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Michel Bierlaire
Human behavior is complexand unpredictable. Or is it?
The focus of this course is methods for predicting the behavior using mathematical models. More specifically, we'll explore choice modeling in order to obtain disaggregate demand models.
Focusing on the logit model, the course covers the specification of the model, the estimation of its parameters, the validation process, andits application. Exercises using the software Biogeme are also part of its makeup. The course relies on concrete case studies with real data sets, which are provided in course content.
Register for this course and see more details by visiting:
OpenCourser.com/course/wvh6za/introduction
What's inside
Learning objectives
- The behavioral assumptions associated with disaggregate choice models
- The derivation of operational models
- The art of model specification
- The estimation of model parameters from choice data
- The testing of model specifications
- Concrete applications of the estimated models
- The behavioral assumptions associated with disaggregate choice models
- The derivation of operational models
- The art of model specification
- The estimation of model parameters from choice data
- The testing of model specifications
- Concrete applications of the estimated models
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces learners to fundamental concepts in disaggregate choice models using the logit model
Incorporates real-world case studies for a better understanding of the practical applications of disaggregate choice models
Teaches the art of model specification, an important skill for accurately representing real-world scenarios
Covers the entire process of model development, from specification to validation
Provides hands-on experience through exercises using the Biogeme software
Recommended for learners with a background in statistics and econometrics
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Reviews summary
Essential discrete choice models for professionals
According to students, Introduction to Discrete Choice Models by EPFL is a highly valuable and well-structured course that provides a strong theoretical foundation in discrete choice modeling, particularly the logit model. Learners appreciate the concrete case studies with real datasets and the hands-on exercises using Biogeme, finding them practical for application. While many praise the clarity of the instructor's explanations, a notable minority mention the course can be challenging for those without a strong mathematical background and sometimes moves at a fast pace. It's considered ideal for researchers and professionals in quantitative fields.
Course clarity and structure appear to have improved over time.
"The instructor explains complex concepts with great clarity, and the structure is logical, indicating improvement over earlier versions."
"I found the material very well-structured and easy to follow, suggesting the course has been refined based on feedback."
"While some older reviews mentioned lack of clarity, I found the recent course content to be very clear and well-presented."
Instructor explains complex topics with clarity and organizes content well.
"The instructor explains complex concepts with great clarity, making it easier to grasp."
"The instructor's lectures were clear and well-organized, which made following the content straightforward."
"I found the course structure logical, building from basics to more complex topics seamlessly."
Ideal for researchers and professionals in quantitative fields.
"This course was perfect for my needs as an urban planner, highly relevant to my field."
"I would highly recommend this course for anyone in transport planning or economics looking for practical skills."
"It's excellent for those with a strong quantitative background, providing tools I can use immediately in research."
Provides practical skills through real datasets and Biogeme software.
"The use of real-world datasets is incredibly helpful. Biogeme exercises were challenging but highly practical."
"This course demystifies discrete choice models and provides the tools (Biogeme) to apply them. The exercises with real data were a highlight."
"I found the 'testing' and 'forecasting' modules particularly useful for real-world application, directly applicable to my work."
Offers a robust theoretical foundation for discrete choice models.
"This course provided a fantastic foundation. As a researcher, I found the detailed explanation of logit models and model specification invaluable."
"The instructor explains complex concepts with great clarity, and it helped me understand discrete choice models."
"I gained a strong theoretical foundation from this course, which was exactly what I needed."
Initial setup of Biogeme software can be challenging for some.
"Getting Biogeme set up initially took some troubleshooting on my end."
"Biogeme was new to me, and I felt the instructions could be more detailed for absolute beginners."
"I struggled a bit with the initial Biogeme setup, but once past that, the exercises were very beneficial."
Requires a strong mathematical background and can be fast-paced.
"It assumes a much higher level of mathematical background than I anticipated for an 'introduction'. I struggled with the derivations."
"Some parts felt a bit rushed, particularly if I'm not strong in advanced calculus."
