My recent teaching is primarily in analytics. I also have experience teaching project management, operations management, probability & statistics, and freshmen engineering.
EBTM 350: Business Analytics
This course addresses the contemporary business issues of using data to support decision-making and implement change. The course focuses on using standard business analytic models to summarize and analyze data, build models, and drive impact through quantitative decision-making. Innovative trends in business will be explored, through methods to create and frame problems. Descriptive, predictive, and prescriptive analytics will be discussed, illustrating the transformation from knowledge gained through problem formation into practice. Creative solutions to open-ended analytics problems will be explored, using data to discover patterns and trends.
Interdisciplinary Cyber4All module
This module can be used as a security injection into an existing course. Students are expected to spend 20-40 minutes with the topic and receive a certificate of completion at the end of the module.
Summary: Each individual, company, or organization has unique values for cybersecurity. These values are what we care about when protecting ourselves and our systems from hackers and malicious actors. Value modeling can help us identify our values and needs in order to mitigate risk and support following best practices that help to keep our systems and devices secure.
EBTM 306: Fundamentals of Project Management and Business Analysis
This course addresses the contemporary business issues of using data to support decision making and using project management techniques to implement change. The project fundamentals module covers the essentials of needs identification, project definition, and project planning. Specific tools include work breakdown structures, Gantt Charts, and network diagrams. The emphasis is on applications in practical business situations through the use of case studies and software such as Microsoft Project. The decision analysis module focuses on using standard business spreadsheet software to summarize and analyze data and build decision models. Specific tools include spreadsheet modeling, optimization, and trendlines.
EBTM 446: Business Intelligence
Classifications of business decision problems and methods of analysis to identify the best solutions using business records for business intelligence. Methods of managing large storage of business records and related information as well as the discovery of knowledge to support managerial decision-making.
OPRE 605: Business Analytics
Business analytics and their applications to management decision making will be explored for a range of business situations including: finance, marketing, operations management, and other business areas. Analytics covered include: problem structuring, big data, data mining, optimization, computer simulation, decision analysis, and predictive modeling. Upon completion of this course, students are expected to competently use selected analytics, to provide management interpretation of the solutions, and to formulate business recommendations. The course utilizes advanced computer modeling tools available in Microsoft Excel and other modeling software packages.
University of Pittsburgh
BUSQOM 1070: Operations Management
This course: (1) explains the strategic role of operations management (OM) and its competitive advantage for organizational survival; (2) clarifies the relationship between the OM function and other business functions (examples: marketing, finance, and information systems) and how they can work together to achieve the business strategy; (3) analyzes business processes to uncover problems, possible solutions, and improvement opportunities; (4) measures the performance of operations from different aspects such as productivity, inventory management, and quality control; (5) applies quantitative models to solve real world problems.
ENGR 0020: Probability and Statistics for Engineers I
Served as Teaching Assistant, also independently taught course
This course is designed for students majoring in engineering. Topics include: data analysis, probability, random variables, discrete and continuous probability distributions, estimation, hypothesis testing, correlation, and linear regression.
ENGR 0011: Introduction to Engineering Analysis
Served as Teaching Assistant
Major content areas included UNIX commands, HTML coding, Microsoft Excel fundamentals, and MATLAB.
ENGR 0012: Introduction to Engineering Computing
Served as Teaching Assistant
Major content areas included MATLAB and C++ programming.