Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• QST IS 841: Advanced Analytics For Managers: Data Mining
  Graduate Prerequisites: IS823 recommended - The widespread proliferation of IT-influenced economic activity leaves behind a rich trail of micro-level data about consumer, supplier and competitor preferences. This has led to the emergence of a new form of competition based on the extensive use of analytics, experimentation, and fact-based decision making. In virtually every industry the competitive strategies organizations are employing today rely extensively on data analysis to predict the consequences of alternative courses of action, and to guide executive decision making. This course provides a hands-on introduction to the concepts, methods and processes of business analytics. We will learn how to obtain and draw business inferences from data by asking the right questions and using the appropriate tools. Topics to be covered include data preparation, data visualization, data mining, text mining, recommender systems as well as the overall process of using analytics to solve business problems, its organizational implications and pitfalls. Students will work with real world business data and analytics software. Where possible cases will used to motivate the topic being covered. Prior courses in analytics, data management and statistics (such as IS823) will be helpful but are not required.
• QST IS 843: Big Data Analytics for Business
  Graduate Prerequisites: Python basics (e.g. IS717, IS834, QM877 (Python Bootcamp) or equivalen t); Some prior experience with analytics (e.g. IS823, IS833, IS834, IS 841, MK842, MK872, MK876); or permission of the instructor. - This programming-based analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python, and basic analytics are prerequisite.
• QST IS 853: Business Insights through Text
  Graduate Prerequisites: (QSTMK842 OR QSTIS833 OR QSTIS834 OR QSTQM877) QST MK842 or IS833 or IS834 or QM877 - Eighty to ninety percent of current exponential data growth is attributed to unstructured data such as text. Increasingly, the data has become more like crude oil that has to be refined and structured to extract value for business insights and strategies. Managers need to understand the opportunities and challenges associated with unstructured data for competitive advantage. In this class, students will learn what businesses can do with text data through a variety of case-based examples based on research and industry applications from Marketing, Information Systems, Finance, Strategy, and Social Impact perspectives. This is a course dedicated to understanding the potentials of text data in different settings curated based on Natural Language Processing (NLP) techniques involved. The focus of the course is on the substantive value of text and methods will be introduced as backdrops. Throughout the course, we will use Python, a powerful language and the main tool used by deep learning data scientists. However, skeleton codes will be provided to reduce technical burdens. 1.5 cr.
• QST IS 854: Digital Strategy for Emerging Business Leaders
  Graduate Prerequisites: SI851 preferred - A digital strategy is an organization's plan to leverage digital technologies to create new business models, products, and services, as well as to improve internal processes and interactions with customers, suppliers, and partners. For business leaders, a digital strategy is crucial to staying competitive and grow their businesses. In this course, students will learn how to design, execute and communicate digital strategies in a complex organization. We will also learn key management concepts such as project delivery, change management, as well as financial modelling of digital investments. In addition to case studies and class exercises, we will bring in industry experts to share their real-world experience. In lieu of a final exam, students will form into project teams to design, develop, present and gain buy-in for a specific digital strategy.
• QST IS 855: Digital Transformation: Immersive Interactions and Insights at Silicon Valley
  Graduate Prerequisites: FT MBA: second-year standing. PEMBA: 36 credits of coursework. - This course will be a one week intensive held in Silicon Valley. The course is designed to achieve two objectives. First, to develop an appreciation for the role of Silicon Valley in Digital Innovation and, secondly, to examine how digital innovations are impacting key shifts in specific sectors. Students will be placed into teams and be expected to develop specific insights as the basis to engage in interactions with corporate executives, alumni and follow classmates. Teams will also visit leading companies involved in each sector and develop and present their team's perspective on key digital trends and leadership challenges for their sector.
• QST IS 858: Agile Project Management
  This course is designed to provide students with an overview of agile development methodologies. The course introduces the various methods currently used in the industry and then focuses on the primary methodologies used today, SCRUM and Kanban. Students will learn the tools of these agile development approaches and will be introduced to RALLY Project Management software, the leader in the industry for SCRUM. Students will learn to analyze requirements, create backlogs, schedule "stories" to be developed and delivered, hold standup meetings, and Retrospectives.
