College of Integrative Studies
Bachelor of Science in Data Science
The Bachelor of Science in Data Science is an interdisciplinary program that draws upon disciplines from multiple colleges. It is a 123-hour inter-college program designed to include three general areas of coursework: general education, program core, and applications of the data science fundamentals in specific body of knowledge such as geoinformatics, computational intelligence and cybersecurity, marketing, management information systems, statistical modeling, social science analytics, architectural design and built environment, and smart agriculture. The overall curriculum is designed to provide students with an ideal educational experience necessary to become effective professional data science experts. Under the proposed undergraduate curriculum, general education coursework will help data science students develop intellectual curiosity, critical thinking, and ethical and aesthetic awareness. The coursework for the core program will provide students with the opportunity to build a strong foundation in the key fields of data science that include computer science, mathematics and statistics, management information systems, communication, management/leadership, design, and ethics. The course sequences for several distinct areas of academic concentration will provide students with the opportunity to become data science experts in a specific area. Students must earn a grade of C or higher in the two-semester senior capstone courses included in each of the concentrations.
DSCI 2012 Data Science Lab: Data Wrangling: 2 hours.
Four hours laboratory. Practical application of data science tools to clean, format, and work with data
DSCI 2013 Data Science Literacy: 3 hours.
Three hours lecture. Introduction to data science as a field that represents the world and society through data objects, extracts new knowledge from these data objects, and creates artificially intelligent systems that perform tasks while producing further insights that improve the performance of institutions, organizations, businesses, and society
DSCI 2022 Data Science Lab: Cloud, High-Performance, and Quantum Computing: 2 hours.
(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of cloud GPU/TPU, high-performance, or quantum computing platforms to perform computing tasks for big data analysis tasks
DSCI 3012 Data Science Lab: Description, Analysis, and Inference: 2 hours.
(Prerequisite: MA 3123). Four hours laboratory. Hands-on programming work to use descriptive, inferential, predictive, and prescriptive statistical techniques with a variety of data types
DSCI 3013 Fundamentals of Data Acquisition: 3 hours.
Three hours lecture. An introduction to the fundamentals of data and data acquisition for data science
DSCI 3022 Data Science Lab: Data Visualization: 2 hours.
(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of tools and programming libraries to visualize data using common approaches to the visual display of numerical, conceptual, and geospatial information
DSCI 3032 Data Science Lab: Artificial Intelligence: 2 hours.
(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of artificial intelligence and machine-learning libraries to train models in areas such as natural language processing, computer vision, and classification
DSCI 4000 Directed Individual Study in Data Science: 1-6 hours.
Hours and credits to be arranged
DSCI 4013 Data Visualization: 3 hours.
Three hours lecture. Course providing theoretical foundation for data visualization. Deals with external representation and interactive manipulation of information, data, or artifacts using digital tools to enhance communication, analytical reasoning, and decision-making. (Same as CSE 4423)
DSCI 4553 Data Science Capstone 1: 3 hours.
(Prerequisite: Senior Standing). The first of two, faculty-directed, individual, project-based, three-hour capstone courses open only to candidates for the Bachelor of Science in Data Science degree. A grade of C or higher is required in DSCI 4553 and DSCI 4663
DSCI 4663 Data Science Capstone 2: 3 hours.
(Prerequisites: Senior Standing, Grace C or higher in DSCI 4553). The second of two, faculty-directed, individual, project-based, three-hour capstone courses open only to candidates for the Bachelor of Science in Data Science degree. A grade of C or higher is required in DSCI 4553 and DSCI 4663
DSCI 6113 Programming for Applied Data Science: 3 hours.
One hour lecture, two hours laboratory. Computer programming and data wrangling through practical application of Python and other data science tools to clean, format, and work with real datasets
DSCI 6122 R Lab for Applied Data Science: 2 hours.
Two hours laboratory. Introduction to programming, data wrangling, and data exploration through practical application of R and associated tools to clean, format, and work with real datasets from various fields
DSCI 6133 Applied Data Visualization: 3 hours.
(Prerequisite: DSCI 6113 Programming for Applied Data Science). One hour lecture, two hours laboratory. Explore and understand data visually, communicate meaning visually, and create interactive visualizations using industry-standard tools and programming languages
DSCI 6204 Applied Statistical Methods for Data Science: 4 hours.
(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Select and apply appropriate statistical methods and data science technologies to achieve analytical objectives. Write code to apply descriptive, inferential, predictive, and prescriptive statistical techniques for a variety of data types and purposes
DSCI 6214 Applied Machine Learning for Data Science: 4 hours.
(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Select and apply appropriate machine learning methods and data science technologies to implement and optimize non-artificial neural network approaches to inference, planning, and classification projects. Learn to use GPUs for computing and estimation
DSCI 6301 Data Science Project Management: 1 hour.
One hour Lecture. A practical introduction to project management in the context of data science projects
DSCI 7000 Directed Individual Study in Data Science: 1-6 hours.
Hours and credits to be arranged
DSCI 8013 Data Science Literacy Pedagogy 1: Governance, Ethics, and Data Science Applications: 3 hours.
Three hours lecture. General subject-matter introduction to the field of data science and data science instruction with a focus on governance, ethics, and data science applications in many fields
DSCI 8023 Data Science Literacy Pedagogy 2: Technical Overview of Data Science Methods & Strategies: 3 hours.
Three hours lecture. General subject-matter introduction to the field of data science and data science instruction with a focus on data science methods and strategies
DSCI 8033 Data Science Classroom Integration: 3 hours.
Three hours lecture. Applying and integrating principles of data science into the context of the classroom. Topics include importance of data science across the domain; digital citizenship; career exploration; and an historical perspective on analyzing, posing, and solving problems using data
DSCI 8133 Foundations of Applied Data Science I: 3 hours.
Three hours lecture. Introduction to data science as a field that advances methods to improve the use of data for human progress
DSCI 8143 Foundations of Applied Data Science II: 3 hours.
Three hours lecture. In-depth engagement with all phases of the data science lifecycle including data modeling and acquisition, storage, analysis, and building smart systems
DSCI 8224 Applied Neural Networks and Deep Learning for Data Science: 4 hours.
(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Learn the fundamentals of artificial neural networks. Use AI/ML libraries to implement artificial neural network approaches to computer vision, natural language processing, reinforcement learning, and generative projects. Deepen ability to use GPUs for computing
DSCI 8413 Applied Graduate Data Science Capstone: 3 hours.
Three hours lecture. Faculty-directed capstone for the Master of Applied Data Science program in which students use the principles and practices of data science to address a challenge within the student's subject area focus
DSCI 8990 Introduction to Data Science Literacy Instruction: 1-9 hours.
Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years)