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3 Week 1 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. Video 1.1 Course Introduction Video 1.2 Definition and Uses of Models, Common Functions Video 1.3 How Models Are Used in Practice Video 1.4 Key Steps in the Modeling Process Video 1.5 A Vocabulary for Modeling Video 1.6 Mathematical Functions Video 1.7 Summary Quiz Module 1: Introduction to Models Quiz Reading PDF of Lecture Slidess

4 Week 2 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 Video 2.1 Introduction to Linear Models and Optimization Video 2.2 Growth in Discrete Time Video 2.3 Constant Proportionate Growth Video 2.4 Present and Future Value Video 2.5 Optimization Video 2.6 Summary Quiz Module 2: Linear Models and Optimization Quiz Reading PDF of Lecture Slides

5 Week 3 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. Video 3.1 Introduction to Probabilistic Models Video 3.2 Examples of Probabilistic Models Video 3.3 Regression Models Video 3.4 Probability Trees Video 3.5 Monte Carlo Simulations Video 3.6 Markov Chain Models Video 3.7 Building Blocks of Probability Models Video 3.8 The Bernoulli Distribution Video 3.9 The Binomial Distribution Video 3.10 The Normal Distribution Video 3.11 The Empirical Rule Video 3.12 Summary Quiz Module 3: Probabilistic Models Quiz Reading PDF of Lecture Slides

6 Week 4 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. Video 4.1 Introduction to Regression Model Video 4.2 Use of Regression Models Video 4.3 Interpretion of Regression Coefficients Video 4.4 R-squared and Root Mean Squared Error (RMSE) Video 4.5 Fitting Curves to Data Video 4.6 Multiple Regression Video 4.7 Logistic Regression Video 4.8 Summary of Regression Models Quiz Module 4: Regression Models Quiz Reading PDF of Lecture Slides

7 4 weeks of study, 1-3 hours/week English, Portuguese (Brazilian) About the Course The simple spreadsheet is one of the most powerful data analysis tools that exists, and it s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have know how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required.

9 Week 2 Addressing Uncertainty and Probability in Models This module was designed to introduce you to how you can use spreadsheets to address uncertainty and probability. You'll learn about random variables, probability distributions, power, exponential, and log functions in model formulas, models for calculating probability trees and decision trees, how to use regression tools to make predictions, as well as multiple regression. By the end of this module, you'll be able to measure correlations between variables using spreadsheet statistical functions, understand the results of functions that calculate correlations, use regression tools to make predictions, and improve forecasts with multiple regression. Video 3.0 Introduction Video 3.1 Random variables and probability distributions Video 3.2 Changes in discrete and continuous time Video 3.3 Power, exponential, and log functions Video 3.4 Probability trees and decision trees Video 3.5 Correlation and Regression Quiz Module 3 Quiz: Probability, Correlation, and Regression Reading PDF of Module 3 Lecture Slides Reading Module 3 examples Reading Additional reading on exponential and other functions

10 Week 3 Simulation and Optimization In this module, you'll learn to use spreadsheets to implement Monte Carlo simulations as well as linear programs for optimization. You'll examine the purpose of Monte Carlo simulations, how to implement Monte Carlo simulations in spreadsheets, the types of problems you can address with linear programs and how to implement those linear programs in spreadsheets. By the end of this module, you'll be able to model uncertainty and risk in spreadsheets, and use Excel's solver to optimize resources to reach a desired outcome. You'll also be able to identify the similarities and differences between Excel and Sheets, and be prepared for the next course in the Business and Financial Modeling Specialization. Video 4.0 Introduction Video 4.1 Monte Carlo Simulations Video 4.2 Linear Programming Video 4.3 Next Steps, and Differences between Excel and Sheets Quiz Module 4 Quiz: Simulations, Scenarios, and Optimization Reading PDF of Module 4 Lecture Slides Reading Module 4 examples Reading Links and other resources for further study

11 4 weeks of study, 1-3 hours/week English, Portuguese (Brazilian) About the 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.

