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 PL 835: Managing Your Workforce
  All students of Management will be employees and/or managers, yet very few know their rights or their responsibilities in those roles. Understanding the legal contours will help students better navigate their career, make smarter management decisions, and hopefully correct behavior or policies before they become lawsuits.
• QST PL 855: Energy Sector Market Dynamics
  This course is designed as a multi-dimensional approach to understanding the energy sector. This includes production, development, distribution, financing, and consumption relating to the two distinct sectors - Power Generation and Transportation, both domestically and internationally. For Power Generation, we will explore the fundamentals of Generation, Transmission (G&T), and Distribution as well as major feedstocks, including wind, solar, nuclear, natural gas, and coal. This includes an in-depth discussion of both challenges and opportunities inherent to altering the current system.
• QST PL 861: Emerging Issues in Business and Law
  You ask your outside lawyer or your company's legal department whether you can undertake some activity without violating the law. You are annoyed when you are told "Well, maybe. It depends". You want a yes-or-no answer, not a game of twenty questions. Why can't your lawyers give you a straight answer? Why do they make everything more complicated? What language are they speaking? Most business people ask these questions. If you do business you cannot avoid dealing with lawyers. You can allow your interactions with lawyers to frustrate you, or you can learn how lawyers think so that you can better manage them. Emerging Issues in Business Law introduces graduate business students to fundamentals of legal analysis by focusing on timely legal problems of particular interest to business. Students develop familiarity with substantive legal principles and leave the course with the ability to recognize legal issues, discuss them intelligently, and understand why the lawyers seem incapable of giving a simple answer. The course uses lectures to provide a common foundation of knowledge. It is primarily discussion based, using a question and answer format to engage students in the process of legal analysis.
• QST PL 898: Directed Study: Markets, Public Policy, and Law
  Graduate Prerequisites: Consent of instructor and the department chair - Graduate-level directed study in Markets, Public Policy, and Law. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST PL 899: Directed Study: Markets, Public Policy, and Law
  Graduate Prerequisites: Consent of instructor and the department chair - Graduate-level directed study in Markets, Public Policy, and Law. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST PL 998: Directed Study: Markets, Public Policy, and Law
  Graduate Prerequisites: Consent of instructor and the department chair - PhD-level directed study in Markets, Public Policy, and Law. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST PL 999: Directed Study: Markets, Public Policy, and Law
  Graduate Prerequisites: Consent of instructor and the department chair - PhD-level directed study in Markets, Public Policy, and Law. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST QM 221: Probabilistic and Statistical Decision-Making for Management
  Undergraduate Prerequisites: QST SM131; CAS MA120, MA121 or MA123 previous or concurrent. - Exposes students to the fundamentals of probability, decision analysis, and statistics, and their application to business. Topics include probability, decision analysis, distributions, sampling, estimation, hypothesis testing, and chi-square. Effective Fall 2018, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning I.
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  • Quantitative Reasoning I
• QST QM 222: Modeling Business Decisions and Market Outcomes
  Examines the use of economic and statistical tools for making business decisions. The course emphasizes linking data analysis to spreadsheet modeling of decision making. Topics include multiple regression, causal inference, forecasting, demand modeling, and optimization. Case studies apply concepts to practical business problems. Effective Fall 2018, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
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  • Quantitative Reasoning II
• QST QM 323: Analytics
  Undergraduate Prerequisites, Questrom students only: QST AC221; MO221; QM221; QM222 or BA222; SM131; SM132; SM275 - Component of QST SM 323, The Cross Functional Core. Teaches quantitative methods and modeling techniques that will improve the student's ability to make informed decisions in an uncertain world. The two major modules of the course are models for optimal decision-making and decision- making under uncertainty. The first module focuses on methods and predictive models for decision-making; how optimization models are used to identify the best choice; and how choices change in response to changes in the model's parameters (sensitivity analysis). The second module covers the measurement and management of risk and Monte Carlo simulation. Throughout the semester, we will perform hands-on analysis that will improve Excel modeling skills; discuss the ethical use of data analytics; and learn to recognize pitfalls and biases in quantitative decision-making. cr. N
• QST QM 498: Directed Study: Quantitative Methods
  Directed study in Quantitative Methods. 2 or 4 cr. Application available on Undergraduate Program website.
• QST QM 716: Business Analytics: Data Analysis and Risk
  The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
• QST QM 717: Data Analysis for Managerial Decision-Making
  Graduate Prerequisites: (QSTMO712 OR QSTMO713) - The overall goal of this course is to improve student ability to learn from data, specifically to 1) assess the validity of conclusions that have been drawn from statistical analyses; 2) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data; and 3) portray, summarize and analyze data to support operational and strategic decisions associated with the core business models. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
• 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.
• QST QM 898: Directed Study: Quantitative Methods
  Graduate Prerequisites: Consent of instructor and the department chair - Graduate-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST QM 899: Directed Study: Quantitative Methods
  Graduate Prerequisites: Consent of instructor and the department chair - Graduate-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST QM 998: Directed Study: Quantitative Methods
  Graduate Prerequisites: Consent of instructor and the department chair - PhD-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST QM 999: Directed Study: Quantitative Methods
  Graduate Prerequisites: Consent of instructor and the department chair - PhD-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST SI 250: Ideas to Impact
  This course is required for the Innovation and Entrepreneurship minor. The goal of this course is to expose students to the conceptual frameworks that guide ideation and innovation. Thus it will include all five learning principles the guide design of the Innovation and Entrepreneurship minor. The course analyzes the conditions that foster innovation as well as the process by which ideas progress from conception to implementation and execution, and the creation of either economic or social impact. Students will be exposed to theories on the conditions that affect the generation and development of creativity and innovation within individuals, teams, cities, and regions. To foster experiential learning, the whole class will be structured around the process of innovation with a "live case" that focuses on creating social innovations for the City of Boston. When people think about great social challenges, they often look afar to distant countries. Yet, many social problems lie right around the corner from students' daily lives. Students will develop a toolkit comprised of brainstorming, design thinking, human centered design, prototyping, storyboarding and field research. Students will conduct original field research within the City of Boston and identify a challenge or problem to address which they will focus on for the duration of the course, culminating in final presentations. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry I, The Individual in Community, Creativity/Innovation.
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  • Creativity/Innovation
  • Social Inquiry I