About this Course

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Learner Career Outcomes

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started a new career after completing these courses

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got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level
Approx. 12 hours to complete
English

Skills you will gain

StatisticsBayesian StatisticsBayesian InferenceR Programming

Learner Career Outcomes

20%

started a new career after completing these courses

14%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 12 hours to complete
English

Instructor

Offered by

Placeholder

University of California, Santa Cruz

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(9,172 ratings)Info
Week
1

Week 1

3 hours to complete

Probability and Bayes' Theorem

3 hours to complete
8 videos (Total 38 min), 4 readings, 5 quizzes
8 videos
Lesson 1.1 Classical and frequentist probability6m
Lesson 1.2 Bayesian probability and coherence3m
Lesson 2.1 Conditional probability4m
Lesson 2.2 Bayes' theorem6m
Lesson 3.1 Bernoulli and binomial distributions5m
Lesson 3.2 Uniform distribution5m
Lesson 3.3 Exponential and normal distributions2m
4 readings
Module 1 objectives, assignments, and supplementary materials3m
Background for Lesson 110m
Supplementary material for Lesson 23m
Supplementary material for Lesson 320m
5 practice exercises
Lesson 130m
Lesson 212m
Lesson 3.130m
Lesson 3.2-3.310m
Module 1 Honors15m
Week
2

Week 2

3 hours to complete

Statistical Inference

3 hours to complete
11 videos (Total 59 min), 5 readings, 4 quizzes
11 videos
Lesson 4.2 Likelihood function and maximum likelihood7m
Lesson 4.3 Computing the MLE3m
Lesson 4.4 Computing the MLE: examples4m
Introduction to R6m
Plotting the likelihood in R4m
Plotting the likelihood in Excel4m
Lesson 5.1 Inference example: frequentist4m
Lesson 5.2 Inference example: Bayesian6m
Lesson 5.3 Continuous version of Bayes' theorem4m
Lesson 5.4 Posterior intervals7m
5 readings
Module 2 objectives, assignments, and supplementary materials3m
Background for Lesson 410m
Supplementary material for Lesson 45m
Background for Lesson 510m
Supplementary material for Lesson 510m
4 practice exercises
Lesson 48m
Lesson 5.1-5.230m
Lesson 5.3-5.430m
Module 2 Honors6m
Week
3

Week 3

3 hours to complete

Priors and Models for Discrete Data

3 hours to complete
9 videos (Total 66 min), 2 readings, 4 quizzes
9 videos
Lesson 6.2 Prior predictive: binomial example5m
Lesson 6.3 Posterior predictive distribution4m
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3m
Lesson 7.2 Conjugate priors4m
Lesson 7.3 Posterior mean and effective sample size7m
Data analysis example in R12m
Data analysis example in Excel16m
Lesson 8.1 Poisson data8m
2 readings
Module 3 objectives, assignments, and supplementary materials3m
R and Excel code from example analysis10m
4 practice exercises
Lesson 630m
Lesson 715m
Lesson 815m
Module 3 Honors8m
Week
4

Week 4

3 hours to complete

Models for Continuous Data

3 hours to complete
9 videos (Total 69 min), 5 readings, 5 quizzes
9 videos
Lesson 10.1 Normal likelihood with variance known3m
Lesson 10.2 Normal likelihood with variance unknown3m
Lesson 11.1 Non-informative priors8m
Lesson 11.2 Jeffreys prior3m
Linear regression in R17m
Linear regression in Excel (Analysis ToolPak)13m
Linear regression in Excel (StatPlus by AnalystSoft)14m
Conclusion1m
5 readings
Module 4 objectives, assignments, and supplementary materials3m
Supplementary material for Lesson 1010m
Supplementary material for Lesson 115m
Background for Lesson 1210m
R and Excel code for regression5m
5 practice exercises
Lesson 912m
Lesson 1020m
Lesson 1110m
Regression15m
Module 4 Honors6m

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