Information Systems

• QST IS 833: Introduction to Data Analytics in Python
  Graduate Prerequisites: MSDT Students Only; IS717 or equivalent Python experience - This course will introduce students to programming-based tools and techniques for becoming analytically-minded managers. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. We will review variables, data types, conditionals, loops, and functions, and use these to introduce data structures, including DataFrames. 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. Throughout the final project, we will learn to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise data preprocessing, explanatory analysis, and machine learning techniques. Prior programming experience in Python is required.
• QST IS 834: Introduction to Python for Data Analytics
  Graduate Prerequisites: Not open to MSDT students (except as a substitute for IS833 with permi ssion) - This course will introduce students to programming-based tools and techniques for becoming analytically-minded managers. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. 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. Throughout the final project, we will learn to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise data preprocessing, explanatory analysis, and machine learning techniques. No prior programming experience is required. Learning basic programming in Python is part of successfully completing the class.
• QST IS 843: Big Data Analytics for Business
  Pre-requisites: 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 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 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 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: 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: 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.