Artificial Intelligence for Business
Master of Science
Quick Facts
Top 1%
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- Center for World University Rankings, 2024
#1
In Alumni Earnings
Potential Among Arizona's
Public Universities
- Payscale, 2024
In a world reshaped by automation, big data, and AI-driven decision-making, organizations urgently need professionals who can translate advanced technologies into strategic impact. The Master of Science in Artificial Intelligence for Business (MS-AIB) at the University of Arizona uniquely prepares you to lead in this new era by combining deep technical skills with ethical insight and business intelligence.
This 12-month, fully online program draws on U of A’s nationally ranked expertise across multiple disciplines—merging the analytical power of computer science and data mining with the managerial perspective of economics, operations, and AI governance. Students benefit from a robust, interdisciplinary experience anchored by the top-ranked Management Information Systems (MIS) Department and 35 years of AI leadership through the Eller MIS AI Lab.
You'll explore advanced topics like machine learning, generative AI, big data technologies, deep learning, and network analysis while also diving into high-impact applications in fields such as cybersecurity, healthcare, and digital platforms. Courses on AI ethics, policy, and governance ensure you are not only a capable technologist but a responsible leader prepared for today’s regulatory and societal expectations.
Through hands-on projects students develop practical solutions to organizational challenges and build the skills to shape intelligent automation, AI strategy, and enterprise transformation. Whether your goal is to scale innovation inside a global corporation, develop AI-integrated products, or pivot into a tech-forward leadership role, the MS-AIB program empowers you to thrive at the intersection of technology and business.
- Completion of a bachelor's degree in any field is required.
- A minimum cumulative GPA of 3.0 is required.
- An entry test covering statistics, Python, and SQL must be completed; supplemental coursework is required for sections not passed.
- The equivalent of six credit hours in business are required; otherwise, foundational business coursework must be completed.
- Required application materials include a statement of purpose, resume, letters of recommendation, and, if applicable, proof of English proficiency.
*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.
The curriculum for this program includes:
Students will learn to frame real-world problems as machine learning tasks. Introduces supervised and unsupervised paradigms of machine learning, selecting suitable models, applying appropriate evaluation metrics, and developing models using Python libraries like PyTorch and Scikit-Learn.
Provides a broad introduction to the concepts, techniques, applications, and tools (mainly Python-based ones) of deep learning (DL). The course will cover a variety of DL methods developed to address modeling and learning challenges across many applications such as image classification, text data analysis, online user modeling, and recommender systems.
Explores the ethical, social, and policy implications of artificial intelligence in contemporary society. It delves into the impact of AI on privacy, employment, bias, democracy, and human relationships; into evolving governance and regulatory frameworks for AI; and into managerial decisions regarding investment in and adoption of AI technologies in the organization.
Introduces students to quantitative methods in network science used to model, analyze, and understand various complex systems and the unique interactions among their components.
This graduate-level course explores essential artificial intelligence concepts, methods, and applications across various digital platforms. Students will gain hands-on experience with software tools for implementing AI solutions to real-world challenges. Key topics include understanding data-driven challenges in digital environments, AI-based personalization systems, content analysis and generation using artificial intelligence, and AI-driven decision making frameworks. The course combines theoretical foundations with practical implementation, preparing students to leverage AI technologies effectively in digital platform contexts.
This graduate-level course provides a comprehensive overview of artificial intelligence as it is developed for and implemented in medicine and healthcare settings. Through seven focused modules, students explore different aspects of AI applications in medicine, examining both the underlying technologies and their integration into clinical practice. This course emphasizes how these AI systems impact various healthcare stakeholders, from practitioners to patients. Students will gain insight into the technical foundations, practical applications, and ethical considerations of AI technologies that are transforming modern healthcare delivery and medical research.
This graduate-level course introduces students to fundamental artificial intelligence methods and their integration with business intelligence processes. Students will explore how AI can transform raw data into actionable insights to address common business challenges related to various stakeholders, including customers, competitors, and suppliers. The course covers essential machine learning and deep learning techniques, emphasizing their practical application in real-world business scenarios. Through hands-on projects and case studies, students will develop the skills to implement AI-driven solutions that enable strategic decision-making and business transformation. Participants will gain a comprehensive understanding of how AI adoption can enhance organizational performance across different business domains.
This graduate-level course provides students with a hands-on introduction to artificial intelligence fundamentals and their practical applications in cybersecurity. Students will develop proficiency in AI core concepts, deep learning architectures, transformer models, large language models, and reinforcement learning techniques. Through practical exercises and projects, participants will learn to implement these advanced AI methodologies to develop innovative cybersecurity research solutions that address emerging threats and vulnerabilities. The course bridges theoretical understanding with practical implementation, equipping students with the technical skills needed to leverage AI for solving complex cybersecurity challenges.
Uses state-of-the-art generative AI and deep learning tools to provide hands-on experience. Students will learn how to apply generative AI techniques to sift through large amounts of data and provide actionable business insights.
Uses state-of-the-art data management, data exploration and computing, and big data machine learning software tools (such as SQL Server, MongoDB, PySpark, and TensorFlow) to provide hands-on experience. Students will learn how to apply big data techniques to sift through large amounts of data and provide actionable business insights.
Outcomes
Skills
Earning your Master of Science in Artificial Intelligence for Business will build core skills, including:
- Data exploration & preprocessing
- Graph & network analysis
- Deep learning & neural network design
- Generative AI principles & applications
- Data analysis & visualization
- Machine learning techniques
- AI safety & risk mitigation
- Ethical AI use
- Regulatory compliance in AI-driven business
Potential Career Paths
Graduates of the Master of Science in Artificial Intelligence in Business will be prepared to pursue the following careers:
