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1. Shard Calculation

Query:

Response:

Calculated Shard: 97 (from laksa193)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa097.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.coursera.org/learn/bayesian-statistics\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.coursera.org/learn/bayesian-statistics\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.coursera.org\/learn\/bayesian-statistics","crawl_time":1776269578,"first_indexed_time":1468253574,"http_code":200,"src_unparsed":"org,coursera!www,\/learn\/bayesian-statistics s443","src_root_hash":"1995246928644532097","history_drop_reason":null,"meta_title":"Bayesian Statistics: From Concept to Data Analysis | Coursera","meta_descriptions":["Offered by University of California, Santa Cruz. This course introduces the Bayesian approach to statistics, starting with the concept of ... Enroll for free."],"attrs_boilerpipe_text":"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.","attrs_markdown":"- [For Individuals](https:\/\/www.coursera.org\/)\n- [For Businesses](https:\/\/www.coursera.org\/business?utm_content=corp-to-home-for-enterprise&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out)\n- [For Universities](https:\/\/www.coursera.org\/campus?utm_content=corp-to-landing-for-campus&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out)\n- [For Governments](https:\/\/www.coursera.org\/government?utm_content=corp-to-landing-for-government&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out)\n\nExplore\n\n[Degrees](https:\/\/www.coursera.org\/degrees)\n\n[Log In](https:\/\/www.coursera.org\/learn\/bayesian-statistics?authMode=login)\n\n[Join for Free](https:\/\/www.coursera.org\/learn\/bayesian-statistics?authMode=signup)\n\nJoin for Free\n\n![University of California, Santa Cruz](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/http:\/\/coursera-university-assets.s3.amazonaws.com\/50\/f80f825a5946daa0b06ea1bc802748\/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=28&h=28)\n\n## Bayesian Statistics: From Concept to Data Analysis\n\n- [About](https:\/\/www.coursera.org\/learn\/bayesian-statistics#about)\n- [Outcomes](https:\/\/www.coursera.org\/learn\/bayesian-statistics#outcomes)\n- [Modules](https:\/\/www.coursera.org\/learn\/bayesian-statistics#modules)\n- [Recommendations](https:\/\/www.coursera.org\/learn\/bayesian-statistics#recommendations)\n- [Testimonials](https:\/\/www.coursera.org\/learn\/bayesian-statistics#testimonials)\n- [Reviews](https:\/\/www.coursera.org\/learn\/bayesian-statistics#reviews)\n\n1. [Browse](https:\/\/www.coursera.org\/browse)\n2. [Data Science](https:\/\/www.coursera.org\/browse\/data-science)\n3. [Probability and Statistics](https:\/\/www.coursera.org\/browse\/data-science\/probability-and-statistics)\n\n**Ends soon:** Grow your skills with Coursera Plus for \\$239\/year (usually \\$399). [**Save now**](https:\/\/www.coursera.org\/courseraplus\/special\/global-spring-2026?utm_medium=coursera&utm_source=bluebanner&utm_campaign=2026MarchQ1SpringTentpole).\n\n![University of California, Santa Cruz](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/http:\/\/coursera-university-assets.s3.amazonaws.com\/05\/373af864af42a2bc6e303590742ba2\/2021-UC-Santa-Cruz-Logo---Blue-RGB-copy.png?auto=format%2Ccompress&dpr=1&h=45)\n\n# Bayesian Statistics: From Concept to Data Analysis\nThis course is part of [Bayesian Statistics Specialization](https:\/\/www.coursera.org\/specializations\/bayesian-statistics)\n\n![Herbert Lee](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-instructor-photos.s3.amazonaws.com\/38\/6ce6e0f5f211e590ed7d08fe585ef8\/lee_herbie-300.jpg?auto=format%2Ccompress&dpr=1&w=75&h=75&fit=crop)\n\nInstructor: [Herbert Lee](https:\/\/www.coursera.org\/instructor\/ucsc)\n\n**159,482** already enrolled\n\nIncluded with\n\n•\n\n[Learn more](https:\/\/www.coursera.org\/courseraplus)\n\n[4 modules](https:\/\/www.coursera.org\/learn\/bayesian-statistics#modules)\n\nGain insight into a topic and learn the fundamentals.