Information Science
Master of Science
Quick Facts
Top 1%
of all Higher-Ed
Institutions
- Center for World University Rankings, 2025
#14
in Top Master's
Machine Learning Programs
- TechGuide, 2025
The Master of Science in Information Science (MSIS) from the University of Arizona’s College of Information Science prepares you to lead in today’s data-driven, technology-focused world. This STEM-designated program can be completed in as few as 18 months and is designed for flexibility, allowing you to build advanced skills while balancing your existing commitments.
You’ll develop a strong foundation in information science with hands-on experience in areas like data analysis, machine learning, artificial intelligence and information management. The program blends technical expertise with real-world application, helping you understand not just how systems work, but how people interact with them.
Students can tailor their experience by choosing between two areas of emphasis:
Human-Centered Computing
Human-centered computing courses explore topics like simulations, virtual reality, human-computer interaction, user experience and personal data-collection. The emphasis includes an additional core course in human-centered computing and a variety of focused electives.
Machine Learning
Machine learning focuses on the interpretation and management of large amounts of data by automating the processes by which models of data are built. The emphasis includes an additional core course in machine learning and a variety of focused electives.
Whether you’re interested in user experience, virtual environments and interaction design, or in building intelligent systems and working with large-scale data, you’ll gain practical skills that translate directly to high-demand careers.
Graduates are prepared to pursue roles across a wide range of industries, including data science, cybersecurity, software development and information technology leadership – equipped with the interdisciplinary knowledge needed to thrive in the evolving information economy.
*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.
The electives for this program include:
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 include 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.
Virtual reality is an emerging technology that has been widely used in recent years in various areas, such as education, training, well-being, and entertainment. Virtual reality offers a highly immersive experience as the head mounted displays replace the vision of the users with digital imagery. It encompasses many disciplines, such as computer science, human computer interaction, game design and development, information science, and psychology. This course merges a theoretical and practical approach to give students the necessary knowledge to design, develop, and critique virtual reality games and applications.
Algorithms are a crucial component of game development. This course will provide students with an in-depth introduction to algorithm concepts for game development. The course will cover basic algorithm and data structures concepts, basic math concepts related to game algorithms, physics and artificial intelligence based game algorithms that are supplemented with modern examples. Unity Game Engine along with C# programming language will be used throughout the class.
The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory). Graduate-level requirements include additional reading of supplementary material, more rigorous tests and homework assignments, and a more sophisticated course project, sophisticated application and technique.
This course provides a comprehensive survey of video game production practices. Students work on game development assignments for presentation in a professional portfolio. The course topics include: collaborative technologies, software design patterns for games, spatial transformations, and technical considerations surrounding game art, such as authoring sprites, 3D models, animations, texture mapping, and writing shaders. Students will be given periodic assignments that reinforce lessons from class.
Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.
Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.
Study of the user interface in information systems, of human computer interaction, and of website design and evaluation. Graduate-level requirements include group work and longer examinations.
Outcomes
Skills
Earning your Master of Science in Information Science will build core skills, including:
- Computational thinking
- Machine learning
- Data analysis
- Data privacy and security
- Information technology
- Information management
- Artificial Intelligence
- Extended reality
- Virtual reality
- Cybersecurity
- Information architecture
- Software development
- Web production
Potential Career Paths
Graduates of the Information Science program will be prepared to pursue careers in the following fields:
Areas of Emphasis
Human-centered computing courses explore topics like simulations, virtual reality, human-computer interaction, user experience and personal data-collection. The subplan includes an additional core course in human-centered computing and a variety of focused electives.
The core curriculum for this emphasis includes:
This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas.
The field of Human-Computer Interaction (HCI) encompasses the design, implementation, and evaluation of interactive computing systems. This course will provide a survey of HCI theory and practice. The course will address the presentation of information and the design of interaction from a human-centered perspective, looking at relevant perceptive, cognitive, and social factors influencing in the design process. It will motivate practical design guidelines for information presentation through Gestalt theory and studies of consistency, memory, and interpretation. Technological concerns will be examined that include interaction styles, devices, constraints, affordances, and metaphors. Theories, principles and design guidelines will be surveyed for both classical and emerging interaction paradigms, with case studies from practical application scenarios. As a central theme, the course will promote the processes of usability engineering, introducing the concepts of participatory design, requirements analysis, rapid prototyping, iterative development, and user evaluation. Both quantitative and qualitative evaluation strategies will be discussed. This course is co-convened: Upper-level undergraduates and graduate students are encouraged to enroll. Graduate students will be expected to complete more substantial projects and will be given more in-depth reading assignments.
This course provides an overview of the various concepts and skills required for effective data visualization. It presents 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.
Machine learning focuses on the interpretation and management of large amounts of data by automating the processes by which models of data are built. The subplan includes an additional core course in machine learning and a variety of focused electives.
The core curriculum for this emphasis includes:
This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas.
Machine learning describes the development of algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.
This course provides an overview of the various concepts and skills required for effective data visualization. It presents 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.
