Streamlining Data Science with Software and Automation Skills (3) |
| Offered Spring 2025 | Code an end-to-end data science project with core software engineering and automation to quickly integrate into a corporate environment. Use version control to focus on solutions, leverage automation at your command line and in the cloud, deliver solid code by incorporating testing, lower extension and maintenance time with OOP and Design Patterns, ensuring your code's path to production to deliver a complete package to the enterprise. Course was offered Summer 2024 |
| DS 5122 | Large Language Models (3) |
| Core concepts underlying LLMs: transformer architectures, attention mechanisms, and pre-training techniques. Advanced topics: fine-tuning, transfer learning, and domain adaptation, learning how to customize LLMs for specific tasks and datasets. Applications of LLMs in text generation, sentiment analysis, language translation, and summarization are explored, providing real-world insights into the capabilities and limitations of these models. | |
| DS 5220 | Advanced Cloud Computing (3) |
| An intensive overview of cloud infrastructure and their role in data science. Topics will include storage as a service, ephemeral computing resources, auto-scaling, and event-driven workloads. Special attention will be paid to cloud-native design patterns, which are built assuming the unique functionality of cloud computing resources. | |
| DS 5221 | Stream Processing (3) |
| Exposes works with high volumes of streaming data -- now common to financial markets, social media, bedside monitoring, or ride-sharing apps. Students will learn how to consume high throughput streaming data, filter and parse it, ship it to storage and take action on stream events in real-time. Will cover approaches to deploying and refining machine learning pipelines with no downtime. | |
| DS 5320 | Human-Centered Design (3) |
| Understanding Human-Centered Design and Human-Centered-AI. Learn problem-solving techniques to create AI systems that put people at the center of the development process to keep users¿ needs and preferences front of mind during every phase of the AI life cycle to build more intuitive, accessible, trustworthy, and acceptable products. Prioritize human-centric approaches, ensuring transparent operations and fostering equitable outcomes in AI. | |
| DS 5400 | Business Analytics for Data Science (3) |
| Focuses on the application of data science to critical problems and opportunities in business. You will learn business concepts in strategy, markets and competition, and will apply data science to analytical projects in operations, marketing, human resources and finance. Additional topics include experimentation, business cases, team leadership and executive communication. Students will use Python or R, and Dataiku DSS. | |
| DS 5420 | Mainframes to Memes (Past, Present, & Future of Information Technology) (3) |
| Equips students with a historical understanding of computing, information technology, AI, and other forms of digital automation. Throughout, students will learn about how power operates in and through digital infrastructures, and how certain technologies have historically had tendencies to privilege or promote particular social, economic, and political arrangements. | |
| DS 5559 | New Course in Data Science (1 - 4) |
| This course provides selected special topics in data science to graduate and undergraduate students. | |
| DS 6001 | Practice and Application of Data Science (3) |
| Offered Spring 2025 | This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains. |
| DS 6002 | Ethics of Big Data I (2) |
| Offered Spring 2025 | This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives. |
| DS 6003 | Practice and Application of Data Science II (1 - 2) |
| This course covers the practice of data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Students will use their capstone projects to explore the impact of data science on that domain. | |
| DS 6011 | Data Science Capstone Project Work I (1) |
| This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects. | |
| DS 6012 | Ethics of Big Data II (1) |
| This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives. Course was offered Spring 2017, Spring 2016 | |
| DS 6013 | Data Science Capstone Project Work II (1 - 3) |
| Offered Spring 2025 | This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects. |
| DS 6015 | Data Science Capstone (1 - 3) |
| Offered Spring 2025 | Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects. Capstone course for MSDS Online students. Course was offered Summer 2024, Spring 2024 |
| DS 6030 | Statistical Learning (3) |
| Offered Spring 2025 | This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language. |