\n\n4\\.6\n\n3,227 reviews\n\nIntermediate level\n\nRecommended experience\n\n## Recommended experience\nIntermediate level\n\nSome experience with statistical data analysis is recommended.\n\nOK\n\nFlexible schedule\n\n1 week at 10 hours a week\n\nLearn at your own pace\n\n92%\n\nMost learners liked this course\n***\n\n[4 modules](https:\/\/www.coursera.org\/learn\/bayesian-statistics#modules)\n\nGain insight into a topic and learn the fundamentals.\n\n4\\.6\n\n3,227 reviews\n\nIntermediate level\n\nRecommended experience\n\n## Recommended experience\nIntermediate level\n\nSome experience with statistical data analysis is recommended.\n\nOK\n\nFlexible schedule\n\n1 week at 10 hours a week\n\nLearn at your own pace\n\n92%\n\nMost learners liked this course\n\n- [About](https:\/\/www.coursera.org\/learn\/bayesian-statistics#about)\n- [Outcomes](https:\/\/www.coursera.org\/learn\/bayesian-statistics#outcomes)\n- [Modules](https:\/\/www.coursera.org\/learn\/bayesian-statistics#modules)\n- [Recommendations](https:\/\/www.coursera.org\/learn\/bayesian-statistics#recommendations)\n- [Testimonials](https:\/\/www.coursera.org\/learn\/bayesian-statistics#testimonials)\n- [Reviews](https:\/\/www.coursera.org\/learn\/bayesian-statistics#reviews)\n\n## What you'll learn\n- Describe & apply the Bayesian approach to statistics.\n- Explain the key differences between Bayesian and Frequentist approaches.\n- Master the basics of the R computing environment.\n\n## Skills you'll gain\n- [Probability](https:\/\/www.coursera.org\/courses?query=probability)\n- [Statistical Methods](https:\/\/www.coursera.org\/courses?query=statistical%20methods)\n- [Statistical Modeling](https:\/\/www.coursera.org\/courses?query=statistical%20modeling)\n- [Statistics](https:\/\/www.coursera.org\/courses?query=statistics)\n- [Data Modeling](https:\/\/www.coursera.org\/courses?query=data%20modeling)\n- [Statistical Visualization](https:\/\/www.coursera.org\/courses?query=statistical%20visualization)\n- [Data Analysis](https:\/\/www.coursera.org\/courses?query=data%20analysis)\n- [Probability Distribution](https:\/\/www.coursera.org\/courses?query=probability%20distribution)\n- [Predictive Modeling](https:\/\/www.coursera.org\/courses?query=predictive%20modeling)\n- [Analytical Skills](https:\/\/www.coursera.org\/courses?query=analytical%20skills)\n- [Statistical Inference](https:\/\/www.coursera.org\/courses?query=statistical%20inference)\n- [Regression Analysis](https:\/\/www.coursera.org\/courses?query=regression%20analysis)\n- [Probability & Statistics](https:\/\/www.coursera.org\/courses?query=probability%20%26%20statistics)\n- [Bayesian Statistics](https:\/\/www.coursera.org\/courses?query=bayesian%20statistics)\n- [Data Visualization](https:\/\/www.coursera.org\/courses?query=data%20visualization)\n- Show all\n\n## Tools you'll learn\n- [R Programming](https:\/\/www.coursera.org\/courses?query=r%20programming)\n- [R (Software)](https:\/\/www.coursera.org\/courses?query=r%20\$software\$)\n- [Microsoft Excel](https:\/\/www.coursera.org\/courses?query=microsoft%20excel)\n\n## Details to know\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/images\/31ebcba3851b87d1d8609abf15d0ff7e.png?auto=format%2Ccompress&dpr=1&w=24&h=24)\n\nShareable certificate\n\nAdd to your LinkedIn profile\n\nAssessments\n\n18 assignments\n\nTaught in English\n\n22 languages available\n\n# See how employees at top companies are mastering in-demand skills\n[Learn more about Coursera for Business](https:\/\/www.coursera.org\/business?utm_medium=coursera&utm_source=xdp&utm_campaign=website&utm_content=c4b-xdp-thin-card&utm_term=out)\n\n![ logos of Petrobras, TATA, Danone, Capgemini, P\\&G and L'Oreal ](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/images\/74c8747e8210831049cf88dd4eefe26c.png?auto=format%2Ccompress&dpr=2&blur=200&px=8&max-w=320)\n\n## Build your subject-matter expertise\nThis course is part of the [Bayesian Statistics Specialization](https:\/\/www.coursera.org\/specializations\/bayesian-statistics)\n\nWhen you enroll in this course, you'll also be enrolled in this Specialization.