• QST QM 221: Probabilistic and Statistical Decision-Making for Management
  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
  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 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
  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 870: R Bootcamp
  This course teaches the essentials of R programming and complements this with knowledge of some of the most popular libraries in data analysis and data visualization. Upon completion, students will apply these skills to a several in-demand applications. No prior programming experience is assumed. The course covers variables, data types and data structures, frames, conditionals and loops, and functions. It also covers reading and writing CSV files, and the core APIs in analysis and visualization. Students will be introduced to the most popular libraries for data analysis in R, such as dplyr, ggplot2, readr, and rmarkdown. These packages will facilitate the workflow and enhance the basic R functionalities; using them, one can clean up a dataset, create elaborate plots, analyze and summarize the data, and produce presentable reports.
• QST QM 875: Python for Data Science Bootcamp
  In this course, students will learn the most essential aspects of python programming. The topics are tailored towards data analysis; no prior programming experience is required. We will cover variables, data types and data structures, data frames, 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 their belt, we will complement these concepts 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 analysis and 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, students solidify their new skills by applying the concepts they have learned to the analysis of several datasets. They will be given the opportunity to live-code during the sessions and troubleshoot their code with classmates and the instructor. Students 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 have the resources in hand for learning more. Please note that students in the MSDi and MSDT programs may not take this course for degree credit.
• QST QM 880: Business Analytics: Spreadsheet Optimization and Simulation
  The modeling process illustrated throughout the course will significantly improve students' abilities to structure complex problems and derive insights about the value of alternatives. You will develop the skills to formulate and analyze a wide range of models that can aid in managerial decision-making in the functional areas of business. These areas include finance (capital budgeting, cash planning, portfolio optimization, valuing options, hedging investments), marketing (pricing, sales force allocation, planning advertising budgets) and operations (production planning, workforce scheduling, facility location, project management). The course will be taught almost entirely by example, using problems from the main functional areas of business. This course is not for people who want a general introduction to or review of Excel. This course is for students who are already comfortable using Excel and would like to use it to create optimization and simulation models.
• QST QM 898: Directed Study: Quantitative Methods
  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-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
  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
  PhD-level directed study in Quantitative Methods. 1, 2, or 3 cr. Application available on the Graduate Center website.

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