Quantitative Modeling
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QST BA 222: Modeling Business Decisions and Market Outcomes with Spreadsheets and Statistical Programming
Undergraduate Prerequisites: CAS EC101, QST QM221, and QST SM131
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 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. -
QST QM 222: Modeling Business Decisions and Market Outcomes
Undergraduate Prerequisites: CAS EC101, QST QM221, and QST SM131
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. -
QST QM 323: Analytics
Undergraduate Prerequisites: 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 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: QST MO 712 or QST MO 713.
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 880: Business Analytics: Spreadsheet Optimization and Simulation
Graduate Prerequisites: QST QM 716 or QST QM 717.
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 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.