\n\n- Learn new concepts from industry experts\n- Gain a foundational understanding of a subject or tool\n- Develop job-relevant skills with hands-on projects\n- Earn a shareable career certificate\n\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/images\/a7c5400e51272c78b710ce9b56fd3178.png?auto=format%2Ccompress&dpr=2&blur=200&px=8&max-w=562)\n\n## There are 4 modules in this course\nThis 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.\n\n### Probability and Bayes' Theorem\nModule 1•3 hours to complete\n\nModule details\n\nIn this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables.\n\n#### What's included\n8 videos4 readings5 assignments\n\nShow info about module content\n\n##### 8 videos•Total 38 minutes\n- 🎥 Course introduction•4 minutes\n- 🎥 Lesson 1.1 Classical and frequentist probability•6 minutes\n- 🎥 Lesson 1.2 Bayesian probability and coherence•3 minutes\n- 🎥 Lesson 2.1 Conditional probability•4 minutes\n- 🎥 Lesson 2.2 Bayes' theorem•6 minutes\n- 🎥 Lesson 3.1 Bernoulli and binomial distributions•5 minutes\n- 🎥 Lesson 3.2 Uniform distribution•5 minutes\n- 🎥 Lesson 3.3 Exponential and normal distributions•3 minutes\n\n##### 4 readings•Total 36 minutes\n- 📖 Module 1 objectives, assignments, and supplementary materials•3 minutes\n- 📖 Background for Lesson 1•10 minutes\n- 📖 Supplementary material for Lesson 2•3 minutes\n- 📖 Supplementary material for Lesson 3•20 minutes\n\n##### 5 assignments•Total 97 minutes\n- ✍️ Lesson 1: Demonstrate your knowledge•30 minutes\n- ✍️ Lesson 2: Demonstrate your knowledge•12 minutes\n- ✍️ Lesson 3.1: Demonstrate your knowledge•30 minutes\n- ✍️ Lesson 3.2-3.3: Demonstrate your knowledge•10 minutes\n- ✍️ Module 1 Honors •15 minutes\n\n### Statistical Inference\nModule 2•3 hours to complete\n\nModule details\n\nThis module introduces concepts of statistical inference from both frequentist and Bayesian perspectives. Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. Lesson 5 introduces the fundamentals of Bayesian inference. Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes’ theorem to update the prior with data to obtain posterior probabilities. This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals.\n\n#### What's included\n11 videos5 readings4 assignments\n\nShow info about module content\n\n##### 11 videos•Total 59 minutes\n- 🎥 Lesson 4.1 Confidence intervals•5 minutes\n- 🎥 Lesson 4.2 Likelihood function and maximum likelihood•7 minutes\n- 🎥 Lesson 4.3 Computing the MLE•3 minutes\n- 🎥 Lesson 4.4 Computing the MLE: examples•4 minutes\n- 🎥 Introduction to R•7 minutes\n- 🎥 Plotting the likelihood in R•5 minutes\n- 🎥 Plotting the likelihood in Excel•5 minutes\n- 🎥 Lesson 5.1 Inference example: frequentist•4 minutes\n- 🎥 Lesson 5.2 Inference example: Bayesian•7 minutes\n- 🎥 Lesson 5.3 Continuous version of Bayes' theorem•4 minutes\n- 🎥 Lesson 5.4 Posterior intervals•8 minutes\n\n##### 5 readings•Total 38 minutes\n- 📖 Module 2 objectives, assignments, and supplementary materials•3 minutes\n- 📖 Background for Lesson 4•10 minutes\n- 📖 Supplementary material for Lesson 4•5 minutes\n- 📖 Background for Lesson 5•10 minutes\n- 📖 Supplementary material for Lesson 5•10 minutes\n\n##### 4 assignments•Total 74 minutes\n- ✍️ Lesson 4: Demonstrate your knowledge•8 minutes\n- ✍️ Lesson 5.1-5.2: Demonstrate your knowledge•30 minutes\n- ✍️ Lesson 5.3-5.4: Demonstrate your knowledge•30 minutes\n- ✍️ Module 2 Honors •6 minutes\n\n### Priors and Models for Discrete Data\nModule 3•2 hours to complete\n\nModule details\n\nIn this module, you will learn methods for selecting prior distributions and building models for discrete data. Lesson 6 introduces prior selection and predictive distributions as a means of evaluating priors. Lesson 7 demonstrates Bayesian analysis of Bernoulli data and introduces the computationally convenient concept of conjugate priors. Lesson 8 builds a conjugate model for Poisson data and discusses strategies for selection of prior hyperparameters.