My Teaching Philosophy
"Topical Relevance, Instructor Performance, and Customer Service"
I believe good teaching consists of three elements: Topical relevance, Instructor performance, and Customer service.
First, students are excited to learn what is truly relevant for their future careers. It’s contingent on the instructor to select the content that is most relevant and present it such that it clearly demonstrates benefit to the students.
Second, within the walls of the classroom, instructors are performers. Enthusiasm, creativity, clarity, variety, and humor are all elements of performance that capture and maintain student attention.
Third, outside of the walls of the classroom, instructors deliver customer service. Students should find course expectations and deliverables clear and the instructor highly accessible with a cheerful attitude.
It is an honor to be an instructor and I consistently evaluate how I can improve my teaching along these three elements.
My Teaching Specialty
Business Analytics
What / Why Happened ?
What will happen ?
What should we do next ?
Analytics is front and center in business today. Whether it’s leveraging big data to extract customer trends, using AI and statistics to build predictive models, or using software to make more effective decisions, analytics is here to stay! Drawing on my rich industry experience and rigorous academic training, I develop and deliver instruction on the three types of analytics tools that lead managers from raw data to effective decisions: 1) Descriptive Analytics tools that enable managers to use data to answer what happened to the organization and why, 2) Predictive Analytics tools that enable managers to use data to predict future trends, 3) Prescriptive Analytics tools that enable managers to make optimal decisions.
My Courses
Business Analytics (Flex MBA, MS Business Analytics/Marketing/Info. Systems)
Graduate Level, Johns Hopkins University, Carey Business School, 2019 – 2022
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In this course we learn how to make optimal decisions to improve the performance of your organization using Prescriptive Analytics tools. The core skills we gain from this course are: 1) how to model a business problem conceptually as an optimization problem, 2) how to formulate the conceptual model quantitatively, and 3) how to solve the optimization model using a variety of solution techniques with Microsoft Excel. These techniques include linear programming, integer programming, decision trees, and Monte Carlo simulation.
Operations Analytics (Global Supply Chain Management Major)
Undergraduate Level, Brigham Young University, Marriott School of Business, 2023 – Current
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The purpose of this course is to prepare Global Supply Chain Management undergraduate students to make quick impact on their summer internship and/or postgraduate job using analytics. We first cover optimization modelling so that students can drive towards results by first rigorously define their problem objective, relevant decisions, and constraints. We then cover data extraction using SQL queries and data wrangling using Excel so that student can draw actionable insights. Finally, we cover presentation with data so that students can succinctly articulate what they have found and recommend.
Advanced Operations Analytics (Global Supply Chain Management Major / MBA Elective)
Upper Division Undergraduate Level, Brigham Young University, Marriott School of Business, 2025 – Current
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The purpose of this course is to expose Global Supply Chain Management undergraduate students and MBA students to more advanced operations analytics topics. We first tackle demand forecasting (an essential component of nearly every business) using multlinear regression. We then explore how to deal with uncertainty in optimization using Monte Carlo simulation. Finally, we examine more advanced optimization problems such as large-scale network design and dynamic optimization. The software includes Excel and basic Python programming, with no previous programming experience required.