Data Science (DATA) Courses

College of Natural and Health Sciences (CNHS)

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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. (Same as CS 171)

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. (Same as CS 172)

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)

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. Recommended: C or better in CS 171 or computer programming experience. (Same as MATH 271)

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: CS/DATA 172 and MATH 241, which may be taken concurrently. (Same as CS 272)

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 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 CS/DATA 171. (Same as CS 370)

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 & Privacy (3) This course studies the numerous privacy and security 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. Primarily taught in Python. Pre: C or better in CS/DATA 172. (Same as CS 373)

DATA 375 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. Pre: C or better in CS 171 or CS 172, C or better in MATH 271 or MARE 250 or Instructor's Consent.

DATA 465 Text Mining for Social Science (3) Provides the concepts and tools to understand the role of natural language processing and text analytics for managerial decision-making and how to apply text analytics tools to real-world problems. Pre: C or better in one of QBA/DATA 260 or QBA/DATA 300 or QBA/DATA 362 or CS/DATA 172, or Instructor's Consent. (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 CS 171 or CS 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. Pre: C or better in CS 171 or CS 172, C or better in MATH 271 or MARE 250 or Instructor's Consent. This course is dual listed with CBES 674. (Previously offered as DATA 375).

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 241, and any of the following: CS/DATA 272, CS321, or CS440. (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.