Data Science

Master of Science

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Quick Facts

Credits Required: 30

Cost Per Credit: $650.00

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College of Information Science

Program Details

Earn your Master of Science in Data Science and join the next generation of professionals and researchers with the knowledge, skills and expertise to engage in data science processes.

This degree encompasses a blend of disciplines to address contemporary and future information science challenges. Engage in issues at the intersection of technology, people and information, drawing upon a central foundation in information science. Through this program, you will gain training in data collection, exploration, manipulation and storage, analysis and presentation in order to navigate data-rich workplace environments.

The University of Arizona Online also offers stackable graduate certificates that can be earned for elective credit toward the MS in Data Science. The Graduates Certificates in Foundations of Data Science and Natural Language Processing provide credits and experience that are directly transferable into this full degree program. You may also choose to pursue one of these graduate certificates concurrently with your degree program and graduate with both credentials. Stackable credentials provide you with opportunities to develop a variety of skills and knowledge appropriate to your specific area of study.

View detailed program information

Applicants are expected to have completed undergraduate coursework in programming and statistics. Coursework in calculus is preferred but not required. The application will have two explicit questions asking to list coursework or professional experience that demonstrates strong quantitative and analytical reasoning abilities as well as experience with math and programming, including data structures, analysis of algorithms, and linear algebra.

If you decide to pursue the MS in Data Science, you can apply at any point (within four years of the completion of a certificate) and synthesize your experiences through a capstone project.

*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.

Courses

The core courses required for this program include:

This course explores ethical challenges stemming from data-driven decision making in society. Students will focus on important topics like bias, fairness, privacy, surveillance, discrimination, as well as data collection, storage, and management. Exploring dilemmas tied to data science, artificial intelligence, robotics, etc. will allow students to consider their own data behaviors as well as trends and problems across contexts like organizations, social media, health, and education. Special attention in the class will be given to matters of policy and governing protocols around the world. Related challenges tied to Internet governance, misinformation, fake video, automation, etc., will also be explored

This course presents an overview and understanding of the intractable and pressing ethical issues as well as related policies in the information fields. Emerging technological developments in relation to public interests and individual well-being are highlighted throughout the course. Special emphasis is placed on case studies and outcomes as well as frameworks for ethical decision-making.

This course presents fundamental aspects of data science, including Python programming (e.g., data collection, cleaning, visualization), statistics, and mathematics (e.g., linear algebra and calculus). The course establishes the foundation for advanced data-intensive classes, providing both theoretical understanding and practical knowledge essential for comprehending Data Science and its applications.

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course includes laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.

This course provides an overview of the various concepts and skills required for effective data visualization. It presents the principles of graphic design, programming skills, and statistical knowledge required to build compelling visualizations that communicate effectively to target audiences. Visualization skills addressed in this course include choosing appropriate colors, shapes, variable mappings, and interactivity based on principles of color perception, pre-attentive processing, and accessibility.