Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. 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 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 754: Human Centered Design
  Graduate Prerequisites: MSDT students only
  When constructing digital artifacts, either for internal or external consumption, creating a coherent and well-motivated design is essential. This class will explore the language and key principles of design and how these impact the user experience. We will also cover the essentials of project management, including scoping as a design constraint. Students will be asked to critique existing designs and offer alternatives. Additionally, we will cover the programming topic of object orientation, and study both it and the structures it enables from a design perspective.
• QST IS 756: Digital Project Build
  Graduate Prerequisites: MSDT students only
  The modern economy is driven by dynamic websites: those whose content is created on the fly by programs, not humans. In this class we will learn the technology behind both static and dynamic websites. Students will be exposed to the concepts through lectures and labs, and ultimately by the construction of a complete dynamic website in Python using our custom infrastructure built on Google App Engine.
• 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 815: FinTech Revolution: Disruptive Technologies, Blockchain and Future of Finance
  FinTech is the intersection of finance, technology, and regulation. The course aims to help students understand FinTech and recognize the challenges and opportunities the innovations within FinTech pose. Students gain a basic understanding of the foundations and principles that enabled the rise of FinTech and examine cases for a more in-depth analysis of specific innovations such as blockchain and cryptocurrencies. The course advances students' knowledge of FinTech, much needed skill in the industry, and gives students insight into the applications, limitations, and risks of technological innovations in finance.
• 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
  Graduate Prerequisites: QST PL727 or PL730, QST SI750 or SI751, or permission of instructor
  To thrive in the modern economy, managers, entrepreneurs and investors need a thorough understanding of platform businesses. Indeed, today's most powerful and valuable firms, from Airbnb, to Amazon, Facebook and Salesforce, operate as platforms connecting buyers and sellers, or users and advertisers, or users and third-party developers. These platforms derive their value from network effects and the ability to harness innovation from their users. Drawing on cases from social media, entrepreneurship, enterprise software, mobile services, and consumer products, we will analyze and learn to launch platform startups, convert existing businesses into platforms, and compete in a platform-centric world. Students will learn to apply concepts from industrial economics, market design, and game theory to real problems.
• 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 permission)
  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 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 equivalent); Some prior experience with analytics (e.g. IS823, IS833, IS834, IS841, 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: QST MK 842 or QST IS 833 or QST IS 834 or QST QM 877; 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 878: Business Modeling with Spreadsheets
  This course aims to sharpen students' ability to frame a business problem and organize the relevant information in a way that is conducive to developing a spreadsheet model, and to perform logical analyses in an organized and rigorous fashion. Students will learn how to create a workable prototype of a spreadsheet based upon formula-charts, or a directed-graph diagram, applying principles of spreadsheet engineering to design the spreadsheet in a way that prevents errors. The course will teach students how to design, build, test, and use a spreadsheet, as well as how to process and visualize data in preparation for building a well-structured model. Students will be exposed to settings in which models can be used effectively. They will apply modeling concepts in practical situations and learn to extract insight from models and to use those insights to communicate, persuade and motivate organizational decision making. They will also learn how to formulate a constrained optimization problem with multiple variables for a variety of applications including, Strategy, Operations, Technology Management, Marketing, and Finance. The course will show how to understand how to use sensitivity analysis to evaluate the impact of a parameter on the optimal solution to a problem and cover the key principles for Real-Time Data Capture for Analytic. Finally, students will learn the latest technologies for effectively linking spreadsheets to relational data bases, and to reliably manage large scale spreadsheets. 1.5 cr.
• 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: Synthesizing Digital Efforts to Deliver Better Outcomes
  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. Foremost among them are how individuals with skills across strategy, design, product/project management, technology, and data science think individually and how they work together collectively. In this class, students will learn and apply leading thinking, practices, and tools used by top digital professionals to design and build digital products. The first half of the course will focus on cloud technology and its effect on organizational structures and product development lifecycles. In the second half of the semester, students will organize into teams to begin work on a digital experience they will design and iteratively realize the following semester in IS890, applying skills they have learned throughout the MSDT as well as new methods including exploratory ethnography, service design, agile/scrum, and data-driven experimentation.
• 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.