| DS 6040 | Bayesian Machine Learning (3) |
| Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference. A course covering statistical techniques such as regression. Course was offered Fall 2024, Summer 2024 | |
| DS 6050 | Deep Learning (3) |
| Offered Spring 2025 | A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments. A course covering statistical techniques such as regression. Course was offered Fall 2024, Spring 2024 |
| DS 6051 | Decoding Large Language Models (3) |
| Offered Spring 2025 | Evolution of language models, from encoding words to simple vectors to training LLMs. Train and build LLM, understand concepts like self- and cross-attention in LLMs and their applications, review research on Tokenizers, Retrieval Augmented Generation (RAG), Prompt Engineering, Fine-tuning LLMs using Low-Rank Adapters (LoRA), Quantization in LLMs, QLoRA, In-context Learning (ICL) and Chain-of-Thought (CoT) reasoning. Using Python libraries. |
| DS 6097 | Graduate Teaching Instruction (1 - 3) |
| Graduate Teaching Instruction assessment for Master's Students. | |
| DS 6200 | Computation I: Fundamentals (3) |
| Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced data structures, searching, sorting, greedy algorithms, linear programming, & basics of databases. Will develop computational thinking skills and learn a variety of ways to represent & analyze real-world data. Course was offered Fall 2024 | |
| DS 6210 | Computation II: Numerical Analysis & Optimization (3) |
| Offered Spring 2025 | Many problems in data science essentially boil down to some mathematical relationships that are to be solved numerically. But have you ever wondered how computers could do math? This graduate-level data science course aims to cover fundamental topics of scientific computing, specifically selected and curated for data scientists, including numerical errors, root finding algorithms, numerical linear algebra, and numerical optimization. Course was offered Spring 2024 |
| DS 6234 | Uncertainty in Artificial Intelligence (3) |
| Covers the fundamental concepts of uncertainty in artificial intelligence (AI). Students will explore various techniques and models used to handle uncertainty in AI and machine learning systems, including Bayesian deep learning, dropout as a Bayesian approximation, and decision theory. Will also cover applications of uncertainty in AI, such as computer vision, natural language processing,and autonomous systems. | |
| DS 6300 | Theory I: Probability & Stochastic Processes (3) |
| Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, sampling distributions, discrete and continuous time Markov chains, and Brownian motion. Course was offered Fall 2024 | |
| DS 6310 | Theory II: Inference & Prediction (3) |
| Offered Spring 2025 | Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, Bootstrap methods, ROC curves, shrinkage, and some large-sample theory, prediction optimality versus estimation optimality. Course was offered Spring 2024 |
| DS 6400 | Machine Learning I: Introduction (3) |
| Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and random forests. Students will be required to fit the models (both MLE and Bayesian) and use the strategies discussed in class. Course was offered Fall 2024 | |
| DS 6410 | Machine Learning II: Methods & Application (3) |
| Offered Spring 2025 | Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and network centrality. Course was offered Spring 2024 |
| DS 6501 | Special Topics in Data Science (1 - 3) |
| Course content varies by section and is selected to fill timely, special interests and needs of students. May be repeated for credit when topic varies. Course was offered Spring 2018 | |
| DS 6559 | New Course in Data Science (1 - 4) |
| This course provides the opportunity to offer a new topic in the subject area of data science. Course was offered Fall 2024, Spring 2024, Spring 2019, January 2018, Fall 2017, Fall 2016, Spring 2016 | |
| DS 6600 | Data Engineering I: Data Management & Visualization (3) |
| Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with pandas, data acquisition via flat files, APIs, JSON formats, and webscraping, relational, document, and graph databases, exploratory data analysis including static & interactive data visualization, dashboards, and cloud computing. Course was offered Fall 2024 | |
| DS 6700 | Value I: Data Ethics, Policy and Governance (3) |
| Offered Spring 2025 | Combines topics in data ethics, critical data studies, public policy, governance, and regulation. Address challenges by topic (Health, Education, Culture & Entertainment, Security & Defense, Cities, Environment, Labor). Research how data-centric systems are deployed within socioeconomic ecosystems and shape the world. Interrogate connections between data science, governments, industry, civil society organizations, and communities. Course was offered Spring 2024 |
