DASC - Data Science

An introduction to computational problem-solving in the context of data science. This course introduces students to the programming language Python and how to use it as a tool for problem-solving. The course utilizes illustrative examples to help students grasp the fundamental concepts and reinforces their understanding through a variety of practical exercises. No prior programming experience is required to take this course.

This course investigates how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Social implications of the digital revolution, including ethical issues associated with algorithmic design and privacy will be examined. Students will use a sociological lens to explore how our increasingly digital lifestyle changes institutions and social relations.

Python provides several libraries which facilitate data manipulation, processing, analysis, and visualization. This course will introduce standard Python packages used for Data Science, including pandas, numpy, seaborn, matplotlib, and scikit-learn. By the end of the course, students will be equipped to create and modify existing Python code to explore a range of data sets.

This course focuses on problem solving and programming in Python. Emphasis is placed on common algorithms and programming principles utilizing the standard library distributed with Python. Upon completion, students should be able to design, code, test, and debug Python language programs.

This course provides an interdisciplinary overview of data sciences drawing on key elementary topics related to data analytics. A specific focus is given to the way that decisions made about data from those disciplinary pursuits inform policy, product development, and humanity. Topics addressed include elements of data, data collection, the connections between machine learning and data, survey research, programming with Python and R, statistical learning, model evaluations, digital engineering, and ethical uses of data.

This course explores artistic foundations for visualization including art theory and aesthetics, color theory, composition and layout. We will also discuss the psychology of visual perception, and semiotics and the underlying nature of symbolic representation. Students will gain experience applying these principles by sketching and using technological tools, and will also critically evaluate visualizations based on the theories discussed.

This course focuses on the design and creation of effective visualizations for communicating data. The Python programming language will be used to create static and interactive visualizations for a variety of data types including tabular, text, and geographic data. Students will develop a portfolio and also will gain experience in analyzing, interpreting, and revising visualizations created by others.

This course explores, from a philosophical perspective, ethical questions arising from collecting, drawing inferences from, and acting on data, especially when these activities are automated and on a large scale. This course will provide students a framework for considering the ethical implications of data usage. Emphasis will be placed on discussing how historic and contemporary examples of potentially unethical practice could be altered to reduce harm and increase equity. Topics to be covered may include, but are not limited to, systematic approaches to assessing ethical issues; privacy and confidentiality; defining research and the responsibilities associated with conducting ethical research; implicit and structural biases in data collection and analysis; freedom of speech; and consent to data collection.

This course allows students to work for an employer in a position related to data science. Students must work for 50 hours per course credit and complete course assignments.

Data storytelling combines data, narrative, and visualizations to communicate insights and influence decision-making. This course will present the conceptual basis for storytelling and techniques for narrative development, as well as leverage technical skills to analyze and visualize data. By the end of the course, students will have experience in developing cohesive data-driven stories using a variety of platforms and tools.

This course introduces students to the basic concepts of neural network–based deep learning, a central pillar of modern artificial intelligence. The course examines how neural networks learn from data and support a wide range of applications, including computer vision and natural language processing. Emphasis is placed on model architecture, training, and performance evaluation. By the end of the course, students will develop both a basic understanding and practical experience in deep learning models.

This course explores how artificial intelligence can accelerate and strengthen the data science pipeline, from defining questions to communicating insights. Students will examine how AI assists in refining problem statements, discovering and augmenting data, cleaning and preparing datasets, analyzing patterns, and optimizing models. The course also considers how AI can enhance clarity and impact when presenting findings. Emphasis will be placed on improving pipeline speed, scalability, and reliability, while addressing ethical considerations such as bias, transparency, and accountability. By the end of the course, students will gain both theoretical foundations and practical skills to design faster, smarter, and more responsible data science pipelines using AI.

Students work individually or in groups to plan, design, and carry out a research project demonstrating expertise with data science. Final papers that report the results for the study are presented in a formal research seminar. The projects reflect knowledge gained from undergraduate work and training received in discipline-specific research methods and statistics courses. This is a writing intensive course.