12 Week 1 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. Video Course Introduction Video 1.1 How To Build an Optimization Model: Hudson Readers Ad Campaign Video 1.2 Optimizing with Solver, and Alternative Data Inputs Video 1.3 Adding Risk: Managing Investments at Epsilon Delta Capital Reading PDFs of Slides for Week 1 Reading Excel Files for Week 1 Quiz Week 1: Modeling in Low Uncertainty Quiz

13 Week 2 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. Video 2.1 High Uncertainty Settings, Probability Distributions, Uncertainty and Risk Video 2.2 Common Scenarios for Multiple Random Variables, Risk Reduction, and Calculating and Interpreting Correlation Values Video 2.3 Using Scenarios for Optimizing Under High Uncertainty, Sensitivity Analysis and Efficient Frontier Reading PDFs of Lecture Slides for Week 2 Reading Excel Files for Week 2 Quiz Week 2: Modeling in High Uncertainty Quiz

14 Week 3 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 Video 3.1 Data and Visualization: Graphical Representation Video 3.2, pt 1: Choosing Among Distributions: Discrete Distributions Video 3.2, pt 2: Choosing Among Distributions: Continuous Distributions Video 3.3 Hypothesis Testing and Goodness of Fit Reading PDFs of Lecture Slides for Week 3 Reading Excel Files for Week 3 Quiz Week 3: Choosing Fitting Distributions Quiz

15 Week 4 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. Video 4.1: Modeling Uncertainty: From Scenarios to Continuous Distributions Video 4.2 Connecting Random Inputs and Random Outputs in a Simulation Video 4.3 Analyzing and Interpreting Simulation Output: Evaluating Alternatives Using Simulation Results Video Course Conclusion Reading PDFs of Lecture Slides Reading Excel files for Week 4 Quiz Week 4: Using Simulations Quiz

16 4 weeks of study, 1-3 hours/week English About the Course This course is designed to show you how use quantitative models to transform data into better business decisions. You ll learn both how to use models to facilitate decision-making and also how to structure decision-making for optimum results. Two of Wharton s most acclaimed professors will show you the step-by-step processes of modeling common business and financial scenarios, so you can significantly improve your ability to structure complex problems and derive useful insights about alternatives. Once you ve created models of existing realities, possible risks, and alternative scenarios, you can determine the best solution for your business or enterprise, using the decision-making tools and techniques you ve learned in this course.

17 Week 1 Evaluation Criteria: Net Present Value This module was designed to introduce you to the many potential criteria for selecting investment projects, and to explore the most effective of these criteria: Net Present Value (NPV). Through the use of concrete examples, you'll learn the key components of Net Present Value, including the time value of money and the cost of capital, the main utility of NPV, and why it is ultimately more accurate and useful for evaluating projects than other commonly used criteria. By the end of this module, you'll be able to explain why net present value analysis is the appropriate criteria for choosing whether to accept or reject a project, and why other criteria, such as IRR, payback, ROI, etc. may not lead to decisions which maximize value. Video Course Introduction & Overview Video 1.1 Introduction: Criteria for Evaluating Projects Video 1.2 Time Value of Money Video 1.3 NPV Analysis of Projects Video 1.4 Other Evaluation Techniques Video 1.5 The Cost of Capital Quiz Evaluation Criteria: Module 1 Quiz Reading PDFs of Lecture Slides Reading Excel Spreadsheets: Module 1

18 Week 2 Evaluating Projects In this module, you'll learn how to evaluate a project with emphasis on analyzing the incremental after-tax cash flows associated with the project. You'll work through a concrete example using alternative scenarios to test the effectiveness of this method. You'll also learn why only future cash flows are relevant, why to ignore financial costs, include all incidental effects, remember working capital requirements, consider the effect of taxes, forget sunk costs, remember opportunity costs, use expected cash flows, and perform sensitivity analysis. By the end of this module, you'll be able to evaluate projects more thoroughly and effectively, with emphasis on how to model the change in the company s after-tax cash flows, so that you can make more profitable decisions. Video 2.1 Introduction and Analyzing Incremental After-Tax Cash Flows - Initial Investment Phase Video 2.2 Analyzing Incremental After-Tax Flows - Operating Phase Video 2.3 Analyzing Incremental After-Tax Flows - Terminal Phase Video 2.4 Example: New Production Machine Video 2.5 Key Considerations in Evaluations Quiz How to Evaluate Projects: Module 2 Quiz Reading PDFs of Lecture Slides

