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.

3 credits | core course

This course provides a hands-on exploration of various approaches to modern app development, including required advanced programming and software engineering concepts. The course explores approaches to app development ranging from native platform programming through programming frameworks that allow cross-platform development, to high-level approaches based on web frameworks. The course requires programming assignments for each of the approaches; a basic familiarity with high-level programming is assumed.

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.

2 credits | core course

This course provides students the necessary skills and an understanding to be able to design and develop mission-critical database applications. At the end of the course, students should thoroughly understand database concepts (e.g., integrity constraints, Normalization), E-R modeling, relational database design, and advanced SQL. PL/SQL (Oracle's Procedural language extension of SQL) will be used to develop complex business applications.  The course will cover various client and server side issues (such as optimizing communication needs, data validation, pitfalls, and security) in building web-based solutions. Students will also be knowledgeable in data warehouse design and in how to use advanced analytics functions within SQL.

2 credits | core course

This course covers a range of topics required for developing modern applications that operate over vast data sets that are potentially distributed in nature. The course covers a range of alternative technologies and architectures for working with big data, examining the pros and cons of the different approaches. The course also covers some unifying distributed programming fundamentals that are pervasive across the different technologies. Students will also get hands on experience working with modern concepts and tools (e.g., MapReduce and Hadoop) through a series of small programming assignments.

2 credits | core course

This is a project-based course exploring all aspects of the IoT product life-cycle. These aspects include, (i) interfacing with the devices (sensors/actuators) that collect data and affect the environment, (ii) exploring network protocols for communication with these constrained devices, (iii) programming the back-end services that host, manipulate and disseminate the collected data and (iv) developing apps that facilitate human interaction with these devices and analysis of the data they produce. Security, privacy and performance considerations specific to IoT will also be explored.

3 credits | core course

This course builds skills in generating business and social insights from user generated content (e.g., text, images, video, etc.) by the use of text analytics, sentiment analysis, visualization techniques, etc.

3 credits | core course

An introduction to basic concepts, methodology, algorithms, and technology used in business analytics and decision making. Explore concepts from probabilistic modeling, analysis and experimental design. Examine the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning.

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.

2 credits | core course

This course develops skills in strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations.

3 credits | core course

This course builds skills for the prevention and mitigation of data security and privacy risks in newly designed digital artifacts by covering IT governance, risk, and control frameworks and relevant laws, regulations, and industry standards.

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.

3 credit | elective course

2 credits | elective course

The last decade has brought remarkable advances in Machine Learning algorithms, platforms and computing architectures. The frontiers what data science and AI can accomplish is 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 Artificial Neural Networks. The course will use Python and Tensorflow, among other tools. Covered material includes convolutional neural networks, recurrent neural networks and generative adversarial networks. Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets.

Students are expected to have a basic knowledge of machine learning and data science, as well as a basic knowledge of Python.

1 credit | elective course

This course is a survey of some of the emerging machine learning: (i) Models are of increasingly larger size (e.g., foundation models such as BERT and GPT), but are trained mostly in an unsupervised manner (contrastive learning + supervised fine-tuning); (ii) Training of models are often distributed due to data privacy (e.g., in healthcare or finance); and (iii) Beyond inference (classification/regression), models are being used for decision-making (bandits and reinforcement learning). Applications of these ideas are important in a variety of settings, including recommendation systems, large language models, personalized models, online advertising, A/B testing and autonomous driving.

 This class will discuss some of the learning approaches that underlie these trends. We will discuss contrastive learning and model fine-tuning for (mostly) unsupervised model training (used in computer vision, large language models), and federated learning for distributed model training (used in healthcare, finance). We will then cover bandits and reinforcement learning for decision-making (used in healthcare, online advertising and recommendations, autonomous driving). We will overview the basic probability concepts, and introduce algorithmic approaches to offline and online decision-making (overview of bandits and reinforcement learning). The class will be structured around a sequence of programming assignments that will provide intuition on the application of these ideas.

2 credits | elective course

1 credit | elective course

This course is a hands-on introduction to technical foundations of cybersecurity. It starts with secure software development principles and methods to enable students to design security into IT applications. Next, the course discusses security vulnerabilities in database management systems and how to protect against them. Third, the course covers hacking techniques commonly used to identify and exploit vulnerabilities in computer networks, operating systems, and applications. Fourth, the course makes an introduction to security of internet of things (IoT). Finally, the course explores the emerging adversarial attacks on machine learning systems, and discusses how to protect ML systems from such attacks. The course balances theory and application. By hosting ethical hackers as guest speakers, the course creates opportunities for students to learn and apply ethical hacking techniques.

1 credit | elective course

Despite millions of dollars invested in cybersecurity every year, the number of information security breaches continues to climb. Many managers and executives derive a false sense of security from the perception that “cybersecurity is an IT issue.” In practice, technological safeguards are necessary but insufficient to protect organizational and customer data. A comprehensive information security and cyber risk management program must also address the many threats posed by human actors – not just hackers – both inside and outside the organization. This course explores both the risks and the opportunities posed by human interaction with organizational and customer data. Drawing on behavioral science, students will consider interventions for engaging employees in building a cyber-safety culture, aka a “human firewall.”

1 credit | elective course

As societies increasingly rely on AI, organizations need robust data governance to ensure the integrity of intelligent models and the data used to develop them. Governance is not just a risk mitigation or defensive posture, but rather safety guardrails that empower organizations to innovate and build intelligent solutions faster with confidence. The data governance discipline includes socio-technical solutions that integrate policy, people, and process to ensure quality, confidentiality, and compliance of data assets throughout its lifecycle. It overlaps with responsible AI in that both require optimized architecture, agile and synergistic operations, accountability structures, and ongoing monitoring. This course prepares future data and IT professionals with conceptual understanding of business goals, risks, regulatory, and ethical considerations with data and AI assets. Students will also develop practical experience in governance, risk, and compliance related projects, such as data stewardship, model validation, secure data SDLC, privacy impact assessment, etc.

1 credit | elective course

Increased consumerization of AI, mobile, and cloud has driven organizations to streamline the management and delivery of data as a product. However, most data engineering and data science teams lack the skills and experience to manage data as a product. At best, they apply governance to ensure the integrity of the data contents that traverse through the operational and analytical pipelines. To truly harness the value of data assets, business leaders need to manage the data lifecycle from the economic lens and user experience of the data consumers and producers. Whether only used by internal users or sold to external customers, a data product should be tied to value creation and usability. This course introduces students to the tools, processes, and framework for data product lifecycle management process. Students will learn concepts related to information economic valuation, user experience design for information assets, agile principles, and data marketplace capabilities. They will be exposed to practical hands-on project such as: building an interoperable data exchange, configuring data licensing and pricing, secure delivery through API, digital assets rights management, data contract, etc.

1 credit | 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.

1 credit | 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.