Institution: University of California Santa Cruz
Start Date: July 23, 2018
The University of California, Santa Cruz is excited to announce to you a free online course named “Bayesian Statistics: Techniques and Models”.This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them.
In this course, you will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course will start on July 23, 2018.
- Duration: 5 weeks of study
- Commitment: 4-6 hours/week
- Subject: Data Science
- Institution: The University of California, Santa Cruz
- Languages: English
- Price: Free
- Session: Start on July 23, 2018
- Requirement: None
- Certificate Available: Yes
Who Developed the Course
The University of California, Santa Cruz, is a public research university and one of 10 campuses in the University of California system. Located 75 miles (120 km) south of San Francisco at the edge of the coastal community of Santa Cruz, the campus lies on 2,001 acres (810 ha) of rolling, forested hills overlooking the Pacific Ocean and Monterey Bay.
No prior programming experience is needed.
Where Could This Lead You
After successfully completing the course you can build your career in the following:
- Senior Research Scientist
- Lead Data Scientist
Get Extra Benefits
If you pay ₹1,944for this course, you will have to access all of the features content you need to earn a course certificate.
you will have to access to all of the features content you need to earn a course certificate.
- If you complete the course successfully, your electronic certificate will be added to your accomplishment page- from there, you can print your certificate or add it to your LinkedIn profile.
- Note that the course certificate does not represent official academic credit from the partner institute offering the course
How to Join This Course
To join for the course, you may enroll here.
- WEEK 1: Statistical modeling and Monte Carlo estimation : Statistical modeling, Bayesian modeling, Monte Carlo estimation
- WEEK 2: Markov chain Monte Carlo (MCMC): Metropolis-Hastings, Gibbs sampling, assessing convergence
- WEEK 3: Common statistical models : Linear regression, ANOVA, logistic regression, multiple factor ANOVA
- WEEK 4: Count data and hierarchical modeling: Poisson regression, hierarchical modeling
- WEEK 5: Capstone project: Peer-reviewed data analysis project
Who Will You Learn With
Matthew Heiner: Doctoral student. Applied Mathematics and Statistics.
- Importance of Course: Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.
- Importance of Certificate: You can get a verified certificate to highlight the knowledge and skills you gain. You can prove your success when applying for jobs or courses and display on your LinkedIn or CV.
For more information about the course, you may visit the Website.