• QST IS 860: Analytics Consulting: Data-Driven Business Solutions
  Graduate Prerequisites: Some prior experience with analytics (e.g. IS823, IS833, IS834, IS841, MK842, MK872, MK876), or permission of the instructor. Experience wi th Python (e.g. IS717, IS834, QM 875 (Python Bootcamp)), or - This course will introduce concepts, methods, and processes of data mining and machine learning within projects that have been sponsored by partner companies. Through practice in this live setting, we will develop our analytical problem solving skills, and understand how to organize and manage agile analytical projects in the most realistic possible situation. We will learn how to collect, wrangle, and analyze both primary and secondary data sources in multiple business contexts and apply this knowledge to the client data.
• QST IS 863: Integration of Generative AI in Business Practice
  This course provides students with a practical understanding of generative AI and how to strategically implement it across organizations. Through lectures, case studies, and hands-on exercises, participants will learn the fundamentals of generative AI and how it stands to transform industries. Given the wide applicability of these technologies, we will consider how to prioritize GenAI applications and develop roadmaps for integrating AI into various business functions. Students will explore best practices for managing AI projects and addressing legal, IP, and ethical considerations. The course will include insights from AI practitioners driving change in major companies through Gen AI. Despite its promise, realizing value through these technologies can be challenging. We will study the barriers to AI integration along technical, organizational, and operational lines. The class does not involve programming and is appropriate for the general MBA audience.
• QST IS 879: Business Modeling with Spreadsheets
  This course aims to sharpen students' ability to conduct quantitative analyses of business problems. The primary focus is on problem formulation and analysis -- identifying the key components of a decision problem, structuring it, translating it into a graphical chart, and then building the appropriate mathematical and spreadsheet models. These models are used to generate valuable qualitative and quantitative managerial insights. Students will be introduced to data management and decision tools such as Formula Diagrams, Linear Optimization, and Error Detection methodologies, as well as to Parametric Sensitivity Analyses. While each business problem is distinctive, a disciplined approach to problem solving can be incredibly useful across many career contexts. The concepts and exercises in this course will sharpen the student's professional ability to structure a messy problem and do some disciplined analysis on it. Developing these modeling skills requires the opportunity to brainstorm, reflect, and practice it on a wide variety of problems. Hence, the course includes intensive team-centered workshop sessions where all students get hands-on practice working with a group of peers to frame various problems in appropriate analytical terms, develop a solution approach, and critically reflect on the results. Examples will be drawn from Strategy, Operations, Technology Management, Marketing, and Finance to expose students to the broad applications of the concepts and tools learned in this class. Many of the up- to-the-minute Excel techniques covered in the course are now considered standard in industry, and developing a good understanding of them will deepen the student's ability to identify opportunities in which spreadsheet analytics can be used to improve performance, drive value, and support important decisions. Finally, students will learn the latest technologies for effectively linking spreadsheets to relational databases, and to manage reliably large scale spreadsheet development projects.
• QST IS 883: Deploying Generative AI in the Enterprise
  Graduate Prerequisites: MSDT Students Only - Most organizations today -- of all sizes and stages of maturity -- are undertaking internally and externally focused digital initiatives. The success of these programs varies widely and depends on numerous strategic, tactical and technical factors; that is, active management of not only the technology but also the organizational and product development lifecycle. Accordingly, this course will delve into the mechanics of Large Language Models, including their structure and functionality. Through practical exercises students will learn to deploy these models effectively in various business contexts, from enhancing decision-making processes to optimizing operational efficiencies. We will cover integration of Language Models with cloud-based platforms such as Azure and OpenAI's APIs. A focused exploration of query optimization and prompt engineering will equip students with the skills to fine-tune AI outputs for strategic use. Ethical and social implications of the technology will also be considered. Students will apply concepts -- including agile methodologies, design thinking, user experience, and financial modeling -- to architect and execute an AI-driven business project.