19 Week 3 Expressing Business Strategies in Financial Terms This module was designed to give you the opportunity to learn how business activities, transactions and events are translated into financial statements, including balance sheets, income statements, and cash flow statements. You'll also learn how these three statements are linked to each other, and how balance sheets and income statements can help forecast the future cash flow statements. By the end of this module, you'll be able to explain how accounting systems translate business activities into financial terms, and how to use this to better forecast future cash flows, so that you can express your business strategies in these financial terms, and show "the bottom line" for your proposed plan of action. Video 3.1 Introduction to Financial Statements Video 3.2 Balance Sheets and Income Statements Video 3.3 Cash Flow Statements Quiz Financial Statements and Forecasting: Module 3 Quiz Reading PDFs of Lecture Slides

20 Week 4 New Product Value In this module, you'll apply what you ve been learning to an analysis of a new product venture. You ll learn how to map out a plan of the business activities, transactions and events that need to happen to implement the new venture, including their timing. You'll also learn how to set up a spreadsheet to help with forecasts, and to re-calculate things automatically as we re-think our plans. You'll see how to forecast out the implied financial statements, and calculate the Net Present Value (NPV). By the end of this module, you'll be able to use spreadsheets to explore different risks a venture may face, and analyze the implications of these scenarios for NPV, so that you can make the most profitable, data-driven decision possible. Video 4.1 Introduction and Speadsheet Setup Video 4.2 Forecasting Future Cash Flows Video 4.3 NPV and IRR Calculations Video 4.4 Formulation and Evaluation of Alternative Scenarios Video 4.5 Expanding Beyond the Time Horizon Video Course Conclusion Quiz Calculating Value: Module 4 Quiz Reading PDFs of Lecture Slides Reading Excel Spreadsheets: Module 4

21 English About the Course In this Capstone you will recommend a business strategy based on a data model you ve constructed. Using a data set designed by Wharton Research Data Services (WRDS), you will implement quantitative models in spreadsheets to identify the best opportunities for success and minimizing risk. Using your newly acquired decision-making skills, you will structure a decision and present this course of action in a professional quality PowerPoint presentation which includes both data and data analysis from your quantitative models. Wharton Research Data Services (WRDS) is the leading data research platform and business intelligence tool for over 30,000 corporate, academic, government and nonprofit clients in 33 countries. WRDS provides the user with one location to access over 200 terabytes of data across multiple disciplines including Accounting, Banking, Economics, ESG, Finance, Insurance, Marketing, and Statistics.

22 Week 1 Getting Started Welcome! This opening module was designed to give you an overview of the Business and Financial Modeling Capstone, in which you will be working with historical financial data to calculate individual returns and summary statistics on those returns. The project has multiple steps, which are outlined below in the "Project Prompt", and culminates in a recommendation for portfolio allocation that you will prepare a presentation on. You will draw on elements from all courses to complete this project, and you can use your final presentation as a work sample to improve your current job or even find a new one. Before moving on, complete the "Project Scope Quiz." The work you do this week enables you to understand the steps needed to successfully complete your final project. Reading Project Description - Read me first! Reading Project Prompt Reading Historical Stock Data Quiz Project Scope Quiz Other Module 1 Discussion: Introductions Other Questions about the Project

26 Week 5 Step 5: Creating Your Asset Allocation & Final Presentation In this final module you are asked to move beyond a stock-only portfolio to one utilizing more diversified assets and to prepare a short presentation summarizing your findings. As explained in Step 5 of the Project Prompt, you have \$5 million to invest in the Vanguard Total Bond Market Index Fund (ticker: VBTLX) and Vanguard 500 Index (ticker: VFIAX) investment vehicles. There are two assessments in this module. First, you'll complete a short quiz on the characteristics of your optimal risky portfolio. Then, in the peer review component of this Capstone, you are tasked with preparing a short presentation that (i) explores how your portfolio of mixed asset class of funds compares to a single security (AAPL) and (ii) uses that comparison to discuss the importance of portfolio diversification. Reading VBTLX and VFIAX Monthly Returns Quiz Working with a Diversified Portfolio Other Module 5 Discussion - Reflect on your experience and share your insights Peer Review Portfolio Performance Presentation Other How did you create a Mixed Asset Portfolio? Other Arguments for and against mixed asset portfolios

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