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Jizhou Kang
This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.
Register for this course and see more details by visiting:
OpenCourser.com/course/009oxr/bayesian
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What's inside
Syllabus
Bayesian Conjugate Analysis for Autogressive Time Series Models
In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
Read more
Syllabus
Bayesian Conjugate Analysis for Autogressive Time Series Models
In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
Read more
Model Selection Criteria
In this module, we will introduce some criteria that can be used in selecting the order of AR processes and the number of mixing components, which will be used later when we introduce mixture of AR models.
Bayesian location mixture of AR(P) model
In this module, we will perform Bayesian analysis for location mixture of AR(p) models.
Peer-reviewed data analysis project
In this module, we will use everything we have learned up until now to perform a mixture model on time series data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Bayesian Conjugate Analysis for Autogressive Time Series Models, which is standard in statistical modeling
Examines Model Selection Criteria, which helps learners select the order of AR processes and the number of mixing components in mixture of AR models
Develops Bayesian location mixture of AR(P) model, which helps learners analyze more complex time series data
Taught by Jizhou Kang, who is recognized for their work in Bayesian statistics
Offers hands-on labs and interactive materials, which allows learners to apply their knowledge practically
Requires learners to have a strong foundation in Bayesian statistics, which may be a barrier for some
Save this course
Save Bayesian Statistics: Capstone Project to your list so you can find it easily later:
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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 Bayesian Statistics: Capstone Project with these
activities:
Follow online tutorials on Bayesian statistics
Show steps
Supplement your learning with online tutorials on Bayesian statistics.
Show steps
-
Find an online tutorial on Bayesian statistics.
-
Follow the tutorial carefully.
-
Complete the exercises at the end of the tutorial.
Review conditional probability and Bayes' theorem
Show steps
Review these concepts to prepare for the course's coverage of Bayesian statistics.
Browse courses on
Conditional Probability
Show steps
-
Review the definition and properties of conditional probability.
-
Review Bayes' theorem and its applications.
Create a compilation of Bayesian statistics resources
Show steps
Create a compilation of Bayesian statistics resources to help you study for the course.
Show steps
-
Share your compilation with other students.
-
Gather a variety of Bayesian statistics resources.
-
Organize the resources into a coherent collection.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read Bayesian Data Analysis by Andrew Gelman
Show steps
This is the key textbook for the course and will provide a deep understanding of Bayesian statistics.
View
Bayesian Data Analysis (Chapman & Hall/CRC...
on Amazon
Show steps
-
Read the book thoroughly.
-
Take notes on the key concepts.
-
Complete the exercises at the end of each chapter.
Solve Bayesian inference problems
Show steps
Practice applying Bayesian inference to real-world problems.
Show steps
-
Find a dataset that you are interested in.
-
Formulate a Bayesian model for the data.
-
Use Bayesian inference to estimate the parameters of the model.
-
Evaluate the performance of your model.
Volunteer at a local data science organization
Show steps
Gain practical experience in Bayesian statistics by volunteering at a local data science organization.
Show steps
-
Find a local data science organization that you can volunteer for.
-
Contact the organization and express your interest in volunteering.
-
Attend volunteer training.
-
Volunteer your time to help the organization with data science projects.
Mentor other students in Bayesian statistics
Show steps
Help other students learn Bayesian statistics by mentoring them.
Show steps
-
Find a student who is interested in learning Bayesian statistics.
-
Meet with the student regularly to answer their questions and provide guidance.
-
Help the student develop their skills in Bayesian statistics.
Create a presentation on Bayesian statistics
Show steps
Create a presentation to demonstrate your understanding of Bayesian statistics.
Show steps
-
Choose a topic related to Bayesian statistics.
-
Research the topic and gather information.
-
Create a presentation that explains the topic clearly.
-
Present your presentation to a group of peers.
Participate in a Bayesian statistics competition
Show steps
Test your skills and knowledge by competing in a Bayesian statistics competition.
Show steps
-
Find a Bayesian statistics competition.
-
Register for the competition.
-
Complete the competition challenges.
-
Submit your results.
Follow online tutorials on Bayesian statistics
Show steps
Supplement your learning with online tutorials on Bayesian statistics.
