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 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.

2 credits | core course

The course is split into five main parts: (1) Network Patterns, which describes and seeks to explain several common patterns found in real-world social networks, (2) Network Communities, which explores the structure of network communities and how to find them, (3) Importance and Influence, which discusses an individual’s place in the network, and how memes, early adoption, and such “cascades” propagate, (4) Advertising and Marketing, which focuses on viral marketing and social advertising techniques, and (5) Advanced Analytics, which describes the latest methods for inferring user interests and recommending items to them, and related topics.

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.

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.

2 credits | elective course

This course introduces students to the role of digital technologies in managing and innovating business processes. Students will learn about the IT capabilities needed by firms to co-ordinate their activities, collaborate with business partners and make decisions. The course will illustrate the role of technologies like IoT, Block Chain and AI and how these technologies interface with enterprise technologies such as ERP and CRM to improve business performance. Students will also learn about collaboration technologies such as XML, web services, and microservices.

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.

2 credits | elective course

Over the last few years, Generative AI (GenAI) models are increasingly used in business settings. This class will cover some of the main machine learning (ML) techniques that underlie generative modeling, including transformer-based models for languages and sequence generation (LLMs), and diffusion models for image generation. We will discuss architectures and their 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.