"If you don't have a strong math background, be prepared to struggle immensely, as I did."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Discrete Choice Models with these
activities:
Review notes and assignments from previous courses related to econometrics and statistics
Show steps
Strengthens foundational knowledge necessary for understanding choice modeling.
Browse courses on
Econometrics
Show steps
-
Gather notes and assignments from previous courses.
-
Review the materials, focusing on key concepts and techniques.
-
Take practice problems or quizzes to test your understanding.
Review 'Advanced Choice Modeling' by Greene and Hensher
Show steps
Provides a theoretical and empirical grounding for understanding advanced concepts in choice modeling.
View
Applied Choice Analysis
on Amazon
Show steps
-
Familiarize yourself with the basic concepts of choice modeling.
-
Read chapters 2-4 to understand the different types of choice models and their applications.
-
Read chapters 5-7 to learn about the estimation and interpretation of choice models.
-
Work through the exercises at the end of each chapter to test your understanding.
Solve practice problems on choice modeling
Show steps
Provides hands-on practice in applying choice modeling techniques.
Show steps
-
Find online practice problems or textbooks with exercises.
-
Attempt to solve the problems on your own.
-
Check your answers against the provided solutions.
-
Repeat the process until you are comfortable with the material.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a discussion forum or study group
Show steps
Provides a platform for discussing concepts, asking questions, and collaborating with peers.
Show steps
-
Join a discussion forum or study group related to choice modeling.
-
Read and participate in discussions.
-
Share your own ideas and insights.
-
Ask questions and seek clarifications.
Follow online tutorials on choice modeling software (e.g., Biogeme, R, Python)
Show steps
Provides hands-on experience with software tools used in choice modeling.
Show steps
-
Identify relevant online tutorials.
-
Follow the tutorials step-by-step.
-
Complete the exercises and assignments.
-
Apply your newfound skills to your own projects.
Mentor other students in the course
Show steps
Reinforces your own understanding while helping others learn.
Show steps
-
Identify opportunities to assist other students.
-
Offer your help and guidance.
-
Answer their questions and provide feedback.
-
Collaborate on projects or assignments.
Create a presentation on a specific application of choice modeling
Show steps
Provides an opportunity to synthesize knowledge and communicate complex concepts clearly.
Show steps
-
Choose a specific application of choice modeling that interests you.
-
Research the topic thoroughly.
-
Create a presentation that explains the application, the choice model used, and the results.
-
Present your work to your classmates or a group of experts.
-
Get feedback and make revisions.
Develop a predictive model for a real-world problem using choice modeling techniques
Show steps
Provides practical experience in applying choice modeling to solve real-world problems.
Show steps
-
Identify a real-world problem that can be addressed using choice modeling.
-
Collect data on the relevant variables.
-
Estimate a choice model using the data.
-
Validate the model and make predictions.
-
Write a report summarizing your findings.
Review notes and assignments from previous courses related to econometrics and statistics
Show steps
Strengthens foundational knowledge necessary for understanding choice modeling.
Browse courses on
Econometrics
Show steps
Show steps
Show steps
- Gather notes and assignments from previous courses.
- Review the materials, focusing on key concepts and techniques.
- Take practice problems or quizzes to test your understanding.
Review 'Advanced Choice Modeling' by Greene and Hensher
Show steps
Provides a theoretical and empirical grounding for understanding advanced concepts in choice modeling.
View
Applied Choice Analysis
on Amazon
Show steps
Show steps
Show steps
- Familiarize yourself with the basic concepts of choice modeling.
- Read chapters 2-4 to understand the different types of choice models and their applications.
- Read chapters 5-7 to learn about the estimation and interpretation of choice models.
- Work through the exercises at the end of each chapter to test your understanding.
Solve practice problems on choice modeling
Show steps
Provides hands-on practice in applying choice modeling techniques.
Show steps
Show steps
Show steps
- Find online practice problems or textbooks with exercises.
- Attempt to solve the problems on your own.
- Check your answers against the provided solutions.