Show steps
Show steps
Show steps
- Find an online tutorial on Bayesian statistics.
- Follow the tutorial carefully.
- Complete the exercises at the end of the tutorial.
Review conditional probability and Bayes' theorem
Show steps
Review these concepts to prepare for the course's coverage of Bayesian statistics.
Browse courses on
Conditional Probability
Show steps
Show steps
Show steps
- Review the definition and properties of conditional probability.
- Review Bayes' theorem and its applications.
Create a compilation of Bayesian statistics resources
Show steps
Create a compilation of Bayesian statistics resources to help you study for the course.
Show steps
Show steps
Show steps
- Share your compilation with other students.
- Gather a variety of Bayesian statistics resources.
- Organize the resources into a coherent collection.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read Bayesian Data Analysis by Andrew Gelman
Show steps
This is the key textbook for the course and will provide a deep understanding of Bayesian statistics.
View
Bayesian Data Analysis (Chapman & Hall/CRC...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes on the key concepts.
- Complete the exercises at the end of each chapter.
Solve Bayesian inference problems
Show steps
Practice applying Bayesian inference to real-world problems.
Show steps
Show steps
Show steps
- Find a dataset that you are interested in.
- Formulate a Bayesian model for the data.
- Use Bayesian inference to estimate the parameters of the model.
- Evaluate the performance of your model.
Volunteer at a local data science organization
Show steps
Gain practical experience in Bayesian statistics by volunteering at a local data science organization.
Show steps
Show steps
Show steps
- Find a local data science organization that you can volunteer for.
- Contact the organization and express your interest in volunteering.
- Attend volunteer training.
- Volunteer your time to help the organization with data science projects.
Mentor other students in Bayesian statistics
Show steps
Help other students learn Bayesian statistics by mentoring them.
Show steps
Show steps
Show steps
- Find a student who is interested in learning Bayesian statistics.
- Meet with the student regularly to answer their questions and provide guidance.
- Help the student develop their skills in Bayesian statistics.
Create a presentation on Bayesian statistics
Show steps
Create a presentation to demonstrate your understanding of Bayesian statistics.
Show steps
Show steps
Show steps
- Choose a topic related to Bayesian statistics.
- Research the topic and gather information.
- Create a presentation that explains the topic clearly.
- Present your presentation to a group of peers.
Participate in a Bayesian statistics competition
Show steps
Test your skills and knowledge by competing in a Bayesian statistics competition.
Show steps
Show steps
Show steps
- Find a Bayesian statistics competition.
- Register for the competition.
- Complete the competition challenges.
- Submit your results.
Career center
Learners who complete Bayesian Statistics: Capstone Project will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (QAs) use mathematical and statistical models to analyze data and make predictions. QAs rely on advanced statistical modeling techniques like those taught in this course to develop and evaluate models. By mastering Bayesian analysis and time series models, you will gain valuable skills that will help you succeed in this field.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to identify trends and patterns. This course provides a foundational understanding of Bayesian statistics and time series models, which are essential tools for Data Scientists to extract meaningful insights from data. The hands-on experience you gain in this course will prepare you for the challenges you will face in this role.
Statistician
Statistician
Statisticians apply statistical methods to collect and analyze data to solve problems in various industries. The Bayesian statistical methods covered extensively in this course equip you with the tools and techniques to develop and implement statistical models. The course will also provide you with a solid foundation in time series modeling, which is widely used by Statisticians.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They use statistical modeling techniques to train and evaluate models that can make predictions or decisions based on data. This course provides a deep dive into Bayesian statistics and time series modeling, two important techniques used by Machine Learning Engineers to build robust and accurate models.
Financial Analyst
Financial Analyst
Financial Analysts use statistical and financial models to analyze financial data and make investment recommendations. This course provides a solid foundation in Bayesian statistics, a powerful statistical framework widely used in finance. The course also covers time series modeling, a fundamental tool for analyzing financial time series data.
