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.
Ce cours fait partie de la Spécialisation Modélisation commerciale et financière
Fundamentals of Quantitative Modeling
proposé par
À propos de ce cours
4.6
3,631 notes
Compétences que vous acquerrez
ModelingLinear RegressionProbabilistic ModelsRegression Analysis
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
12 heures pour terminer
Module 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 vidéos (Total 72 min), 1 lecture, 1 quiz
7 vidéos
1.2 Definition and Uses of Models, Common Functions14 min
1.3 How Models Are Used in Practice10 min
1.4 Key Steps in the Modeling Process7 min
1.5 A Vocabulary for Modeling8 min
1.6 Mathematical Functions20 min
1.7 Summary4 min
1 lectures
PDF of Lecture Slides10 min
1 exercices pour s'entraîner
Module 1: Introduction to Models Quiz20 min
Semaine
22 heures pour terminer
Module 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 vidéos (Total 69 min), 1 lecture, 1 quiz
6 vidéos
2.2 Growth in Discrete Time7 min
2.3 Constant Proportionate Growth12 min
2.4 Present and Future Value15 min
2.5 Optimization13 min
2.6 Summary2 min
1 lectures
PDF of Lecture Slides10 min
1 exercices pour s'entraîner
Module 2: Linear Models and Optimization Quiz20 min
Semaine
32 heures pour terminer
Module 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 vidéos (Total 83 min), 1 lecture, 1 quiz
12 vidéos
3.2 Examples of Probabilistic Models2 min
3.3 Regression Models4 min
3.4 Probability Trees5 min
3.5 Monte Carlo Simulations6 min
3.6 Markov Chain Models6 min
3.7 Building Blocks of Probability Models9 min
3.8 The Bernoulli Distribution7 min
3.9 The Binomial Distribution16 min
3.10 The Normal Distribution5 min
3.11 The Empirical Rule7 min
3.12 Summary2 min
1 lectures
PDF of Lecture Slides10 min
1 exercices pour s'entraîner
Module 3: Probabilistic Models Quiz20 min
Semaine
42 heures pour terminer
Module 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 vidéos (Total 70 min), 1 lecture, 1 quiz
8 vidéos
4.2 Use of Regression Models15 min
4.3 Interpretation of Regression Coefficients4 min
4.4 R-squared and Root Mean Squared Error (RMSE)12 min
4.5 Fitting Curves to Data8 min
4.6 Multiple Regression7 min
4.7 Logistic Regression8 min
4.8 Summary of Regression Models4 min
1 lectures
PDF of Lecture Slides10 min
1 exercices pour s'entraîner
Module 4: Regression Models Quiz20 min
4.6
725 avis31%
a commencé une nouvelle carrière après avoir terminé ces cours
36%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
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.
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.
Enseignants
Richard Waterman
Professor of StatisticsStatistics-Wharton School
À propos de Université de Pennsylvanie
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.
À propos de la Spécialisation Modélisation commerciale et financière
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.
Foire Aux Questions
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