NYU Stern School of Business
44 West 4th Street
New York, NY 10012
Most firms invest time and dollars into data analytics that identify what has already happened and what might happen in the future, but this is not enough to drive success. In order to take full advantage of their data analytics, executives must know how to transform data insights into optimal, executable actions that are evaluated by their impact on key performance metrics, leading to better decision making.
This course teaches participants to harness the full potential of large quantities of data to make more informed decisions at all levels of their organizations. Participants will learn about modern decision models and machine learning tools. Through application of these tools, executives will examine data, recommend a range of actions and evaluate each action’s impact on targeted performance metrics. This course provides hands-on experience working with different models--including optimization modeling, uncertainty modeling and risk prediction--and emphasizes their application in finance, marketing and operations functions across industries.
Upon completion of this course, participants will receive a Certificate of Achievement.
During this course, participants will:
Decision ModelsLearn about key decision models in analytics and their applications across a wide range of industries including healthcare, financial services, logistics and more
Direct ExperienceGain hands-on experience working with data and transforming it into actionable decisions through simulation exercises
Value of DataIdentify opportunities where decision models can be applied to derive value for your organization
Who Should Attend
Although there are no formal education or background requirements, this course is designed for executives who meet the criteria below. While we strongly encourage global participation, please note that all courses are taught in English. Proficiency in written and spoken English is required.
Years of ExperienceDesigned for professionals with 5+ years of work experience
Job FunctionsIdeal for executives who head analytically oriented functions within their organization
PrerequisitesIntended for individuals who are interested in analytics and data-driven decision making, and who already have working knowledge of analytics
The following agenda is a sample and subject to change.
9:00 am ET: Session 1: Predictive and Prescriptive Analytics
- What is prescriptive analytics and why is it important?
- Differences between prescriptive and predictive analytics and their roles in data-driven decision making
- Best practices and success stories
Session 2: Machine Learning and Predictive Modeling
- Basic classification and prediction methods
- What is artificial neutral network and what is deep learning
- Hands-on exercise: credit risk prediction
12:15 - 1:15 pm ET: Lunch Break
Session 3: Framework of Optimization Modeling
- Formulating a business decision problem: decision choices, performance measure, and constraints
- Hands-on exercise: online dating platform
Session 4: Business Applications of Optimization Models
- Applications in revenue management and online advertising
- Value of optimization
- Challenge and address model assumptions
4:30 pm ET: Day 1 Conclusion and Evaluations
9:00 am ET: Session 5: Markdown Optimization Game
- Combining predictive modeling and optimization modeling
- Challenges in decision making under risk
Session 6: Modeling Risk
- Meaningful definition of risk
- How to model uncertainty
- Value of data in risk modeling
12:15 - 1:15 pm ET: Lunch Break
Session 7: Risk Prediction: Monte Carlo Simulation
- Build simulation models for performance evaluation and risk prediction
- Interpretation of results and obtaining insights
- Hands-on exercise: retirement planning
Session 8: Optimization under Uncertainty
- Simulation meets optimization modeling
- Value of strategic flexibility
4:30 pm ET: Program Conclusion and Evaluations
Join Our Mailing List
Stay up to date on new courses, special events, free content and more. Enter your email address below to subscribe.