Data Science (DATA) Courses

DATA 101 Awesome Data Science Skills (3) Introduction to Data Science. Students will learn super awesome data science skills to better understand the world. No prerequisites are required. (Attributes: GQ)

DATA 171 Data Science Fundamentals in R (3) (lecture/lab) Introduction to the field of data science. Focus on communicating narratives regarding the underlying patterns in the data, i.e. storytelling with data. Topics include R programming fundamentals, data properties, visualization, importing, cleaning, and transforming data. No prior programming experience required. Pre: C or better in MATH 135T or higher, or placement into MATH 140 or higher. (Attributes: GQ)

DATA 172 Python for Data Analysis (3) (lecture/lab) Fundamentals of Python programming for the analysis of real-world datasets. Topics include writing scripts and programs in Python and tools for cleaning, manipulating, and visualizing data. Introduction to intelligent analysis techniques. Properties of domain-specific datasets. No prior programming experience required. Pre: C or better in MATH 135T or higher, or placement into MATH 140 or higher. (Attributes: GQ)

DATA 180 Intro to Prompt Eng. & AI (3) This course introduces undergraduate students from all majors to the foundations of Artificial Intelligence (AI), Large Language Models (LLMs), and Prompt Engineering. Students will explore AI tools such as ChatGPT, Gemini, and multimodal platforms that generate text, images, audio, and video. With a focus on hands-on learning and no coding required, students will practice designing effective prompts, explore real-world datasets, and create AI-assisted projects in their own fields (art, history, business, engineering, etc.). The course emphasizes critical thinking and ethical reflection alongside practical skills. The semester culminates in a discipline-specific AI project where students apply AI tools to produce an analysis, artifact, or creative work supported by a written report. (Same as CS 180 and QBA 180) (Attributes: GCC, GQ)

DATA 200 Intro to Business Analytics (3) An introduction to quantitative modeling and data-driven decision-making used in Business Analytics. Includes the basic concepts and mathematical tools to understand the role of quantitative analytics in organizations; application of analysis tools and interpretations of model outputs for effective communication. (Same as QBA 200) (Attributes: GQ)

DATA 210 Integrated Data Science (3) This course integrates Indigenous knowledge with data science for management. Students work with stewardship groups, use R and GIS to analyze ecological data, and deliver projects in aquaculture, fisheries, native species, or ecosystem health while building technical and cross-cultural skills.

DATA 271 Applied Statistics with R (3) Introduction to probability and statistics, with an emphasis on applied use of the R statistical computing system. Topics include categorical and quantitative random variables, probability distributions, descriptive statistics, estimation, hypothesis testing, and linear regression. Pre: C or better in MATH 135T or higher, or placement into MATH 140 or higher; C or better in DATA 171 or instructor consent. (Same as MATH 271) (Attributes: FQ, GQ)

DATA 272 Machine Learning for Data Sci (3) How to use data to automatically understand the world, make complex decisions, and even predict the future. Focuses on helping students do more with data by understanding and using a wide variety of machine learning tools. Taught in Python. Pre: DATA 172 and MATH 241, which may be taken concurrently.

DATA 315 Math Methods for Data Science (3) A collection of mathematical and computational techniques for data analysis. Topics include numerical integration and optimization in multiple dimensions, pseudorandom number generation, Markov Chains and MCMC samplers, and an introduction to Bayesian statistics. Pre: MATH 211, MATH 241, MATH 271

DATA 362 Business Analytics (3) Fundamentals of Business Analytics. This course aims to teach students to analyze, formulate, and solve managerial decision-making problems using quantitative models and techniques. Pre: C or better in QBA 200 or QBA 260. (Same as QBA 362)

DATA 367 AI-Driven Business Analytics (3) Introduces AI-driven business analytics for decision-making. Covers AI-assisted data acquisition, exploratory analysis, predictive and prescriptive modeling, with emphasis on oversight, ethics, and effective communication. Pre: C or better in one of QBA 180, QBA 260, QBA 300, QBA 362, DATA/MATH 271 or MATH 115. (Same as QBA 367)

DATA 370 Data Management (3) Fundamentals of relational database usage and management from a data science perspective. Topics include properties of multi-table data, the entity- relationship data model, SQL for single and multiple table queries and updates, and communicating with databases using R. Pre: C or better in DATA 171.

DATA 371 Multivariate Modeling with R (3) Multivariate statistical methods and model selection using R. Topics include the multivariate normal distribution and covariances, multiple regression, analysis of variance, principal component analysis, logistic regression, and decision trees. The course will emphasize model selection and techniques such as validation sets to address the problem of overfitting. Pre: C or better in MATH 271. (Same as MATH 371)

DATA 373 Data Security and Ethics (3) This course studies the numerous security, privacy and ethics issues that arise when gathering, storing, analyzing, and distributing data. This course will teach students about the fundamental underpinnings of security & privacy as well as give practical, hands-on experience designed to help data scientists identify and resolve real-world issues. Topics include differential privacy, database security, server security, data ethics, machine learning safety, and data integrity. The course will also cover security, privacy and ethics issues surrounding large AI systems. Primarily taught in Python. Pre: C or better in DATA 172.

DATA 445 Natural Language Processing (3) A survey of the field of natural language processing, spanning both statistical and neural approaches for analyzing, transforming, and generating language. Topics include text classification, parsing, embeddings, neural networks, language models, transformers, agents, machine translation, computational linguistics. Pre: CS 440 or DATA 272. (Same as CS 445)

DATA 465 NLP and GenAI in Business (3) Hands-on NLP and generative AI for business: classical NLP, LLMs, prompt engineering, agents, and governance. Pre: C or better in one of QBA 180, QBA 260, QBA 300, QBA 362, DATA/MATH 271 or MATH 115. (Same as QBA 465)

DATA 470 3D Mapping of Ecosystems (3) Introduction and application of 3D habitat mapping to study natural environments. Students will learn the fundamentals of photogrammetry and geomatics and learn to integrate and analyze multiple data products. Pre: C or better in DATA 171 or DATA 172.

DATA 474 Applied Informatics (3) Introduction to the theory and application of informatics tools used in Marine and Natural Sciences. Students will learn the fundamentals of data management, data analytics, ecoinformatics, bioinformatics, and data visualization. (Previously offered as DATA 375) Pre: C or better in DATA 171 or DATA 172, C or better in MATH 271 or MARE 250 or Instructor's Consent. (Same as MARE 474) This course is dual listed with CBES 674.

DATA 483 Computer Vision (3) A survey of the field of computer vision. Covers both classic as well as deep learning approaches to analyzing images and video. Topics covered include keypoint features, object detection, multi-object tracking, and interacting with humans. Pre: MATH 211, and either DATA 272 or CS 321. (Same as CS 483)

DATA 490 Data Science Capstone (3) Students are asked to use the skills and techniques they have learned throughout the data science program to create a capstone project. The content of the course will additionally focus on giving students skills in written and oral communication. Note: Restricted to Data Science students only. Pre: Senior class standing or Instructorʻs Consent.

DATA 495 Data Science Seminar (1) This course will offer lectures, discussions, and research reports of topics in data science presented by faculty, students, invited speakers, and visiting scholars. It will also help students become aware of research and job opportunities, in both academia and industry. Pre: Senior standing with a major in Data Science, or instructor's consent.

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DATA x94 Special Topics in Subject Matter (Arr.) Special topics chosen by the instructor. Course content will vary. May be repeated for credit, provided that a different topic is studied. Additional requirements may apply depending on subject and topic.

DATA x99 Directed Studies (Arr.) Statement of planned reading or research required. Pre: instructor’s consent.