Quantitative Modeling
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QST BA 221: Introduction to Data and Business Analytics
Undergraduate pre-requisite: QSTSM 131 or sophomore standing and QSTSM 131 previously or concurrently. - Exposes students to business data and business analytics. Topics in business analytics include the fundamentals of probability and statistics, but the emphasis is on the collection, structuring, and analysis of data to support business decision-making. Topics include descriptive, predictive, and prescriptive analytics, as well as distributions, sampling, estimation, hypothesis testing, and chi-square analyses. Effective Fall 2025, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning I. -
QST BA 222: Modeling Business Decisions and Market Outcomes with Spreadsheets and Statistical Programming
Undergraduate Prerequisites: QSTBA 221. - Students must choose either QSTBA 222 or QSTBA 223, and students cannot take both courses. This course examines the use of economic and statistical tools for making business decisions at an advanced level, and prepares students for future study in business analytics. Introduces programming for data analysis (no previous programming knowledge required) and links data analysis to decision making using both spreadsheet modeling and statistical programming. Topics include multiple regression, causal inference, forecasting, predictive analytics, machine learning, demand modeling, and optimization. Case studies apply advanced concepts to practical business problems. Effective Spring 2021, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II. -
QST BA 223: Business Modeling with Spreadsheets
Undergraduate pre-requisite: QSTBA 221. Formerly QSTQM 222. Students must choose either QSTBA 222 or QSTBA 223 (formerly QM222). Students cannot take both BA222 and BA223. - This course examines the use of economic and statistical tools for making business decisions. The course emphasizes linking data analysis to spreadsheet modeling to support advanced business decision making. Topics include multiple regression, causal inference, forecasting, demand modeling, and optimization. Case studies apply concepts to practical business problems and the principal software tool used in the course is the spreadsheet. Effective Fall 2025, this course fulfills a single unit in the following BU Hub area: 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 895: Action Learning Directed Study in Quantitative Methods
ALDS: QUANT MTH -
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.
