Design Methods
3 credits | core course
The goal of this course is to give you hands on experience using design tools and methods to understand user needs, frame business opportunities, and design solutions. The course will examine design from both organizational and technical perspectives. You will be given an actual design challenge and, over the course of the semester, guided through an iterative design process, which takes you from opportunity identification through solution definition. You will learn to conduct research with end users, synthesize data, prototype solution ideas, and communicate compelling stories. Design challenges will be focused on emerging information technologies, including IoT, cognitive computing, AI, cloud, mobile, and 3D/4D printing.
Advanced Programming and App Development
3 credits | core course
This course offers a practical, hands-on exploration of full-stack web development using a traditional approach. Students will engage with advanced programming and foundational software engineering concepts essential for developing robust, scalable web applications. The curriculum covers industry-standard toolchains and frameworks that are widely adopted by professional full-stack developers. Emphasis is placed on mastering the most popular full-stack frameworks, while reinforcing critical topics such as source code management, agile methodologies, the software development life cycle (SDLC), and common design patterns. By the end of the course, students will possess the technical proficiency and software engineering discipline required to contribute effectively in real-world development environments.
15 Credit Hours
Advanced Programming and App Development II
2 credits | core course
This course covers a range of topics required for developing modern applications that are distributed in nature and natively developed for cloud deployment. In this course, students will learn architectural building blocks, such as microservices, containers, serverless computing, storage types, portability, and functions. This course will explore the fundamentals of cloud native applications, including how to design, develop, and operate them. This course also covers some unifying distributed programming fundamentals that are pervasive across different technologies such as MapReduce, streaming-data analytics and structured data API. The course explores machine learning design patterns for big data and distributed programming concepts.
Data Management
2 credits | core course
This course provides a comprehensive exploration of modern data management practices, with a strong focus on the design and implementation of scalable, cloud-native data systems. Through a combination of theory, hands-on labs, and real-world case studies, students will gain the knowledge and skills needed to manage diverse data types—structured, semi-structured, and unstructured—across their full lifecycle. Students will learn foundational principles including relational schema design, SQL optimization, and NoSQL data models, while also diving deep into advanced topics such as data lakes, serverless architectures, and real-time data ingestion. The course emphasizes the role of cloud infrastructure in enabling agile, secure, and scalable data solutions.
Mathematical Foundations of AI
2 credits | core course
This class covers the mathematical foundations of AI, and the material will be applied in several classes in the program including Business Data Science, Unstructured Data Analytics, and Generative AI, parts 1 and 2. The class will cover the foundations of probability theory and linear algebra.
Fundamentals of Neural Networks and Machine Learning
3 credits | core course
An introduction to fundamental concepts, methodology, algorithms, and technology used in business analytics and decision making. We explore the most powerful concepts and algorithms from probabilistic modeling, analysis and machine learning. We move quickly over some of the basic ideas of modern regression and classification, in order to devote appropriate time to the algorithmic ideas behind ensembling, including Bagging and Boosting. We then move into neural networks, building and experimenting with sophisticated neural networks. The course culminates in developing examples from computer vision using transfer learning and leveraging.
Business Problem Solving with Unstructured Data and GenAI
3 credits | core course
The rise of Generative AI (GenAI) and AI-driven business transformation is fueled by the explosion of unstructured data — text, images, audio, and video. While GenAI represents a breakthrough in its ability to fundamentally transform business and society, its impact is amplified when combined with traditional unstructured data analytics. Together, these tools present unprecedented opportunities for organizations to address strategic, operational, and tactical challenges — from real-time customer engagement to improving brand equity, customer loyalty, competitiveness, and profitability.
This course explores how modern enterprises can harness the combined power of GenAI and traditional unstructured data analytics to unlock business value. It covers a broad range of business questions and analytics driven use cases, spanning decision-making, process optimization, and innovation. Students will gain hands-on experience through large-scale assignments and a final project, developing practical skills grounded in real-world business applications.
Financial Management
3 credits | core course
This course is an introduction to financial management fundamentals. It examines the roles of financial management in creating value and to present the analytic framework used in the study of finance.
15 Credit Hours
(8 core credit hours, 7 elective hours)
IT Capstone
3 credits | core course
This is a practicum course in which students apply their learning in the MSITM program to develop real-life business and social solutions using emerging information technologies. Industry partners will provide business context for IT capstone projects.