| DS 6993 | Independent Study (1 - 12) |
| Offered Spring 2025 | Specialized or advanced topics not in DS current course offerings. Requires (a) approval of the program director and (b) an SDS faculty member who will serve as instructor. Propose a syllabus which includes a week-by-week accounting of the topics, materials (papers and textbooks), and assessments. Reach out to the program director for more details. |
| DS 6999 | Independent Study (1 - 12) |
| Graduate-level independent study conducted under the supervision of a specific instructor(s) | |
| DS 7008 | Data Design & Method for Digital Humanists: Practicum for Certificate (3) |
| The DH Certificate Practicum provides principles for working with humanities materials as data, while maintaining a commitment to humanistic inquiry. Students will learn to integrate digital humanities methods into coursework and research required in their home departments. This course provides students with a broad understanding of basic technologies and approaches used by digital humanists and introduces data standards and data modeling. Course was offered Spring 2024 | |
| DS 7200 | Computation III - Distributed Computing (3) |
| Learning tools and concepts for computing on big data. Learn how to use Spark for large-scale analytics and machine learning. Spark is an open-source, general-purpose computing framework that is scalable and blazingly fast. Fundamental data types and concepts will be covered (e.g., resilient distributed datasets, DataFrames) along with Tools for data processing, storage, and retrieval, including Amazon Web Services (AWS). Course was offered Fall 2024 | |
| DS 7400 | Machine Learning III: Deep Learning (3) |
| Covers advanced theoretical concepts for deep neural networks. Topics include convolutional neural networks and their design principles, encoder-decoder architectures, recurrent neural networks, transformers, bounding box detection, image segmentation, generative adversarial networks, diffusion models, etc. Using open-source Python libraries such as NumPy, TensorFlow, and Keras, to understand how theoretical concepts are implemented. Course was offered Fall 2024 | |
| DS 7406 | Machine Learning Systems (3) |
| Offered Spring 2025 | Current state and future trends in Machine Learning Systems are covered. Topics include hardware systems, software systems, and Machine Learning optimized for metrics beyond predictive accuracy. |
| DS 7540 | Machine Learning IV (3) |
| Offered Spring 2025 | Advanced topics within Machine Learning. Course was offered Spring 2024 |
| DS 7700 | Value II: Data and Society (3) |
| Introduces ways that data and information have historically been constructed in different realms--from medicine to public health to computing--to shed light on the power relationships embedded in some of our present-day and near-future tools, systems, and economic relationships. Will use a historical lens, as well as methods from STS, to give an introduction to how data and power interact in people's lives. Course was offered Fall 2024 | |
| DS 7800 | Research Methods in Data Science (3) |
| Offered Spring 2025 | Transition into principal investigators and generators of data science-based knowledge. Develop practical skills necessary to conduct high quality data science research, advance development into producers and critical consumers of research, and further development into professional data scientists broadly defined. Research based career topics covered: time management, research products, types of research positions, and grant writing. Course was offered Spring 2024 |
| DS 8104 | Network Science (3) |
| Networks provide a unifying framework to study the structure hidden within complex data. This graduate-level course focuses on the fundamental concepts and statistics as well as recent advancements and applications of network science. Topics include: graph theory, structural paradoxes, measures and algorithms for quantifying importance, community detection, network inference, recommendation systems, and link prediction. Course was offered Spring 2024 | |
| DS 8998 | Master's Level Thesis Research (1 - 12) |
| Offered Spring 2025 | Engages students in identification of a research question, a review of the literature and the application of an existing data science tool or technique (algorithm) to that problem. This is a mentored experience and will allow the student to demonstrate their capacity for research and begin to develop a relationship with a faculty mentor in Data Science. Course requires instructor permission. |
| DS 9999 | Dissertation Research (1 - 12) |
| Offered Spring 2025 | PhD level Dissertation Research. |