Information Systems

• QST IS 843: Big Data Analytics for Business
  Prerequisites: Python basics (e.g., IS717, IS834, QM877 (Python Bootcamp) or equivalent); Some prior experience with analytics (e.g., QSTIS 823, QSTIS 833, QSTIS 834, QSTIS 841, QSTMK 842, QSTMK 872, QSTMK 876); or permission of the instructor. - Every company is a "data company," possessing vast quantities of data from operations, customers, products, and transactions. With big data comes significant challenges requiring specific infrastructure and skills. The analytics process, including deploying and using big data tools, is essential for organizations to improve efficiency, drive new revenue streams, and gain a competitive edge. This course addresses these challenges, discusses methods to overcome them, and common pitfalls in implementation and unnecessary analysis. Data analytics involves exploring, discovering, interpreting, and communicating meaningful patterns, whereas big data analytics focuses on analyzing data on a larger scale, where a single computer cannot process it timely. Distributed computation, the foundation of big data analytics, involves a network of computers processing data segments. This course teaches students to perform statistical data analysis of large datasets using distributed computation and introduces machine learning techniques and libraries that handle big data. Basic programming in python, and basic analytics are prerequisite.
• QST IS 854: Digital Strategy for Business Leaders
  A digital strategy is an organization's roadmap for leveraging digital technologies - including artificial intelligence, cloud computing, data analytics, and emerging platforms - to innovate business models, enhance products and services, streamline operations, and foster meaningful interactions with customers, suppliers, and strategic partners. For business leaders, developing and executing a robust digital strategy is essential not only for competitiveness but also for resilience in a rapidly evolving market environment. This course equips students with practical tools and frameworks to effectively design, implement, and communicate digital strategies within complex organizations. Students will explore critical management practices such as agile project management, effective change management in digital transformations, strategic use of AI and data analytics, cybersecurity considerations, and financial modeling to evaluate digital investments. Through interactive case studies, simulations, and exercises, students will apply these concepts in practical contexts. Industry experts will provide insights into current challenges and opportunities, sharing real-world experiences and best practices. Instead of a traditional final exam, student teams will work collaboratively on projects to conceptualize, develop, present, and secure stakeholder support for their own comprehensive digital strategy initiatives.
• 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 865: 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: MSDT Student Only - 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 895: Action Learning Directed Study in Information Systems
  ALDS: INFO SYS
• 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: Current Topics in AI for Business and Social Science
  Pre-requisite: Questrom PhD students only. Nearly four years after LLMs entered mainstream use, they have evolved from simple chatbots into tool-using agents deployed in the wild, interacting with consumers, organizations, and other agents. At the same time, recent CS research continues to uncover distinctive peculiarities, limitations, and failure modes of LLMs and agentic systems. This seminar begins by covering cutting-edge CS research to understand these systems in depth, including their theory, algorithms, and empirical behavior. We then turn to emerging research in business, economics, and the social sciences that describes, documents, and theorizes the frictions and opportunities arising from real-world deployment: a deep dive into LLMs in the wild. The goal is to equip students with the technical and conceptual foundations needed to identify new thesis-worthy research streams in business and the social sciences. We will cover three modules: mechanistic interpretability, which dissects the inner workings of LLMs and explores how they may be steered and controlled; peculiarities of frontier foundational models, which examine emerging capabilities and failure modes; and agent-to-agent interaction, where agents may be humans, LLMs, or both. The seminar will also have a special section on the role of AI in science and will cover how to use the state-of-the-art AI tools for research and personal productivity. Previous iterations of this seminar have consistently identified and explored topics well ahead of their mainstream adoption by the public and their emergence within the mainstream business and social science research communities, earning a reputation for staying ahead of the curve: Interpretable Machine Learning and Bias in ML (2017), Generative AI in Business (2019), Neural Language Models and the Economics of AI (2020), Generative AI & Causal Inference with Text (2023), Agentic LLMs & Multi-Agent Systems For Business Research (2025). Collectively, these seminars have contributed to the publication or ongoing working papers of over 35 journal articles in top-tier business journals, such as PNAS, Science Advances, Management Science, JMR, ISR, MISQ, SMJ, Marketing Science, and alongside numerous contributions to leading conferences like CHI, AAAI, ACL, ICML, EMNLP, Neurips, and KDD."
• 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.
• QST MK 842: Machine Learning for Business Analytics
  Graduate Prerequisites: (QSTMK723 OR QSTMK724) - This course introduces students to the foundational machine learning techniques that are transforming the way we do business. Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. Students will gain a theoretical understanding of why the algorithms work, when they fail, and how they create value. They will also gain hands-on experience training machine learning models in Python and deriving insights and making predictions from real-world data. Prior programming experience (or IS833/IS834) is strongly recommended. Note: The course was previously offered under the title "Digital Marketing Analytics," but does not overlap with MK876; students may take both courses for credit.
• QST QM 877: Intro to Python Bootcamp
  In this Bootcamp, students will learn the most essential aspects of Python programming. The topics are tailored toward data analysis; no prior programming experience is required. We will cover variables, data types and data structures, DataFrames, conditionals, loops, and functions. We will also cover reading and writing raw files and the core APIs in analysis and visualization. With the basics under our belt, we will complement it with some of the most popular libraries for data analysis in Python, such as Pandas and Numpy for data manipulation, Matplotlib and Seaborn for visualization, and Jupyter Notebook for reporting. These packages will facilitate workflow and enhance the basic Python functionalities. Using them, one can effortlessly clean up a dataset, create elaborate plots, analyze and summarize the data, and produce presentable reports. During this module, you solidify your new skills by applying the concepts you have learned to analyze several datasets. You will have a chance to live-code during the sessions and troubleshoot your code with your classmates and the instructor. You will walk out of this Bootcamp with newly-forged Python coding skills, knowledge of several of the most important data science libraries and tools, and the resources for learning more. 1.5 cr
• QST QM 878: Deep Learning with Python Bootcamp
  Graduate Prerequisites: QM877, IS833, IS834 or instructor permission - In this bootcamp, students will learn the most essential aspects of machine learning, and in particular, deep learning in Python. Prior programming experience in Python is required. We will cover some standard machine learning algorithms and solve business problems using tabular, time-series, and image data using deep learning algorithms. During this module, students solidify their new skills by applying the concepts they have learned to analyze several datasets. They will have a chance to live-code during the sessions and troubleshoot their code with their classmates and the instructor. 1.5 cr.