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.
제공자:
About this Course
학습자 경력 결과
44%
32%
귀하가 습득할 기술
학습자 경력 결과
44%
32%
공유 가능한 수료증
100% 온라인
다음 전문 분야의 5개 강좌 중 3번째 강좌:
유동적 마감일
완료하는 데 약 9시간 필요
영어
강의 계획 - 이 강좌에서 배울 내용
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.
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.
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.
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.
검토
4.6
MODELING RISK AND REALITIES의 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.
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.
Awesome couse for learning applications of statistics in real world problems. They show how can we optimize the results using the most common tool among all business environments: excel.
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.
This course is very informative and maintains a good pace for the learner. Although it might not be challenging enough. I still recommend it to anyone wanting to learn this material.
Really great course! Learnt a lot, more than promised by the course description. The course instructors are really good and very articulate. Thoroughly pleased with the course!
Great examples, the best course of the specialization so far.\n\nWeek 3 was a little bit slow. I think it was going through some theory that was already covered by course 1.
펜실베이니아 대학교 정보
Business and Financial Modeling 전문 분야 정보
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.