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 474: Action Learning Lab: Platform Strategies
  Prerequisites: QSTIS 223 and QSTXP 298; or QSTIS 223, QSTFE 323, QSTMK 323, QSTOM 323 and QSTQM 323. - The world's most valuable companies—Alibaba, Amazon, Apple, Google, Meta, Microsoft, Uber—don't sell standalone products. They build platforms connecting multiple customer groups, harnessing network effects to create exponential value. This course teaches students how platforms work, why they're powerful yet hard to build, and how to design, launch, and scale them successfully. Through frameworks, economic models, and case studies spanning Airbnb to TikTok, students will tackle critical questions: How should platforms price when traditional models fail? When should they open versus close their architectures? How much responsibility should they take for harmful content? Can AI and blockchain reshape platform power? The centerpiece is a hands-on consulting project with a real sponsoring company. Past clients include Cisco, eBay, Haier, Mahindra, Pearson, SAP, Siemens, and the U.S. Postal Service. Students conduct stakeholder interviews and deliver strategic recommendations. Previous teams have been hired by clients and secured startup funding for their proposals.
• QST IS 498: Directed Study: Management Information Systems
  Directed study in Management Information Systems. 2 or 4 cr. Application available on Undergraduate Program website.
• QST IS 565: Managing Data Resources
  Undergraduate Prerequisites: QST IS223 and QST BA222 or CAS CS105 or CAS CS108 or CAS CS111 or CDS DS110 (co-requisite /pre-requisite) - Required for Information Systems concentrators. Provides a practical and theoretical introduction to data management focusing on the use of relational database technology and SQL to manage an organization's data and information. Introduces recent topics such as data warehouses and Web databases. Includes a project to design and implement a relational database to manage an organization's data. 4 cr.
• QST IS 579: Financial Business Modeling with Spreadsheets
  Prerequisites: QSTAC 414 or QSTAC 420 or QSTFE 445 or QSTFE 449 or QSTIS 465 or QSTSI 445. This course enhances students' quantitative modeling and analysis skills in addressing complex financial decisions, focusing on effective problem formulation and analysis. Students will learn to identify the critical components of the decision-making tasks, structure them methodically, translate them into visual and analytical models, and develop appropriate mathematical models and their spreadsheet solutions. With a focus on finance modeling, the course introduces advanced Excel tools, such as Pivot Tables for interactive data analysis and Power BI for building executive KPI dashboards. Students will explore essential data management and decision tools such as Formula Diagrams, Linear Optimization, Error Detection methodologies, and Parametric Sensitivity Analysis. The course also provides extensive hands-on experience with Solver to optimize financial strategies, such as designing an investment portfolio that optimally balances risk and return. Real-world applications drawn from finance and accounting will guide students through the financial modeling cycle: planning, control, and feedback. By the end of the course, students will have developed the technical and analytical skills needed to approach complex financial problems and deliver data-driven solutions that support thoughtful strategic decision-making.
• QST IS 717: Systems Architecture in Management and Applications
  Graduate Prerequisites: MSDT students only - The objective of this course is to provide an overview of the concept of systems architecture and how it has evolved from a technical notion to an important business issue. The course has several themes: (1) Students develop an appreciation of how a business may leverage architectural design choices for operational and competitive advantage, both at a technical and business level. (2) Students obtain insight into how interface driven systems (those using APIs) enable flexibility and increased innovation. (3) Students are confronted with the difficult aspects of modern systems, such as parallelism and concurrency, and how these technical challenges are managed. An introduction to programming in Python provides a context to help students develop a hands-on understanding of these concepts. Care is taken to not just apply the technical material to management contexts, but also inform business strategy and organization and operations through these architectural principles.
• QST IS 737: Decision Making with Data
  Graduate Prerequisites: QST IS717, IS833, IS834, QM877, or knowledge of Python - This is an advanced python-based analytics course on data-driven decision-making in business environments. Business analytics professionals need to be able to i) uncover patterns in the data (descriptive analytics); ii) use the data to make predictions about future outcomes (predictive analytics); and iii) leverage this data to make optimal business decisions (prescriptive analytics). This course takes a holistic approach to analytics, touching on aspects of all three descriptive, predictive, and prescriptive pillars. We explore advanced business analytics topics, including data reduction, classification, decision analysis, and optimization. We link data models to strategy relying on statistical programming in Python and introduce novel techniques used in practice. Case studies and projects apply topics to practical business problems. 3 cr.
