Acerca de este curso: Useful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. In this course, you can learn how to create quantitative models to reflect complex realities, and how to include in your model elements of risk and uncertainty. You’ll also learn the methods for creating predictive models for identifying optimal choices; and how those choices change in response to changes in the model’s assumptions. You’ll also learn the basics of the measurement and management of risk. By the end of this course, you’ll be able to build your own models with your own data, so that you can begin making data-informed decisions. You’ll also be prepared for the next course in the Specialization.

Creada por: University of Pennsylvania

Enseñado por: Sergei Savin, Associate Professor of Operations, Information and Decisions
The Wharton School
Enseñado por: Senthil Veeraraghavan, Associate Professor of Operations, Information and Decisions
The Wharton School

Información básica	Curso 3 de 5 en Business and Financial Modeling Specialization
Compromiso	4 weeks of study, 1-3 hours/week
Idioma	Inglés (English), Subtítulos: Portugués (de Brasil)
Cómo aprobar	Aprueba todas las tareas calificadas para completar el curso.
Calificaciones del usuario	4.6 estrellas Calificación promedio del usuario 4.6Ve los que los estudiantes dijeron

Programa

SEMANA 1

Week 1: Modeling Decisions in Low Uncertainty Settings

This module is designed to teach you how to analyze settings with low levels of uncertainty, and how to identify the best decisions in these settings. You'll explore the optimization toolkit, learn how to build an algebraic model using an advertising example, convert the algebraic model to a spreadsheet model, work with Solver to discover the best possible decision, and examine an example that introduces a simple representation of risk to the model. By the end of this module, you'll be able to build an optimization model, use Solver to uncover the optimal decision based on your data, and begin to adjust your model to account for simple elements of risk. These skills will give you the power to deal with large models as long as the actual uncertainty in the input values is not too high.

4 videos, 2 lecturas

Vídeo: 1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign
Vídeo: 1.2 Optimizing with Solver, and Alternative Data Inputs
Vídeo: 1.3 Adding Risk: Managing Investments at Epsilon Delta Capital
Reading: PDFs of Slides for Week 1
Reading: Excel Files for Week 1

Calificado: Week 1: Modeling in Low Uncertainty Quiz

SEMANA 2

Week 2: Risk and Reward: Modeling High Uncertainty Settings

What if uncertainty is the key feature of the setting you are trying to model? In this module, you'll learn how to create models for situations with a large number of variables. You'll examine high uncertainty settings, probability distributions, and risk, common scenarios for multiple random variables, how to incorporate risk reduction, how to calculate and interpret correlation values, and how to use scenarios for optimization, including sensitivity analysis and the efficient frontier. By the end of this module, you'll be able to identify and use common models of future uncertainty to build scenarios that help you optimize your business decisions when you have multiple variables and a higher degree of risk.

3 videos, 2 lecturas

Vídeo: 2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values
Vídeo: 2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier
Reading: PDFs of Lecture Slides for Week 2
Reading: Excel Files for Week 2

Calificado: Week 2: Modeling in High Uncertainty Quiz

SEMANA 3

Week 3: Choosing Distributions that Fit Your Data

When making business decisions, we often look to the past to make predictions for the future. In this module, you'll examine commonly used distributions of random variables to model the future and make predictions. You'll learn how to create meaningful data visualizations in Excel, how to choose the the right distribution for your data, explore the differences between discrete distributions and continuous distributions, and test your choice of model and your hypothesis for goodness of fit. By the end of this module, you'll be able to represent your data using graphs, choose the best distribution model for your data, and test your model and your hypothesis to see if they are the best fit for your data.

4 videos, 2 lecturas

Vídeo: 3.2, pt 1: Choosing Among Distributions: Discrete Distributions
Vídeo: 3.2, pt 2: Choosing Among Distributions: Continuous Distributions
Vídeo: 3.3 Hypothesis Testing and Goodness of Fit
Reading: PDFs of Lecture Slides for Week 3
Reading: Excel Files for Week 3

Calificado: Week 3: Choosing Fitting Distributions Quiz

SEMANA 4

Week 4: Balancing Risk and Reward Using Simulation

This module is designed to help you use simulations to enabling compare different alternatives when continuous distributions are used to describe uncertainty. Through an in-depth examination of the simulation toolkit, you'll learn how to make decisions in high uncertainty settings where random inputs are described by continuous probability distributions. You'll also learn how to run a simulation model, analyze simulation output, and compare alternative decisions to decide on the most optimal solution. By the end of this module, you'll be able to make decisions and manage risk using simulation, and more broadly, to make successful business decisions in an increasing complex and rapidly evolving business world.

4 videos, 2 lecturas

Vídeo: 4.2 Connecting Random Inputs and Random Outputs in a Simulation
Vídeo: 4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results
Vídeo: Course Conclusion
Reading: PDFs of Lecture Slides
Reading: Excel files for Week 4

Calificado: Week 4: Using Simulations Quiz

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?

¿Qué sucede si necesito más tiempo para completar el curso?

¿Qué recibiré si pago este curso?

¿Puedo tomar este curso de manera gratuita?

¿Cuál es la política de reembolsos?

¿Hay ayuda económica disponible?

Cómo funciona

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Creadores

University of Pennsylvania

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.

Tarifa

	Comprar curso
Accede a los materiales del curso	Disponible
Accede a los materiales con calificación	Disponible
Recibe una calificación final	Disponible
Obtén un Certificado de curso para compartir	Disponible

Calificaciones y revisiones

Calificado 4.6 de 5 1,140 calificaciones

Great course.

The course was what I expected it to be. I was desperately looking for enlightenment in model risk and realities which was well delivered in the course.

My only concern is that the assessments had very short mathematical exercises. I would have loved to dive deeper into the different types of distributions, and experimented with how different assumptions about the initial data would lead to different simulations. This WAS a part of the course, but maybe there will be a more advanced course that dives even deeper into that subject.