In this course, students will be introduced to modern methods of data collection (including scientific surveys), gathering (including API use), and sourcing (including various data repositories). Emphasis will be placed on matching data collection/gathering goals with project goals to ensure sourced data meets project standards. Students will also learn about how to assess data quality from multiple frameworks, including the Total Data Error, among others.

This course will introduce students to various approaches and frameworks for Prompt Engineering to systematically frame task requests for Large Language Models. Emphasis on prompt typology, structure, robustness and creation will ensure students are conversant in modern approaches for incorporating LLM technologies and tools within the research, development and production workflows. Students will learn about different prompt approaches for various kinds of tasks and learn how to create and evaluate prompts for robustness across LLM versions and models.

This course covers the fundamentals of computer vision with a focus on (1) foundations of image processing (topics: image signals, imaging systems, and linear image processing), (2) basics of machine learning for computer vision (topics: foundations of machine learning, neural architectures for vision, and best practices of training vision models), and (3) modern applications of computer vision (topics: computer vision tasks such as object detection, semantic segmentation, and representation Learning).

This course is designed to help students enhance their personal and professional development through innovation guided by faculty members and professionals. It offers students an opportunity to integrate disciplinary theory and knowledge through developing a nonprofit program, product, business, or other initiative. The real-world experiences that entrepreneurships provide will help students understand how academic knowledge leads to transformations, innovations, and solutions to different types of problems. The course can be delivered either as an independent project for individual students or as group projects similar to those sometimes offered in topics courses.

The advanced study of selected topics designed to permit small groups of qualified students to work on subjects of mutual interest which, due to their specialized nature, may not be offered regularly. These courses will appear in the course schedule, and will be more fully described in information distributed to academic advisors.

Independent reading and study on a topic to be selected under the direction of an instructor. Conferences and papers as appropriate.

This course introduces students to the basic concepts of neural network–based deep learning, a central pillar of modern artificial intelligence. The course examines how neural networks learn from data and support a wide range of applications, including computer vision and natural language processing. Emphasis is placed on model architecture, training, and performance evaluation. By the end of the course, students will develop both a basic understanding and practical experience in deep learning models.

In this course, students will be introduced to modern methods of data collection (including scientific surveys), gathering (including API use), and sourcing (including various data repositories). Emphasis will be placed on matching data collection/gathering goals with project goals to ensure sourced data meets project standards. Students will also learn about how to assess data quality from multiple frameworks, including the Total Data Error, among others.

This course will introduce students to various approaches and frameworks for Prompt Engineering to systematically frame task requests for Large Language Models. Emphasis on prompt typology, structure, robustness and creation will ensure students are conversant in modern approaches for incorporating LLM technologies and tools within the research, development and production workflows. Students will learn about different prompt approaches for various kinds of tasks and learn how to create and evaluate prompts for robustness across LLM versions and models.

This course covers the fundamentals of computer vision with a focus on (1) foundations of image processing (topics: image signals, imaging systems, and linear image processing), (2) basics of machine learning for computer vision (topics: foundations of machine learning, neural architectures for vision, and best practices of training vision models), and (3) modern applications of computer vision (topics: computer vision tasks such as object detection, semantic segmentation, and representation Learning).

The advanced study of selected topics designed to permit small groups of qualified students to work on subjects of mutual interest which, due to their specialized nature, may not be offered regularly. These courses will appear in the course schedule and will be more fully described in information distributed to academic advisors.

Independent reading and study on a topic to be selected under the direction of an instructor. Conferences and papers as appropriate.

This course provides foundational programming skills essential for future coursework in data science. Designed for students with little to no prior programming experience, it develops essential skills in programming and problem-solving, equipping students with the ability to manipulate data, perform basic analyses, and create visualizations. The course emphasizes writing clean, efficient, and reproducible code.

This course will serve as an introduction for modeling data using probability and statistical methods. Topics include basic concepts of probability, Bayes theorem, frequently-occurring discrete and continuous probability distributions, as well as how to simulate data from these distributions. Basic properties of the probability distributions will be discussed, which will provide an insight into the use of these distributions in data science. R and/or Python will be the computation software used in class. This course is open only for students enrolled in the M.S. degree program in Data Science and Analytics.

