How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization.
Этот курс входит в специализацию ''Специализация Business and Financial Modeling'
Fundamentals of Quantitative Modeling
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Об этом курсе
4.6
Оценки: 3,333
Приобретаемые навыки
ModelingLinear RegressionProbabilistic ModelsRegression Analysis
Программа курса: что вы изучите
Неделя
1Module 1: Introduction to Models
In this module, you will learn how to define a model, and how models are commonly used. You’ll examine the central steps in the modeling process, the four key mathematical functions used in models, and the essential vocabulary used to describe models. By the end of this module, you’ll be able to identify the four most common types of models, and how and when they should be used. You’ll also be able to define and correctly use the key terms of modeling, giving you not only a foundation for further study, but also the ability to ask questions and participate in conversations about quantitative models.
7 видео ((всего 72 мин.)), 1 материал для самостоятельного изучения, 1 тест
7 видео
1.2 Definition and Uses of Models, Common Functions14мин
1.3 How Models Are Used in Practice10мин
1.4 Key Steps in the Modeling Process7мин
1.5 A Vocabulary for Modeling8мин
1.6 Mathematical Functions20мин
1.7 Summary4мин
1 материал для самостоятельного изучения
PDF of Lecture Slides10мин
1 практическое упражнение
Module 1: Introduction to Models Quiz20мин
Неделя
2Module 2: Linear Models and Optimization
This module introduces linear models, the building block for almost all modeling. Through close examination of the common uses together with examples of linear models, you’ll learn how to apply linear models, including cost functions and production functions to your business. The module also includes a presentation of growth and decay processes in discrete time, growth and decay in continuous time, together with their associated present and future value calculations. Classical optimization techniques are discussed. By the end of this module, you’ll be able to identify and understand the key structure of linear models, and suggest when and how to use them to improve outcomes for your business. You’ll also be able to perform present value calculations that are foundational to valuation metrics. In addition, you will understand how you can leverage models for your business, through the use of optimization to really fine tune and optimize your business functions.
6 видео ((всего 69 мин.)), 1 материал для самостоятельного изучения, 1 тест
6 видео
2.2 Growth in Discrete Time7мин
2.3 Constant Proportionate Growth12мин
2.4 Present and Future Value15мин
2.5 Optimization13мин
2.6 Summary2мин
1 материал для самостоятельного изучения
PDF of Lecture Slides10мин
1 практическое упражнение
Module 2: Linear Models and Optimization Quiz20мин
Неделя
3Module 3: Probabilistic Models
This module explains probabilistic models, which are ways of capturing risk in process. You’ll need to use probabilistic models when you don’t know all of your inputs. You’ll examine how probabilistic models incorporate uncertainty, and how that uncertainty continues through to the outputs of the model. You’ll also discover how propagating uncertainty allows you to determine a range of values for forecasting. You’ll learn the most-widely used models for risk, including regression models, tree-based models, Monte Carlo simulations, and Markov chains, as well as the building blocks of these probabilistic models, such as random variables, probability distributions, Bernoulli random variables, binomial random variables, the empirical rule, and perhaps the most important of all of the statistical distributions, the normal distribution, characterized by mean and standard deviation. By the end of this module, you’ll be able to define a probabilistic model, identify and understand the most commonly used probabilistic models, know the components of those models, and determine the most useful probabilistic models for capturing and exploring risk in your own business.
12 видео ((всего 83 мин.)), 1 материал для самостоятельного изучения, 1 тест
12 видео
3.2 Examples of Probabilistic Models2мин
3.3 Regression Models4мин
3.4 Probability Trees5мин
3.5 Monte Carlo Simulations6мин
3.6 Markov Chain Models6мин
3.7 Building Blocks of Probability Models9мин
3.8 The Bernoulli Distribution7мин
3.9 The Binomial Distribution16мин
3.10 The Normal Distribution5мин
3.11 The Empirical Rule7мин
3.12 Summary2мин
1 материал для самостоятельного изучения
PDF of Lecture Slides10мин
1 практическое упражнение
Module 3: Probabilistic Models Quiz20мин
Неделя
4Module 4: Regression Models
This module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You’ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You’ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You’ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you’ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them so that you can discuss your model and convince others that your model makes sense, with the ultimate goal of implementation.
8 видео ((всего 70 мин.)), 1 материал для самостоятельного изучения, 1 тест
8 видео
4.2 Use of Regression Models15мин
4.3 Interpretation of Regression Coefficients4мин
4.4 R-squared and Root Mean Squared Error (RMSE)12мин
4.5 Fitting Curves to Data8мин
4.6 Multiple Regression7мин
4.7 Logistic Regression8мин
4.8 Summary of Regression Models4мин
1 материал для самостоятельного изучения
PDF of Lecture Slides10мин
1 практическое упражнение
Module 4: Regression Models Quiz20мин
33%
начал новую карьеру, пройдя эти курсы
38%
получил значимые преимущества в карьере благодаря этому курсу
14%
стал больше зарабатывать или получил повышение
Лучшие рецензии
автор: SC•Jun 4th 2018
Course is having ultimate content regarding the understanding of Quantitative modeling and its applications. Having great explanation with examples of linear, power, exponential and log functions.
автор: NM•Jul 23rd 2017
Very good background to quantitative modelling. It gets a bit heavy on the mathematical formulas in places, but if you follow through, it helps cement understanding. Good speed/pace of material.
Преподаватель
Richard Waterman
Professor of StatisticsStatistics-Wharton School
О 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.
О специализации ''Business and Financial Modeling'
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.
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