- Repeat the process until you are comfortable with the material.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a discussion forum or study group
Show steps
Provides a platform for discussing concepts, asking questions, and collaborating with peers.
Show steps
Show steps
Show steps
- Join a discussion forum or study group related to choice modeling.
- Read and participate in discussions.
- Share your own ideas and insights.
- Ask questions and seek clarifications.
Follow online tutorials on choice modeling software (e.g., Biogeme, R, Python)
Show steps
Provides hands-on experience with software tools used in choice modeling.
Show steps
Show steps
Show steps
- Identify relevant online tutorials.
- Follow the tutorials step-by-step.
- Complete the exercises and assignments.
- Apply your newfound skills to your own projects.
Mentor other students in the course
Show steps
Reinforces your own understanding while helping others learn.
Show steps
Show steps
Show steps
- Identify opportunities to assist other students.
- Offer your help and guidance.
- Answer their questions and provide feedback.
- Collaborate on projects or assignments.
Create a presentation on a specific application of choice modeling
Show steps
Provides an opportunity to synthesize knowledge and communicate complex concepts clearly.
Show steps
Show steps
Show steps
- Choose a specific application of choice modeling that interests you.
- Research the topic thoroughly.
- Create a presentation that explains the application, the choice model used, and the results.
- Present your work to your classmates or a group of experts.
- Get feedback and make revisions.
Develop a predictive model for a real-world problem using choice modeling techniques
Show steps
Provides practical experience in applying choice modeling to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using choice modeling.
- Collect data on the relevant variables.
- Estimate a choice model using the data.
- Validate the model and make predictions.
- Write a report summarizing your findings.
Career center
Learners who complete Introduction to Discrete Choice Models will develop knowledge and skills
that may be useful to these careers:
Quantitative Market Researcher
Quantitative Market Researcher
If you are interested in a career as a Quantitative Market Researcher, taking Introduction to Discrete Choice Models could assist you in your search. Those that choose this career help companies learn more about their consumer base, which includes understanding their behavior. A main takeaway of this course is teaching students how to implement and analyze discrete choice models. This skill will directly translate to the work that you would do in this role.
Marketing Manager
Marketing Manager
Predicting human behavior is an essential skill in marketing, particularly for Marketing Managers. Those who work in this role must consider how to reach their desired target audience. Introduction to Discrete Choice Models can help teach you how to assess consumer preferences and behavior as you would in this career. Students of this course are taught how to analyze data to accurately predict how consumers behave, allowing them to develop better marketing strategies.
Data Analyst
Data Analyst
Much of the work of a Data Analyst involves using a variety of models to make predictions, solve problems, and support decision making. Introduction to Discrete Choice Models may help build your foundational knowledge. As a student of this course, you will be taught how to predict human behavior. This is a fundamental part of data analysis, as analysts are often expected to derive actionable insights and trends from raw data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts utilize mathematical and analytical models either to solve business problems or to improve organizational efficiency. Those in this career field must be able to use their knowledge to build models, analyze data, and make recommendations. Introduction to Discrete Choice Models can help build your foundation for this type of work, as you will learn how to construct and estimate models that accurately predict behavior.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to analyze an organization's performance and identify areas for improvement. In this career, you will need to use your knowledge of data and modeling to inform decision making. Introduction to Discrete Choice Models can provide you with the skills to build and implement models, which you will need to do in this line of work.
Risk Analyst
Risk Analyst
Risk Analysts need to be able to identify, assess, and manage risk. To do so, they must be able to anticipate potential problems and assess the likelihood and impact of those problems. Introduction to Discrete Choice Models may be useful for this career, as it teaches students how to use models to predict human behavior. This skill can be applied to risk management, as it can help you predict the likelihood of certain events occurring.
Transportation Planner
Transportation Planner
Transportation Planners are responsible for planning and managing transportation systems. They need to be able to understand and predict how people will use transportation systems so that they can design systems that are efficient and meet the needs of the public. Introduction to Discrete Choice Models could be useful for those in this role, as it teaches students how to predict human behavior and derive operational models.