Risk Analyst
Risk Analyst
Risk Analysts evaluate and manage risks for organizations. This course provides a thorough understanding of Bayesian statistics and time series modeling, which are essential techniques for assessing and mitigating risks. By mastering these techniques, you will gain a competitive advantage in this field.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This course provides a foundation in Bayesian statistics and time series modeling, which are widely used in operations research to optimize and improve decision-making processes.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand consumer behavior and market trends. This course provides hands-on experience in Bayesian statistics and time series modeling, which are essential tools for Market Researchers to develop and analyze models that capture market dynamics.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to analyze biological and medical data. This course provides a solid foundation in Bayesian statistics and time series modeling, two key techniques used in biostatistics to analyze complex biological and medical data and draw meaningful conclusions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to extract insights and inform decision-making. This course provides a foundation in Bayesian statistics and time series modeling, which are valuable tools for Data Analysts to uncover hidden patterns and trends in data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While not directly related to the field of Bayesian statistics, this course may provide valuable statistical modeling skills that can be applied to certain aspects of software development, such as performance analysis and optimization.
Actuary
Actuary
Actuaries analyze financial data to assess and manage risks. This course provides a foundation in Bayesian statistics, which is increasingly used in actuarial science to model and assess risks. The course also covers time series modeling, a valuable tool for analyzing financial time series data.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and patterns of health-related events to identify risk factors and develop prevention strategies. This course provides a strong foundation in Bayesian statistics and time series modeling, which are widely used in epidemiology to analyze health data and identify trends and patterns.
Economist
Economist
Economists analyze economic data to understand economic trends and make policy recommendations. This course provides a solid foundation in Bayesian statistics, which is increasingly used in econometrics to model and analyze economic data. The course also covers time series modeling, a valuable tool for analyzing economic time series data.
Insurance Analyst
Insurance Analyst
Insurance Analysts evaluate and manage risks for insurance companies. This course provides a foundation in Bayesian statistics, which is widely used in insurance to model and assess risks. The course also covers time series modeling, a valuable tool for analyzing insurance-related time series data.
For more career information including salaries, visit:
OpenCourser.com/course/009oxr/bayesian
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
Bayesian Statistics: Capstone Project.
Provides a comprehensive overview of Bayesian analysis, covering both theoretical foundations and practical applications. It valuable resource for students, researchers, and practitioners in a wide range of fields.
Provides a comprehensive overview of Bayesian analysis using the R programming language and the Stan probabilistic programming language. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to their own research.
Provides a comprehensive introduction to Bayesian analysis using the Python programming language. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to their own research.
Provides a practical introduction to Bayesian analysis for programmers. It great resource for students and researchers who want to learn how to apply Bayesian methods to their own research.
Provides a comprehensive overview of Bayesian forecasting and dynamic models. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to forecasting and dynamic modeling.
Save
Provides a comprehensive overview of the Bayesian approach to decision theory. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to decision making.
Provides a comprehensive overview of Bayesian cognitive modeling. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to cognitive modeling.
Provides a comprehensive overview of Bayesian statistics for social scientists. It valuable resource for students, researchers, and practitioners who want to learn how to apply Bayesian methods to social science research.
Provides a hands-on introduction to Bayesian analysis using the R programming language. It great resource for students and researchers who want to learn how to apply Bayesian methods to their own research.
Save
Provides a gentle introduction to Bayesian statistics. It great resource for students and researchers who want to learn the basics of Bayesian analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/009oxr/bayesian
Share
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Level
Advanced
Via
Coursera
Institution
University of California, Santa Cruz
Instructor
Jizhou Kang
Language
English
Transcripts
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This course belongs to a
collection
called
Bayesian Statistics. It's comprised of five courses:
- Bayesian Statistics: Mixture Models
- Bayesian Statistics: Time Series Analysis
- Bayesian Statistics: Techniques and Models
- Bayesian Statistics: From Concept to Data Analysis
- Bayesian Statistics: Capstone Project (viewing)
Good to know
Explores Bayesian Conjugate Analysis for Autogressive Time Series Models, which is standard in statistical modeling
Examines Model Selection Criteria, which helps learners select the order of AR processes and the number of mixing components in mixture of AR models
Develops Bayesian location mixture of AR(P) model, which helps learners analyze more complex time series data
Taught by Jizhou Kang, who is recognized for their work in Bayesian statistics
Offers hands-on labs and interactive materials, which allows learners to apply their knowledge practically
Requires learners to have a strong foundation in Bayesian statistics, which may be a barrier for some
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