Master of Financial Technology and Analytics

Examines governance and oversight of artificial intelligence in enterprise risk contexts. Covers regulatory developments, model risk management, algorithmic transparency, data governance, and fairness and bias concerns. Emphasizes evaluating AI-related risks and designing governance frameworks for responsible adoption.

Students explore how emerging technologies—including AI, predictive analytics, and data visualization—shape international policy, economic development, business strategy, security, and diplomacy.

This course prepares engineering managers to lead innovation by strategically evaluating and integrating emerging technologies (e.g., AI, IoT) into engineering practice. Students explore technology readiness levels, adoption models, and governance frameworks while weighing ethical, regulatory, and risk considerations.

Explore the foundations of neural networks, deep learning networks, and their various problems. This course will have students participate in hands-on labs with real-world datasets to enhance practical skills and use prompt engineering to interact with advanced models.

Gain an introduction to computer vision by exploring a combination of traditional AI, machine learning, image processing, and mathematical theories to provide ways of programming a computer to understand visual imagery. The course will expose students to the techniques required to efficiently analyze images for representation in applicable context scenarios.

Dive deeper into the latest trends, tools, and innovations at the forefront of the rapidly evolving cybersecurity landscape. This course will explore the potential impacts of these technologies on cybersecurity practices, assess their practical applications, and understand how to integrate them into existing security frameworks. Students will gain a comprehensive understanding of how to leverage emerging technologies in cybersecurity.

Dive into advanced strategies and frameworks designed to anticipate, identify, and mitigate cyber threats. This course will cover security architectures such as Zero Trust, proactive threat hunting, continuous monitoring, and the integration of artificial intelligence (AI) and machine learning (ML) in cyber defense. Through a combination of theoretical knowledge and practical exercises, students will learn to design and implement robust defense mechanisms that enhance the resilience of digital infrastructures.

Learn more about the selection and implementation of AI across various industries and teams. This course will equip students with the skills to leverage AI technologies in interdisciplinary contexts to foster collaboration, ethical decision-making, problem-solving, and impactful integration. Students will review real-world, industry-specific case studies and strategic frameworks in various sectors.

Gain deeper understanding of machine learning techniques, including supervised, unsupervised, and reinforcement learning. This course will cover key algorithms—such as linear regression, decision trees, support vector machines, k-means clustering, and neural networks—and emphasize practical applications. It will help students become proficient in selecting and applying appropriate machine learning techniques to solve complex problems.

Explore the key areas of consideration when deploying products that contain AI. This course will cover the social, political, and economic effects that AI may have on society, including an understanding of public concerns with AI such as economic, equity, and human rights. Students will study diverse ethical issues that arise with the widespread and rapid integration of AI technologies, as well as the tools and frameworks for ensuring ethical AI practices to mitigate AI bias.

Develop an understanding of the ethical considerations and issues that IT professionals encounter in the workplace given their exposure to data, various forms of electronic communication, and other types of information. Students will also explore contract law to cover a variety of services, including software, cybersecurity, and privacy.

Dive into a comprehensive overview of the principles and practices involved in the analysis, design, development, and implementation of effective information systems. Students gain proficiency in modern software development practices — including analyzing project requirements, risk analysis, cost estimation and budgeting, and quality control.

Learn to leverage the latest tools and methodologies in the field. In this course, students will track the continuous iterations of AI as one of the most rapidly evolving technologies of our time through large language model (LLM) benchmarking. Students will experiment with various LLMs, learning to craft and refine prompts to optimize model outputs for different application

Learn and perform the best practices for building AI systems in real-world applications. This course will include modules on change management specific to AI implementation. Students will build AI systems knowledge and the skills necessary to develop and implement AI strategies effectively in diverse organizational contexts.

Gain knowledge of the overview and historical progression of AI. This course will cover key topics such as machine learning, neural networks, natural language processing, and the historical milestones that have shaped the development of AI. It will also explore the current landscape and future directions of AI, emphasizing its use in various industries to give students a thorough understanding of AI's foundational theories, practical applications, and evolutionary trajectory.

Examine the role of emerging technologies in setting and implementing public policy, the role of stakeholders and interest groups, and available tools to apply from a technology and innovation framework. This course will analyze how new technologies improve and impact policy efficacy by assessing key principles and defining opportunities regarding the role and application of technology in public administration.

Develop a deeper understanding of ethical decision-making and effective conflict management against a backdrop of major social, economic, and cultural transitions. In this course, learners will gain the knowledge and skills for navigating complex organizational landscapes.

This course will provide an overview of the digital tools, techniques, opportunities and challenges associated with learning technologies and leading teams. The course will also introduce learning technology applications, provide tools to evaluate learning technology solutions and related multimedia learning design models, address digital accessibility, and engage in best instructional technology practices.

This course will review the foundations of artificial intelligence (AI) with applications to the prevention, detection, diagnosis, and prognosis of diseases. Learners will differentiate various artificial intelligence concepts and enabling technologies, and discover and employ processes used in designing and implementing artificial intelligence systems to prevent bias and inequities.

This course will explore leadership, organizational structure, and effective team functioning in healthcare. It will provide students with an understanding of the importance of developing high-quality relationships, the impact of motivation, power, and influence, principles of organizational ethics, and health equity.

This course will explore the theory, applied practice, and impact of current and emerging digital health technologies and clinical documentation systems for all demographics. Learners will differentiate between the technology tools used in healthcare, including wearables, telemedicine, Mobile Health, Internet of Things (IoT), and other Consumer eHealth tools, and examine how digital health solutions and patient portals impact patients’ health and wellness, access to healthcare services, and interactions with their caregivers (patient perspective). They will also evaluate how digital platforms can improve and transform clinical operations and the delivery of healthcare (care provider perspective).

Dive into the theory and application of database management systems. This course will help learners apply principles of database management, data modeling, privacy, and cybersecurity to improve healthcare and manage health data effectively, and identify and analyze database management systems. It will also cover query languages and the design and maintenance of cloud databases.

A comprehensive introduction to the current state of the science and practice of analytics in healthcare, including how to “tell the story” the numbers present. Core competency skills are achieved using a variety of learning methods to help students apply analytic techniques supporting data mining, visualization and data driven decision making.

Explore the impact of machine learning, deep learning, and artificial intelligence tools in finance. While learners will not engage in coding or the building of machine learning and AI tools, this course will cover the practical application of these tools to solve problems in asset management, corporate finance, and financial institution settings.