Strategic IT and Digital Transformation
2 credits | core course
The course focuses on information technology-enabled digital transformation and explores how digitalization impacts firm competition and enables industry transformation. The course provides a broad overview of the role of digital business models, how firms can compete in disruptive ecosystems, and allows students to think critically about the benefits and challenges of strategic AI implementations and change management in real-world applications. This is an interdisciplinary course that integrates concepts and principles from different disciplines, using cases, simulations, and in-class exercises.
Risk Governance in AI and Intelligent Automation
3 credits | core course
This course prepares students to govern and control the complex cybersecurity, privacy, and IT risks organizations face in an era increasingly shaped by artificial intelligence (AI) and intelligent automation technologies.
Students begin by building a solid foundation in governance and control of digital risks through established frameworks such as NACD, COSO, COBIT, NIST CSF, AICPA GAPP, and SOC audits. Building on this base, students explore how emerging technologies—including robotic process automation (RPA), process mining, AI, machine learning (ML), generative AI (GenAI), Internet of Things (IoT), blockchain, and brain computer interfaces (BCI)—introduce new and evolving risk landscapes. Students also analyze leading-edge frameworks like MITRE’s ATLAS and explore innovative risk mitigation techniques at the intersection of AI, cybersecurity, privacy, and ethics. Special attention is given to privacy-enhancing technologies and ethical decision-making frameworks that help address the novel challenges posed by intelligent systems, such as autonomous vehicles and smart cities.
The course is delivered through an active, participant-centered learning approach—including brief lectures, guest lectures, interactive case discussions, simulations, and in-class group exercises. This course equips students to develop enterprise-wide strategies for secure, ethical, and compliant digital transformation; and lead responsibly and effectively in a digitally driven world. The course is well-suited for students pursuing careers in IT management; cybersecurity and privacy consulting; data governance, data engineering and privacy engineering; AI security, AI Ethics, and AI-driven digital risk management; IT audit, IT security, and enterprise IT risk management; and digital strategy.
Generative AI I
2 credits | elective course
The last decade has brought remarkable advances in Machine Learning algorithms, platforms and computing architectures. The frontiers of what data science and AI can accomplish are currently progressing much faster than what was previously considered possible. Even for those not on the front lines of research, a basic working knowledge of the latest algorithms, business applications and limitations can provide a fundamental advantage.
This course will explore precisely this, through hands on, project-based coverage of Deep Learning, and in particular, Generative AI. Covered material includes attention mechanisms and the transformer, image and language embeddings, large language models, multi-modal models, and the important latest ideas in reasoning. We will also cover some of the key emerging issues in security and reliability.
Students are expected to have a basic knowledge of machine learning and data science, as well as a basic knowledge of Python and Pytorch, at the level of the class “Fundamentals of Neural Networks and Machine Learning”.
Generative AI II
2 credits | elective course
Over the last few years, Generative AI (GenAI) models are increasingly used in business settings. This class will cover diffusion models for image generation, language generation, video and other applications. These models are the state-of-art for image and video, and are being introduced for language generation due to their increased inference speed. We will discuss their architectures and mathematical foundations. Beyond generative models, we will study machine learning models for decision-making (bandits and reinforcement learning). Applications of these decision-making ideas are important in a variety of settings, including recommendation systems, online advertising, A/B testing and autonomous driving. The class will be structured around a sequence of programming assignments that will provide intuition on the application of these ideas.
Product Management
2 credits | elective course
The product management course provides an introduction on what it means to build, lead, and scale a product. Product managers are often called the “CEO of the product” where they own the end-to-end product development cycle and play at the intersection of technology, business, and management. This course introduces students to the broad range of skills required to successfully lead a product, such as working backwards from the customer, designing the product, defining requirements, technical tradeoffs, and go-to-market.
Project Management
2 credits | elective course
Project management (PM) delivers outcome(s) of value within a predetermined set of resources – people, time, budget and infrastructure using defined processes, skills, knowledge and tools that help track and achieve successful delivery. In this course, the participants will have an opportunity to understand an overview of these processes, skills and tools, their inter relationships, and how and where they might acquire the related knowledge. Through a mix of course materials, practical exercises and simulation, this course will provide an experiential learning of Info Tech project management typical in US / global businesses.
Participants will become familiar with concepts such as planning, working and closing a project; methods such as Agile; interactions within and across stakeholder domains; setting and managing scope, schedule, and communications; tracking and reporting on progress to goal; dependencies, scope creep, budget and risk; governance and escalations; resource (people, code, infrastructure) organization and management; change control, knowledge transfer and transition to operations.
On completing this course participants could expect to perform as a confident team member of any IT project and contribute to its successful execution with a firm grasp of the vocabulary and techniques.
