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There are 4 modules in this course
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.
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.
What's included
4 videos2 readings2 assignments
Show info about module content
4 videos•Total 60 minutes
Course Introduction•2 minutes
1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign•13 minutes
1.2 Optimizing with Solver, and Alternative Data Inputs•27 minutes
1.3 Adding Risk: Managing Investments at Epsilon Delta Capital•18 minutes
2 readings•Total 20 minutes
PDFs of Slides for Week 1•10 minutes
Excel Files for Week 1•10 minutes
2 assignments•Total 60 minutes
Week 1: Modeling in Low Uncertainty Quiz•30 minutes
Practice Quiz #1•30 minutes
Week 2: Risk and Reward: Modeling High Uncertainty Settings
Module 2•2 hours to complete
Module details
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.
What's included
3 videos2 readings2 assignments
Show info about module content
3 videos•Total 51 minutes
2.1 High Uncertainty Settings, Probability Distributions, Uncertainty and Risk•17 minutes
2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values•18 minutes
2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier•15 minutes
2 readings•Total 20 minutes
PDFs of Lecture Slides for Week 2•10 minutes
Excel Files for Week 2•10 minutes
2 assignments•Total 60 minutes
Week 2: Modeling in High Uncertainty Quiz•30 minutes
Practice Quiz #2•30 minutes
Week 3: Choosing Distributions that Fit Your Data
Module 3•3 hours to complete
Module details
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.
What's included
4 videos2 readings2 assignments
Show info about module content
4 videos•Total 81 minutes
3.1 Data and Visualization: Graphical Representation•22 minutes
3.2, pt 1: Choosing Among Distributions: Discrete Distributions•26 minutes
3.2, pt 2: Choosing Among Distributions: Continuous Distributions•11 minutes
3.3 Hypothesis Testing and Goodness of Fit•23 minutes
Week 4: Balancing Risk and Reward Using Simulation
Module 4•2 hours to complete
Module details
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.
What's included
4 videos2 readings2 assignments
Show info about module content
4 videos•Total 53 minutes
4.1: Modeling Uncertainty: From Scenarios to Continuous Distributions•19 minutes
4.2 Connecting Random Inputs and Random Outputs in a Simulation•23 minutes
4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results•11 minutes
Course Conclusion•0 minutes
2 readings•Total 20 minutes
PDFs of Lecture Slides•10 minutes
Excel files for Week 4•10 minutes
2 assignments•Total 60 minutes
Week 4: Using Simulations Quiz•30 minutes
Practice Quiz #4•30 minutes
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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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J
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5·
Reviewed on Nov 27, 2019
Tough course but challenging in a good way. This course challenges you to think differently and will improve your skillset in many areas including analysis, critical thinking, and numbers.
C
CO
4·
Reviewed on Feb 20, 2020
one of the best, as a data analyst this course will give you the necessary knowledge needed in business intelligence and financial modelling. The last week was very challenging but apt.
A
AC
5·
Reviewed on Sep 28, 2020
One of the most useful courses I've taken to date. This course is what the Spreadsheets and Models course should have been. Only complaint was week 3 was unnecessarily lengthy.
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What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
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