Course Summary
Learn the fundamentals of quantitative modeling, including how to create models, and how to successfully interpret and communicate their results.Key Learning Points
- Gain skills in creating and interpreting quantitative models
- Learn how to use Excel and other tools for modeling
- Understand how to effectively communicate your modeling results
Related Topics for further study
Learning Outcomes
- Develop quantitative modeling skills
- Learn how to use Excel and other tools for modeling
- Effectively communicate your modeling results
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Excel
- Basic understanding of statistics and mathematics
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science Essentials
- Python for Data Science
Related Education Paths
Related Books
Description
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.
Outline
- Module 1: Introduction to Models
- 1.1 Course Introduction
- 1.2 Definition and Uses of Models, Common Functions
- 1.3 How Models Are Used in Practice
- 1.4 Key Steps in the Modeling Process
- 1.5 A Vocabulary for Modeling
- 1.6 Mathematical Functions
- 1.7 Summary
- PDF of Lecture Slides
- Module 1: Introduction to Models Quiz
- Module 2: Linear Models and Optimization
- 2.1 Introduction to Linear Models and Optimization
- 2.2 Growth in Discrete Time
- 2.3 Constant Proportionate Growth
- 2.4 Present and Future Value
- 2.5 Optimization
- 2.6 Summary
- PDF of Lecture Slides
- Module 2: Linear Models and Optimization Quiz
- Module 3: Probabilistic Models
- 3.1 Introduction to Probabilistic Models
- 3.2 Examples of Probabilistic Models
- 3.3 Regression Models
- 3.4 Probability Trees
- 3.5 Monte Carlo Simulations
- 3.6 Markov Chain Models
- 3.7 Building Blocks of Probability Models
- 3.8 The Bernoulli Distribution
- 3.9 The Binomial Distribution
- 3.10 The Normal Distribution
- 3.11 The Empirical Rule
- 3.12 Summary
- PDF of Lecture Slides
- Module 3: Probabilistic Models Quiz
- Module 4: Regression Models
- 4.1 Introduction to Regression Model
- 4.2 Use of Regression Models
- 4.3 Interpretation of Regression Coefficients
- 4.4 R-squared and Root Mean Squared Error (RMSE)
- 4.5 Fitting Curves to Data
- 4.6 Multiple Regression
- 4.7 Logistic Regression
- 4.8 Summary of Regression Models
- PDF of Lecture Slides
- Module 4: Regression Models Quiz
Summary of User Reviews
Coursera's Wharton Quantitative Modeling course is a well-structured, informative and engaging program that teaches students how to use mathematical models to analyze and solve real-world problems. Many users appreciated the practical application of the course content.Pros from User Reviews
- Practical application of course content
- Well-structured and informative program
- Engaging course content
Cons from User Reviews
- Some users found the course challenging
- Course may be too technical for beginners
- Some users felt that the course required more prior knowledge of statistics
Richard Waterman
4.6 Raiting
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