\n\n#### What's included\n9 videos2 readings4 assignments\n\nShow info about module content\n\n##### 9 videos•Total 66 minutes\n- 🎥 Lesson 6.1 Priors and prior predictive distributions•4 minutes\n- 🎥 Lesson 6.2 Prior predictive: binomial example•5 minutes\n- 🎥 Lesson 6.3 Posterior predictive distribution•4 minutes\n- 🎥 Lesson 7.1 Bernoulli\/binomial likelihood with uniform prior•4 minutes\n- 🎥 Lesson 7.2 Conjugate priors•5 minutes\n- 🎥 Lesson 7.3 Posterior mean and effective sample size•7 minutes\n- 🎥 Data analysis example in R•13 minutes\n- 🎥 Data analysis example in Excel•16 minutes\n- 🎥 Lesson 8.1 Poisson data•8 minutes\n\n##### 2 readings•Total 13 minutes\n- 📖 Module 3 objectives, assignments, and supplementary materials•3 minutes\n- 📖 R and Excel code from example analysis•10 minutes\n\n##### 4 assignments•Total 68 minutes\n- ✍️ Lesson 6: Demonstrate your knowledge•30 minutes\n- ✍️ Lesson 7: Demonstrate your knowledge•15 minutes\n- ✍️ Lesson 8: Demonstrate your knowledge•15 minutes\n- ✍️ Module 3 Honors •8 minutes\n\n### Models for Continuous Data\nModule 4•3 hours to complete\n\nModule details\n\nThis module covers conjugate and objective Bayesian analysis for continuous data. Lesson 9 presents the conjugate model for exponentially distributed data. Lesson 10 discusses models for normally distributed data, which play a central role in statistics. In Lesson 11, we return to prior selection and discuss ‘objective’ or ‘non-informative’ priors. Lesson 12 presents Bayesian linear regression with non-informative priors, which yield results comparable to those of classical regression.\n\n#### What's included\n9 videos5 readings5 assignments\n\nShow info about module content\n\n##### 9 videos•Total 69 minutes\n- 🎥 Lesson 9.1 Exponential data•4 minutes\n- 🎥 Lesson 10.1 Normal likelihood with variance known•4 minutes\n- 🎥 Lesson 10.2 Normal likelihood with variance unknown•3 minutes\n- 🎥 Lesson 11.1 Non-informative priors•8 minutes\n- 🎥 Lesson 11.2 Jeffreys prior•3 minutes\n- 🎥 Linear regression in R (Datasets included in Downloads)•17 minutes\n- 🎥 Linear regression in Excel (Analysis ToolPak)•14 minutes\n- 🎥 Linear regression in Excel (StatPlus by AnalystSoft)•14 minutes\n- 🎥 Conclusion•1 minute\n\n##### 5 readings•Total 33 minutes\n- 📖 Module 4 objectives, assignments, and supplementary materials•3 minutes\n- 📖 Supplementary material for Lesson 10•10 minutes\n- 📖 Supplementary material for Lesson 11•5 minutes\n- 📖 Background for Lesson 12•10 minutes\n- 📖 R and Excel code for regression•5 minutes\n\n##### 5 assignments•Total 63 minutes\n- ✍️ Lesson 9: Demonstrate your knowledge•12 minutes\n- ✍️ Lesson 10: Demonstrate your knowledge•20 minutes\n- ✍️ Lesson 11: Demonstrate your knowledge•10 minutes\n- ✍️ Regression: Demonstrate your knowledge•15 minutes\n- ✍️ Module 4 Honors •6 minutes\n\n### Earn a career certificate\nAdd this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.\n\n### Instructor\nInstructor ratings\n\n## Instructor ratings\nWe asked all learners to give feedback on our instructors based on the quality of their teaching style.\n\nOK\n\n4\\.6\n\n(525 ratings)\n\n![Herbert Lee](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-instructor-photos.s3.amazonaws.com\/38\/6ce6e0f5f211e590ed7d08fe585ef8\/lee_herbie-300.jpg?auto=format%2Ccompress&dpr=1&w=75&h=75&fit=crop)\n\n[Herbert Lee](https:\/\/www.coursera.org\/instructor\/ucsc)\n\nUniversity of California, Santa Cruz\n\n1 Course•159,482 learners\n\n### Offered by\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/http:\/\/coursera-university-assets.s3.amazonaws.com\/50\/f80f825a5946daa0b06ea1bc802748\/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=38&h=38&fit=fill)\n\n[University of California, Santa Cruz](https:\/\/www.coursera.org\/partners\/ucsc)\n\nLearn more\n\n## Offered by\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/http:\/\/coursera-university-assets.s3.amazonaws.com\/50\/f80f825a5946daa0b06ea1bc802748\/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=88&h=88&fit=fill)\n\n[University of California, Santa Cruz](https:\/\/www.coursera.org\/partners\/ucsc)\n\nUC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.