College of Integrated Studies
Master of Applied Data Science (MADS)
The Master of Applied Data Science (MADS) provides an applied approach to data science and is oriented towards working professionals. In the current economic environment, as every industry and sector of the economy transitions to digital models of operation, data science knowledge and expertise is in great demand. Many working professionals must acquire an understanding of data science methods to contribute to digital transformation projects and the ongoing operation of digitally transformed organizations. These professionals seek a practical approach to data science, focusing on the skills for responsible use of data science methods and tools. Because data science tools are used in every aspect of the modern organization, these professionals have a variety of educational backgrounds and may lack traditional preparation in subjects like computer science and statistics that form a significant part of data science. In response to this need, the MADS degree innovatively combines concepts and skills from many disciplines to enable learners to gain a mastery of data science concepts along with practical, hands-on experience in using data science methods. To address the needs of working professionals, the degree is designed to be offered fully online. To ensure the accessibility of the program, the degree has been designed to obviate the need for prerequisite courses that add demotivating friction to a learner's ability to commence the program. Coursework provides deep engagement with data modeling, collection, analysis (including statistical, machine learning, and deep learning AI approaches), computation, and integration. Students learn how to present data-driven discoveries and craft data-intensive solutions to real-world problems while understanding the legal and ethical context of their work. A capstone practicum class provides experience in the complete data science lifecycle applied to a real-world problem in the student's field of choice.
Required Courses
| DSCI 6113 | Programming for Applied Data Science | 3 |
| DSCI 6122 | R Lab for Applied Data Science | 2 |
| DSCI 6133 | Applied Data Visualization | 3 |
| DSCI 6204 | Applied Statistical Methods for Data Science | 4 |
| DSCI 6214 | Applied Machine Learning for Data Science | 4 |
| DSCI 6301 | Data Science Project Management | 1 |
| DSCI 8133 | Foundations of Applied Data Science I | 3 |
| DSCI 8143 | Foundations of Applied Data Science II | 3 |
| DSCI 8224 | Applied Neural Networks and Deep Learning for Data Science | 4 |
| DSCI 8413 | Applied Graduate Data Science Capstone | 3 |
| Total Hours | 30 | |
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)