• QST IS 795: Blockchains and their Applications
  Blockchain technology amalgamates technical tools, economic mechanisms, and system design patterns. It facilitates the construction of information systems with novel combinations of robustness, decentralization, privacy, cost, and flexibility. Beyond their initial use in cryptocurrencies such as Bitcoin, blockchains have become a promising and powerful technology in business, financial services, law, and other areas. This course covers blockchain technology in a comprehensive, systematic, and interdisciplinary way. It surveys major approaches, variants, and applications of blockchains in these areas. Beyond a solid grasp of the principles, the course aims to build familiarity with practice through numerous case studies and hands-on projects. To facilitate its interdisciplinary perspective, this course will be open to two categories of students: students with Computer Science background (graduate or advanced undergraduate), and graduate students with a substantial Business or Law background and a working knowledge of computer programming. Projects will be done in heterogeneous teams combining these categories, and will center on devising and analyzing sample applications of blockchain technology, including both prototype implementations and analysis of its business/legal implications. Topics covered: disentangling 'blockchain'; cryptographic prerequisites; assets and their representations; on-chain programming; state consensus; deployments; decentralized applications (Dapps/Web3); protocol governance; protocol revenue and business models; market structure; privacy and authorization; regulation.
• QST IS 811: Responsible AI for Business Analytics
  AI and data are powerful tools that require thoughtful and responsible applications. The stakes are high as algorithms can scale to influence millions and even billions of people within a moment of launch and morph businesses and society with the impact that might last generations. Our mission is to understand and anticipate potential pitfalls and misuse of personal data and AI to guard against harm. We will also discuss existing solutions and the lack of solutions. The course is designed to be accessible to everyone and targeted to practitioners whose businesses will use some form of data and algorithms. Examples from Technology, Retail, Marketing, Health Care, Finance, and Society will be discussed.
• QST IS 813: Generative AI: Implementation and Impact for Business
  Central to the curriculum is the practical, hands-on approach to understanding Generative AI. Students will gain firsthand experience with language models like GPT, learning the intricacies of model fine-tuning, prompt engineering and its various frameworks, and deployment strategies through interactive sessions and real-world projects. This approach ensures that participants develop a robust technical skillset, enabling them to effectively implement and utilize Generative AI technologies in business contexts. The course also delves into advanced issues critical to the responsible implementation of AI including privacy, ethics, bias, data integrity, and challenges like mitigating hallucinations in AI outputs. By the end of the course, participants will be equipped not just with theoretical knowledge but also with practical skills, empowering them to harness the potential of Generative AI in innovative and strategic ways. While some minimal coding experience is expected, this class does not require advanced technical skills as a pre-requisite.
• QST IS 823: Analytics for Managers
  This non-programming-based analytics course examines how the abundance of data has transformed decision making in organizations and the strategic implications of this transformation. We explore how data are being used, ranging from the core principles of properly identifying data sources to the actual analytical methods being used to solve a wide range of business problems. Students will have some hands-on work with advanced Excel, Tableau, and two database applications, Microsoft Access and Neo4j (Neo4j is used to compare and contrast SQL and NoSQL databases in an analytics context). At the end of this course, students will have gained a big-picture perspective on business analytics as well as hands-on experience with commonly-used business analytics software.
• QST IS 827: Platform Strategy and Design
  Managers, entrepreneurs, and investors need mastery of platform business models to thrive in the modern economy. Today's most powerful firms—Airbnb, Amazon, Salesforce, Tencent—operate as platforms connecting buyers and sellers, users and advertisers, developers and consumers, deriving value from network effects and orchestrated ecosystem innovation. This rigorous course combines advanced theory from industrial economics, market design, and game theory with practical application. Students will develop expertise in platform design, pricing strategy, governance, competitive dynamics, and regulatory challenges through case studies spanning social media, enterprise software, healthcare, and emerging technologies including AI, blockchain, and Web3. The signature element is an intensive consulting engagement with a sponsoring organization. Past clients include Airbnb, Allstate, BASF, edX, IBM, Microsoft, PTC, and Thomson Reuters. Teams conduct stakeholder interviews and produce comprehensive 25-35 page strategic blueprints with executive presentations. Projects have resulted in direct hiring, million-dollar budget allocations, and new venture launches. This class prepares students for careers in strategy, technology, entrepreneurship, venture capital, or any industry competing with or transforming to platforms.
• QST IS 828: Managing Information Security
  Graduate Prerequisites: IS710/711/716 - This MBA elective (also open to undergraduates) will combine a technical and business approach to the management of information. It will address technical issues such as cryptography, intrusion detection and firewalls along with managerial ideas such as overall security policies, managing uncertainty and risk and organization factors. We will examine different aspects of computer security such as passwords, virus protection and managing computer security in dynamic environments. Topics will also include network security and how to secure wireless application and services. These technical details will be placed in a business context. The class will have a practical focus as we examine current best practices. There well be several guest speakers in the security area. This will be a project oriented class and students will present their research projects during the last several classes.
• 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
  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 853: Business Insights through Text
  Graduate Prerequisites: (QSTMK 842 OR QSTIS 833 OR QSTIS 834 OR QSTQM 877) QSTMK 842 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 with 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.