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.
이 강좌는 Business and Financial Modeling 전문 분야의 일부입니다.
Modeling Risk and Realities
제공자:
28,937명이 이미 등록했습니다!
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 2시간 필요
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 (Total 60 min), 2 readings, 1 quiz
4개의 동영상
1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign13m
1.2 Optimizing with Solver, and Alternative Data Inputs26m
1.3 Adding Risk: Managing Investments at Epsilon Delta Capital18m
2개의 읽기 자료
PDFs of Slides for Week 110m
Excel Files for Week 110m
1개 연습문제
Week 1: Modeling in Low Uncertainty Quiz20m
주
2완료하는 데 2시간 필요
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 (Total 51 min), 2 readings, 1 quiz
3개의 동영상
2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values18m
2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier15m
2개의 읽기 자료
PDFs of Lecture Slides for Week 210m
Excel Files for Week 210m
1개 연습문제
Week 2: Modeling in High Uncertainty Quiz20m
주
3완료하는 데 2시간 필요
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 (Total 81 min), 2 readings, 1 quiz
4개의 동영상
3.2, pt 1: Choosing Among Distributions: Discrete Distributions25m
3.2, pt 2: Choosing Among Distributions: Continuous Distributions11m
3.3 Hypothesis Testing and Goodness of Fit22m
2개의 읽기 자료
PDFs of Lecture Slides for Week 310m
Excel Files for Week 310m
1개 연습문제
Week 3: Choosing Fitting Distributions Quiz20m
주
4완료하는 데 2시간 필요
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 (Total 53 min), 2 readings, 1 quiz
4개의 동영상
4.2 Connecting Random Inputs and Random Outputs in a Simulation23m
4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results10m
Course Conclusion25
2개의 읽기 자료
PDFs of Lecture Slides10m
Excel files for Week 410m
1개 연습문제
Week 4: Using Simulations Quiz20m
4.6
186개의 리뷰29%
이 강좌를 수료한 후 새로운 경력 시작하기
24%
이 강좌를 통해 확실한 경력상 이점 얻기
최상위 리뷰
covers good amount of material and exactly what is in the outline, presented with enough detail to follow. Good walk-through of the spreadsheets helps understanding, easy to follow along and practice.
Material was very well presented. Week 3 was challenging, but taking time to print out the slides, work through them rigorously proved very helpful. I found all sections very, very informative.
강사
Sergei Savin
Associate Professor of Operations, Information and DecisionsThe Wharton School
Senthil Veeraraghavan
Associate Professor of Operations, Information and DecisionsThe Wharton School
펜실베이니아 대학교 정보
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.
Business and Financial Modeling 전문 분야 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
Coursera는 세계 최고의 교육에 대한 보편적인 접근 기회를 제공하며,
최고의 대학 및 기관들과 제휴하여 온라인 강좌를 제공합니다.
© 2019 Coursera Inc. All rights reserved.
