4.6
3,243 ratings
665 reviews

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 10 hours to complete

Suggested: 4 weeks of study, 1-3 hours/week...

#### English

Subtitles: English, Russian

### Skills you will gain

ModelingLinear RegressionProbabilistic ModelsRegression Analysis

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 10 hours to complete

Suggested: 4 weeks of study, 1-3 hours/week...

#### English

Subtitles: English, Russian

### Syllabus - What you will learn from this course

Week
1
2 hours to complete

## 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 videos (Total 72 min), 1 reading, 1 quiz
7 videos
1.2 Definition and Uses of Models, Common Functions14m
1.3 How Models Are Used in Practice10m
1.4 Key Steps in the Modeling Process7m
1.5 A Vocabulary for Modeling8m
1.6 Mathematical Functions20m
1.7 Summary4m
PDF of Lecture Slides10m
1 practice exercise
Module 1: Introduction to Models Quiz20m
Week
2
2 hours to complete

## 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 videos (Total 69 min), 1 reading, 1 quiz
6 videos
2.2 Growth in Discrete Time7m
2.3 Constant Proportionate Growth12m
2.4 Present and Future Value15m
2.5 Optimization13m
2.6 Summary2m
PDF of Lecture Slides10m
1 practice exercise
Module 2: Linear Models and Optimization Quiz20m
Week
3
2 hours to complete

## 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 videos (Total 83 min), 1 reading, 1 quiz
12 videos
3.2 Examples of Probabilistic Models2m
3.3 Regression Models4m
3.4 Probability Trees5m
3.5 Monte Carlo Simulations6m
3.6 Markov Chain Models6m
3.7 Building Blocks of Probability Models9m
3.8 The Bernoulli Distribution7m
3.9 The Binomial Distribution16m
3.10 The Normal Distribution5m
3.11 The Empirical Rule7m
3.12 Summary2m
PDF of Lecture Slides10m
1 practice exercise
Module 3: Probabilistic Models Quiz20m
Week
4
2 hours to complete

## 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 videos (Total 70 min), 1 reading, 1 quiz
8 videos
4.2 Use of Regression Models15m
4.3 Interpretation of Regression Coefficients4m
4.4 R-squared and Root Mean Squared Error (RMSE)12m
4.5 Fitting Curves to Data8m
4.6 Multiple Regression7m
4.7 Logistic Regression8m
4.8 Summary of Regression Models4m
PDF of Lecture Slides10m
1 practice exercise
Module 4: Regression Models Quiz20m
4.6
665 Reviews

## 35%

started a new career after completing these courses

## 41%

got a tangible career benefit from this course

## 11%

got a pay increase or promotion

### Top Reviews

By SCJun 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.

By NMJul 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.

## Instructor

### Richard Waterman

Professor of Statistics
Statistics-Wharton School

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. ...