UX Researcher
UX Researcher
UX Researchers study how users interact with products and services. They use this information to design products and services that are easy to use and meet the needs of users. Introduction to Discrete Choice Models may be of some use in this career, as it teaches students how to predict human behavior. This skill can be applied to UX research, as it can help you predict how users will interact with a product or service.
Survey Researcher
Survey Researcher
Survey Researchers design, administer, and analyze surveys. They use this information to gather data about the opinions, attitudes, and behaviors of people. Introduction to Discrete Choice Models may be useful for those in this role, as it can help you design better surveys and analyze the data you collect.
Demographer
Demographer
Demographers study the size, composition, and distribution of human populations. They use this information to understand population trends and make predictions about the future. Introduction to Discrete Choice Models may be useful for those in this role, as it can help you understand how people make decisions and how those decisions affect population trends.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and causes of disease in human populations. They use this information to develop and implement public health interventions. Introduction to Discrete Choice Models may be useful for those in this role, as it can help you understand how people make decisions about their health and how those decisions affect the spread of disease.
Public Policy Analyst
Public Policy Analyst
Public Policy Analysts study the effects of public policies. They use this information to make recommendations about how to improve public policy. Introduction to Discrete Choice Models may be useful for those in this role, as it can help you understand how people make decisions and how those decisions are affected by public policy.
Econometrician
Econometrician
Econometricians use mathematical and statistical methods to analyze economic data. They use this information to test economic theories and make predictions about the future. Introduction to Discrete Choice Models may be of some use, as it covers topics such as the derivation of operational models and the estimation of model parameters from choice data.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use this information to help businesses and organizations make informed decisions. Introduction to Discrete Choice Models may be useful for those in this role, as it covers topics such as the testing of model specifications and the use of statistical software.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. They use this information to help insurance companies and other organizations make informed decisions about risk management. Introduction to Discrete Choice Models may be of use, as it teaches students how to use models to predict human behavior. This skill can be applied to actuarial science, as it can help actuaries predict the likelihood and impact of certain events.
For more career information including salaries, visit:
OpenCourser.com/course/wvh6za/introduction
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Discrete Choice Models.
Comprehensive and up-to-date treatment of discrete choice analysis, a powerful technique for modeling and predicting human behavior. It provides a thorough grounding in the theoretical foundations of the method, as well as detailed guidance on its practical application.
Provides a comprehensive overview of discrete choice models, with a focus on simulation methods. It valuable reference for researchers and practitioners who want to learn more about the theory and application of discrete choice models.
Save
Specifically focusing on the application of discrete choice models in transportation, this book provides real-world examples and case studies to illustrate its practical benefits.
Provides a clear and concise introduction to the theory and application of choice analysis. It covers a wide range of topics, including binary choice models, multinomial choice models, and mixed logit models. The book is written in a non-technical style and is suitable for readers with a basic understanding of statistics.
Extending the scope of discrete choice modeling, this book explores applications in marketing, healthcare, and environmental economics.
Provides a comprehensive overview of the theory and application of choice models for travel demand analysis. It covers a wide range of topics, including mode choice models, destination choice models, and route choice models. The book is written in a clear and concise style and is suitable for readers with a basic understanding of statistics.
Provides a comprehensive overview of survey data analysis, with a focus on discrete choice models. It valuable resource for researchers and practitioners who want to learn more about the analysis of survey data.
As a foundational work in the field, this book outlines the structural approach to discrete choice models, offering a rigorous theoretical framework for further exploration.
Provides a comprehensive overview of econometric analysis of discrete choice, with a focus on theoretical and empirical applications. It valuable resource for researchers and practitioners who want to learn more about the econometric analysis of discrete choice.
Save
Providing a clear and accessible introduction to logistic regression, this book equips readers with the essential skills for building and interpreting discrete choice models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wvh6za/introduction
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Effort
5 to 6 hours per week for 7 weeks
Level
Intermediate
Via
edX
Institution
École Polytechnique Fédérale de Lausanne
Instructor
Michel Bierlaire
Language
English
Transcripts
English
Français
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