Linda Nozick is Professor and Director of Civil and Environmental Engineering at Cornell University. She is co-founder and a past director of the College Program in Systems Engineering and has been the recipient of several awards, including a CAREER award from the National Science Foundation and a Presidential Early Career Award for Scientists and Engineers from President Clinton for “the development of innovative solutions to problems associated with the transportation of hazardous waste.” Dr. Nozick has authored over 60 peer-reviewed publications, many focused on transportation, the movement of hazardous materials, and the modeling of critical infrastructure systems. She has been an associate editor for Naval Research Logistics and a member of the editorial board of Transportation Research Part A. Dr. Nozick has served on two National Academy Committees to advise the U.S. Department of Energy on renewal of their infrastructure. During the 1998-1999 academic year, she was a Visiting Associate Professor in the Operations Research Department at the Naval Postgraduate School in Monterey, California. Dr. Nozick holds a B.S. in Systems Analysis and Engineering from the George Washington University and an MSE and Ph.D. in Systems Engineering from the University of Pennsylvania.
Simulation is about quantifying the outcome of specific "what if" questions. What if the average demand for tickets on a 150-seat aircraft is actually 200? What if people who have purchased a ticket don't show up? What if we offered a different number, or economy and first-class tickets? Perhaps most importantly, what effect do these "what if" scenarios have on total revenue?
As you might guess, many "what if" questions in the real world are fundamentally uncertain; there is no deterministic formula for predicting exactly how many people will not show up for a given flight. You can, however, use historical data to estimate no-show probabilities. Once you conclude that uncertainty plays an important part in your problem, it may be that you will have to turn to a probabilistic simulation. Running many replications of the simulation will then help you statistically analyze the system's behavior and assess the effects of different design choices.
In this course, you will explore the intricacies of designing and analyzing probabilistic simulations. You will also run simulations using packages in the free and open-source statistical programming language R to solve real-world logistical business problems. The focus will be on making these methods accessible for you in your own work.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Understanding Data Analytics
- Finding Patterns in Data: Association Rules, PCA, and Factor Analysis
- Finding Patterns in Data: Cluster and Hotspot Analysis
- Regression Analysis and Discrete Choice Models
- Supervised Learning Techniques
- Neural Networks and Machine Learning
- Making Data-Driven Recommendations Using Optimization
Key Course Takeaways
- Develop skills in discrete event simulation using R
- Recognize what an agent-based simulation is and how it compares to a discrete event simulation
- Use variance reduction techniques
How It Works
Who Should Enroll
- Current and aspiring data scientists
- Technical managers