AI in the Enterprise
2 credits | elective course
This course teaches students how to design and deploy artificial-intelligence (AI) capabilities across the enterprise. It covers mapping AI solutions to specific business requirements and aligning them with strategic goals. Instruction develops a solid foundation in data environments and information architecture, enabling the identification, assessment, and preparation of high-quality data essential for AI success. Through hands-on exercises, case studies, and simulations, the curriculum demonstrates how to select and scale suitable models and integrate them with existing systems and processes. It also addresses designing effective human-AI interactions and verifying that the AI solution delivers measurable business value. Finally, the course establishes governance and continuous-improvement practices that keep AI initiatives reliable and effective over the long term.
Technical Foundations of AI-Driven Cybersecurity
1 credit | elective course
This course offers a hands-on introduction to the technical foundations of cybersecurity, with a particular focus on emerging threats and challenges at the intersection of cybersecurity and artificial intelligence (AI). Students explore how AI functions both as an enabler of cyberattacks and as a vulnerable target, requiring robust security defenses.
Guided by insights from cybersecurity executives, consultants, and ethical hackers, the course addresses cutting-edge topics identified as critical by industry practitioners. To stay aligned with the rapidly evolving threat landscape, content is refreshed each semester. Recent topics have included:
- Cryptography and public key infrastructure (PKI)
- Quantum-resistant encryption
- Offensive cybersecurity techniques
- AI security and ethical considerations
- AI applications in identity and access management
- Zero-trust and blockchain architectures enhanced by AI
- Adversarial attacks on AI and generative AI (GenAI) systems
- Trade-offs between accuracy, privacy, security, and fairness in AI system design
Students engage in interactive guest lectures, hands-on technical exercises, and industry-led discussions to develop foundational skills and practical knowledge. The course is well-suited for students pursuing careers in cybersecurity and privacy consulting; data governance, data engineering and privacy engineering; AI security, AI Ethics, and AI-driven digital risk management.
Human Dimensions of Cybersecurity
1 credit | elective course
GenAI introduces novel risks—not just technical, but social and behavioral. This course examines emerging security and privacy vulnerabilities in AI systems, with a focus on human manipulation vectors: prompt injection, phishing via AI-generated content, identity spoofing, and malicious feedback loops. Students will study how adversaries exploit both model behavior and user trust, and how to counteract these risks through red teaming, user training, behavior-aware safeguards, and ethical AI design. This course empowers students to think critically about human-AI interaction security and apply layered defenses that reinforce public trust in AI systems.
Managing Data Product for AI
1 credit | elective course
This course explores the emerging discipline of managing data products designed to power GenAI applications. Students will learn how to curate, package, and deliver reusable datasets and APIs optimized for retrieval-augmented generation, synthetic data generation, and LLM prompt engineering. Emphasis is placed on ensuring fitness for use, economic value, and continuous improvement of data assets. Students will practice modeling user needs, versioning data-as-a-service, and preparing assets with the right metadata and context to support downstream governance and explainability. The course positions data productization as the first step in building trustworthy AI systems.
Data Governance & Responsible AI
1 credit | elective course
Building on data productization, this course focuses on applying data governance frameworks to ensure the integrity, transparency, and fairness of GenAI systems. Students will learn how to embed observability into AI workflows—tracking provenance, bias, quality metrics, and access rights across the AI lifecycle. Topics include model auditability, privacy controls, risk registers, data and model cards, and governance automation tools. This course bridges the socio-technical principles of data governance with the operational realities of GenAI, preparing students to implement trust-by-design practices within enterprise AI programs.
Financial Technology
2 credits | elective course
This course is focused on three main FinTech areas in which technology is driving changes in the way financial services are provided: (i) Lending/Banking services, (ii) wealth management (iii) Trading. The course is going to provide specific coverage and examples of developments from (1) payments (2) peer-to-peer lending (3) robo-advising (4) algo/quantitative trading. In each of these areas, we start by analyzing the marketing place, the incumbents, and the business case and strategies of the incoming technology players. We then turn to understand the role data and analytics plays in driving the technology-based services. Guest speakers augment the discussion by offering their perspective on future trends in each of these areas.
Web 2 to Web 3: Blockchain Solution Development Using Smart Contracts
1 credit | elective course
The mission of this graduate course is to introduce students to the cutting edge of Web3 blockchain technology and the token economy, to teach students the professional tools and skills of smart contract development, and to guide them to build feasible decentralized applications. Every student in this course will have an opportunity to go through an end-to-end process in building a blockchain application that is immutable, transparent, and secure.