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Bayesian Statistics

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

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Rating 4.5 based on 303 ratings
Length 5 weeks
Effort Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics.
Starts Aug 24 (4 weeks ago)
Cost $49
From University of California, Santa Cruz via Coursera
Instructor Herbert Lee
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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What people are saying

introduction to bayesian

An interesting introduction to Bayesian statistics and inference.

A very solid introduction to Bayesian Statistics.

Great introduction to Bayesian Statistics with some easy-enough-to-follow mathematical insights.

Great introduction to Bayesian Statistics.

Very good introduction to Bayesian Statistics.

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

A great introduction to bayesian statistics.

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herbert lee

Herbert Lee is great at explaining the mathematics behind Bayesian statistics.

Thank you, Herbert Lee and Coursera.

Prof. Herbert Lee is a great professor providing very thorough notes and material for the Bayesian paradigm of Statistics.

Thank you so much, Herbert Lee.

Thanks to Prof Herbert Lee for making the easy to understand without sacrificing rigour.

Herbert Lee's Tests are fun (Best!)

I also really valued learning how to use R. Professor Herbert Lee is world-class.

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recommend this course

I strongly recommend this course to those who are interested in learning theoretical concepts that build Machine Learning statistics especially Bayesian.

Would recommend this course to anyone.

I warmly recommend this course to those already familiar with the frequentist approach and willing to expand their knowledge.

Overall, I would definitely recommend this course.

I recommend this course for all data scientists and machine learning practitioners.

Other than that I can whole-heartedly recommend this course.

I strongly recommend this course.

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machine learning

But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge.

As a graduate student pursuing Machine Learning, this was a great course for me to get introduced to Bayesian Models.

I took this course due to my interest in machine learning and graphical models.

I will use the principles taugh for other topics like machine learning.

Also, adding modern real life examples and going into detail would make this course better A well organized course, learned important concepts in statistics and probability that will definitely help anyone wanting to specialize in machine learning or take up data science.

Followed the course in order to fill a gap I had in statistics knowledge, as I'm very interested in machine learning - deep learning, and always came upon things as MLE without really knowing well what they were talking all about.

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data analysis

Even better if you continue with the 2nd course that teaches about how to implement Bayesian data analysis in JAGS Excellent course, but the lack of the written notes is a big minus Amazing.

Now I am no more afraid to face the book 'Bayesian Data Analysis' by A. Gelman et al.

Hi , this course opened a door for me in Data analysis.

Great introductory course on Bayesian data analysis.

This is a very useful course for people to do the data analysis in astronomy.

Delivers what promises: Bayesian Statistics: From Concept to Data Analysis.

The course is excellent to learn all the basic stuff needed to master the technique of Bayesian Data Analysis.

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easy to follow

The teacher is excellent and charming and the course is also easy to follow.

Good course This course is well prepared.The videos are of high quality and the lessons are easy to follow.I enjoyed the Honors content as well, that gives an extra challenge to those who want it.Thanks!

I found the videos easy to follow and that they prepared me for the quizzes.

A very complete and easy to follow course.

Very concise and easy to follow to the end.

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it.

More 'real life' examples instead of coin flipping examples - although easy to follow - would be very helpful as well, maybe in a consecutive course with applied bayesian statistics?

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Professor of Philosophy Fellow $20k

Lecturer, Philosophy $44k

Professor of Philosophy 2 $54k

Graduate Instructor of Philosophy $55k

Adjunct Lecturer in Philosophy $59k

Philosophy $64k

Professor of Theology/Philosophy $86k

Senior Professor of Philosophy $90k

Professor of Philosophy Consultant $116k

Assitant Professor of Philosophy $121k

Associate Instructor of Philosophy $151k

President Professor of Philosophy $264k

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Rating 4.5 based on 303 ratings
Length 5 weeks
Effort Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics.
Starts Aug 24 (4 weeks ago)
Cost $49
From University of California, Santa Cruz via Coursera
Instructor Herbert Lee
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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