This course will cover both classical and modern statistical methods used within data science. Concepts related to hypothesis testing, fundamentals of experimental design and analysis will be discussed along with tests of association for categorical data. Statistical methods that are often included in machine learning methods like sampling and bootstrapping are also included.

This course will explore data science as a burgeoning field. Students will learn fundamental principles and techniques that data scientists employ to mine data. They will investigate real life examples where data is used to guide assessments and draw conclusions. This course will introduce software and computing resources available to a data scientist to process, visualize, and model different types of data including big data. Cross-listed with CS 620.

Requirements will be established by the School of Data Science and will vary with the amount of credit desired. Allows students an opportunity to gain a short duration career-related experience.

Students demonstrate an ability to integrate and synthesize competencies from their certificate or degree program coursework applied to concentration areas. Students produce high quality written products and an e-portfolio that demonstrate the analysis, synthesis and intersection of AI knowledge with specific domains.

The culminating course in the proposed MS in Data Science and Analytics degree program will bring students together with faculty and external partners. In consultation with a faculty advisor and a business or industry or government representative, students will be required to develop a project that aims to solve a data science/analytics problem in a real-world business, industry, or government setting. Faculty and business/industry/government representatives will serve as external mentors for the students during this experience. Note that an external mentor is not mandatory but encouraged.

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science.

Independent study under the direction of an instructor.

Departmental permission required

This course covers key components of deep learning framework, including loss functions, regularization, training and batch normalization. The course also covers several fundamental deep learning architectures such as multilayer perceptrons, convolutional neural network, recurrent neural network and transformers, as well as some advanced topics such as graph neural network and deep reinforcement learning. The class activities include traditional lectures, paper reading and presentation, and projects.

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging.

Laws in many countries and states within the U.S. require that predictive models impacting humans be accompanied by an understandable interpretation, yet many such models are based on so called black box models that can’t be easily interpreted or explained. This course will enable students to produce explanations and interpretations for advanced ML and AI algorithms. It will review the state of the science methods for interpretable ML and explainable AI, including graphical and contextual approaches as well as model agnostic and model specific methods for generating understandable explanations and interpretations. The course will also introduce the concepts of algorithmic bias and model fairness as they relate to explanation and understanding.

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science.

This course covers key components of deep learning framework, including loss functions, regularization, training and batch normalization. The course also covers several fundamental deep learning architectures such as multilayer perceptrons, convolutional neural network, recurrent neural network and transformers, as well as some advanced topics such as graph neural network and deep reinforcement learning. The class activities include traditional lectures, paper reading and presentation, and projects.

This course introduces the basic concepts of computational imaging. The topics include principles of imaging systems, role of computational methods in enhancing imaging systems, computational imaging inverse problems, and data-driven machine learning approaches to solve inverse problems in computational imaging.

Laws in many countries and states within the U.S. require that predictive models impacting humans be accompanied by an understandable interpretation, yet many such models are based on so called black box models that can’t be easily interpreted or explained. This course will enable students to produce explanations and interpretations for advanced ML and AI algorithms. It will review the state of the science methods for interpretable ML and explainable AI, including graphical and contextual approaches as well as model agnostic and model specific methods for generating understandable explanations and interpretations. The course will also introduce the concepts of algorithmic bias and model fairness as they relate to explanation and understanding.

This course explores the application of AI in health sciences, focusing on machine learning, NLP, computer vision, generative AI techniques for diagnostics, treatment planning, patient monitoring, and biomedical research. It covers precision medicine, ethical AI, and the integration of AI into practice. Students will gain a deep understanding and practical skills to develop innovative AI solutions that address real-world challenges in health sciences.

This course provides a deep dive into the foundations and current advancements in generative AI. It covers key concepts such as transformer models, GANs, VAEs, LLMs, and their applications across various fields, emphasizing both theory and hands-on learning, including ethical considerations such as fairness and bias mitigation. Students will develop a comprehensive understanding of generative AI and gain practical experience.

Provides the advanced student with an opportunity to study and investigate a variety of topics in the field of data science.