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
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.
Business Analytics (Flex MBA, MS Business Analytics/Marketing/Info. Systems)
Graduate Level, Johns Hopkins University, Carey Business School, 2019 – 2022
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
Operations analytics makes extensive use of data and modeling to drive operations-related decision-making in organizations. To become a leader in a data-driven world, it is critical to acquire hands-on experience of both data-related and modeling skills. This class introduces students to analytical frameworks used for decision-making to make sense of data.
For each topic/methodology, students are first exposed to the basic mechanics of the framework, and then apply the methodology to several business problems using software. The methodologies covered include nonlinear, linear and integer linear programming, decision trees, Monte Carlo simulation, data extraction, pivot tables and charts, and time-series forecasting techniques. The applications include deterministic and stochastic optimization, data wrangling and visualization, and business forecasting.