Decision Modeling in Business Analytics

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.

CERTIFICATES AND CREDITS

Upon completion of this course, participants will receive a Certificate of Achievement. This program is also eligible for Continuing Professional Education (CPE) credits through NASBA.

Program Takeaways

During this course, participants will:

Decision Models

Learn about key decision models in analytics and their applications across a wide range of industries including healthcare, financial services, logistics and more

Direct Experience

Gain hands-on experience working with data and transforming it into actionable decisions through simulation exercises

Value of Data

Identify opportunities where decision models can be applied to derive value for your organization

Agenda

Day 1

8:30 am - 9:00 am: Breakfast

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 pm - 1:15 pm: Lunch

Session 3: Framework of Optimization Modeling

• Formulating a business decision problem: decision choices, performance measure, and constraints
• Hands-on exercise: online dating platforms

Session 4: Business Applications of Optimization Models

• Applications in revenue management and online advertising
• Value of optimization
• Challenge and address model assumptions

Day 2

8:30 am - 9:00 am: Breakfast

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 pm - 1:15 pm: Lunch

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 - 5:00 pm: Program Conclusion and Evaluations