• QST IS 889: Data Management
  Graduate Prerequisites: Non-MSDT students only in Fall; MSDT students only in Summer - The ability to collect, organize, access, analyze and harness data is a source of competitive advantage for some and a competitive necessity for others. Getting an organization to the point where it has a data asset it can leverage is a non-trivial task. Many firms have been shocked at the amount of work and complexity that is required to pull together an infrastructure that integrates its diverse data sources and empowers its managers. This course will provide an introduction to the concepts and technologies that are involved in managing and supporting the data assets of your organization. We will cover data modeling, relational databases, including SQL, data warehousing and business intelligence.
• QST IS 890: Creating Successful Digital Products & Experiences
  Graduate Prerequisites: MSDT Students Only - Organizations of all sizes and stages of maturity are undertaking internally and externally focused digital initiatives. The success of these programs varies widely and depends on numerous strategic and tactical factors. In this class students will learn leading models, practices and tools used by top digital teams, and apply them, along with other skills learned throughout the MSDT program, toward the research, ideation, design and creation of a prototype digital product/experience designed to address unmet needs in the market and achieve real-world critical business objectives.
• QST IS 898: Directed Study: Info Systems
  Graduate Prerequisites: consent of instructor and the department chair - Graduate-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST IS 899: Directed Study: Info Systems
  Graduate Prerequisites: consent of instructor and the department chair - Graduate-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST IS 911: Generative AI & Causal Inference with Text
  This seminar will introduce the latest empirical methods in generative AI and causal inference using text, empowering doctoral students to explore and investigate novel and high-impact business and computational social science research. The first half of the seminar will concentrate on the techniques, potential applications, and economics of generative AI and large language models. Topics covered will include Transformer, BERT, the GPT family, VAEs, GANs, Diffusion Model, Human-AI collaboration, etc. The second half will focus on causal inference techniques using text as controls, mediators, and treatment. Students will be required to propose a new idea based on the seminar's content. Previous iterations of the seminar have included Interpretable ML and Bias in ML (2017), Generative AI (2019), and Neural Language Models and Economics of AI (2020). The seminar is engineered to foster innovative ideas for students across a diverse range of academic disciplines.
• QST IS 912: Platform Strategy & Design
  This class will cover seminal works in the economics of information including the Nobel Prize winning ideas of Akerlof, Arrow, Spence, Stiglitz, and von Hayek. It will proceed through (i) concepts of information, its value and measurement (ii) search and choice under uncertainty (iii) signaling, screening, and how rational actors use information for private advantage (iii) how to price and package information goods (iv) how properties of information cause market failure (v) macroeconomic effects of information (vi) social and legal issues of owning information. Although primarily a theory class, it should be of interest to any student applying information economics in academic, commercial, or government policy contexts. Prerequisites are a graduate course in microeconomics and mathematics at the level of introductory calculus and statistics. Students will produce a major paper suitable for publication or inclusion in a thesis.
• QST IS 919: Research Seminar 2
  This course covers those important Information Systems (IS) theories and topics that are at the organizational level of analysis and below. That is, it focuses on the behaviors of single individuals and small numbers of individuals, such as dyads and teams. This is consistent with an approach to organizational phenomena that distinguishes between micro and macro levels of research, this course being the micro. The focus is on ways that individuals and teams use information technologies to acquire, process, and transfer information, and the effects these technologies have on individual cognition and dyadic and group interactions. It also investigates the design and implementation of information technologies and the impact of these on organizational outcomes. The course is designed to engender students with a broad knowledge of research at the intersection of information technologies and organizations, with an emphasis on theoretical underpinnings and methodological choices.
• QST IS 990: Current Topics Seminar
  For PhD students in the Information Systems department. Registered by permission only.
• QST IS 998: Directed Study: Info Systems
  Graduate Prerequisites: consent of instructor and the department chair - PhD-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST IS 999: Directed Study: Info Systems
  Graduate Prerequisites: consent of instructor and the department chair - PhD-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.