This course is designed to show you how use quantitative models to transform data into better business decisions. You’ll learn both how to use models to facilitate decision-making and also how to structure decision-making for optimum results. Two of Wharton’s most acclaimed professors will show you the step-by-step processes of modeling common business and financial scenarios, so you can significantly improve your ability to structure complex problems and derive useful insights about alternatives. Once you’ve created models of existing realities, possible risks, and alternative scenarios, you can determine the best solution for your business or enterprise, using the decision-making tools and techniques you’ve learned in this course.
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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The course material and examples are very useful. I really like the CF spreadsheet at the end of week 4 as this is very organized CF model and I can use this as reference to build my own. Thank you!
I think I would be more helpfull if you worked in more detail on the example. Rather than just reading of the slides, it would be much better if we could follow you step by step in calculatons.
Thank you for the wonderful course. It gave us substantial information to evaluate a project based on NPV and IRR. The sensitivity analyses and the consequent scenarios were spelt out lucidly.
關於 商业与金融建模 專項課程
Wharton's Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational courses will introduce you to spreadsheet models, modeling techniques, and common applications for investment analysis, company valuation, forecasting, and more. When you complete the Specialization, you'll be ready to use your own data to describe realities, build scenarios, and predict performance.