\n\nOK\n\n## Explore more from Probability and Statistics\nRecommended\n\nSpecializations\n\nRelated\n\n- ![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-course-photos.s3.amazonaws.com\/8d\/9466452687403d977f4cfc177fae55\/Bayesian3.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50)\n  \n  Status: Free Trial\n  \n  Free Trial\n  \n  U\n  \n  University of California, Santa Cruz\n  \n  [Bayesian Statistics: Techniques and Models](https:\/\/www.coursera.org\/learn\/mcmc-bayesian-statistics)\n  \n  Course\n- ![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-course-photos.s3.amazonaws.com\/65\/c558c0f50211e5a52493c7e6aee302\/baysian_statistics.v2a.png?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50)\n  \n  D\n  \n  Duke University\n  \n  [Bayesian Statistics](https:\/\/www.coursera.org\/learn\/bayesian)\n  \n  Course\n- ![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-course-photos.s3.amazonaws.com\/58\/85f3621fac44fc9030ed8420e25127\/shutterstock_1642476472.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50)\n  \n  Status: Free Trial\n  \n  Free Trial\n  \n  A\n  \n  Arizona State University\n  \n  [Bayesian Statistical Concepts and Methods](https:\/\/www.coursera.org\/learn\/bayesian-statistical-concepts-and-methods)\n  \n  Course\n- ![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera-course-photos.s3.amazonaws.com\/50\/5ab2b9382e4b04bae268aadd5f6058\/data-sci-2.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50)\n  \n  Status: Free Trial\n  \n  Free Trial\n  \n  I\n  \n  Illinois Tech\n  \n  [Bayesian Computational Statistics](https:\/\/www.coursera.org\/learn\/illinois-tech-bayesian-computational-statistics)\n  \n  Course\n\nShow 8 more\n\n## Why people choose Coursera for their career\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/growth_testimonials\/passionate_learner\/Felipe_Moitta.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop)\n\n### Felipe M.\nLearner since 2018\n\n\"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood.\"\n\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/growth_testimonials\/passionate_learner\/Jennifer_John.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop)\n\n### Jennifer J.\nLearner since 2020\n\n\"I directly applied the concepts and skills I learned from my courses to an exciting new project at work.\"\n\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/growth_testimonials\/passionate_learner\/Larry_Tao_Wang_1.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop)\n\n### Larry W.\nLearner since 2021\n\n\"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go.\"\n\n![](https:\/\/d3njjcbhbojbot.cloudfront.net\/api\/utilities\/v1\/imageproxy\/https:\/\/coursera_assets.s3.amazonaws.com\/growth_testimonials\/passionate_learner\/Chaitanya_Anand.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop)\n\n### Chaitanya A.\n\n\"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits.\"\n\n## Learner reviews\n4\\.6\n\n3,227 reviews\n\n- 5 stars\n  67\\.33%\n- 4 stars\n  25\\.03%\n- 3 stars\n  5\\.23%\n- 2 stars\n  1\\.27%\n- 1 star\n  1\\.11%\n\nShowing 3 of 3227\n\nD\n\nDG\n\n4\n\n·\n\nReviewed on Dec 9, 2019\n\nIt was a good course for me to get familiar with the new perspective on statistics. Thank you! Maybe, some extended practice exercise at the end of the course would make it even better)\n\nM\n\nMD\n\n4\n\n·\n\nReviewed on Feb 19, 2020\n\nthe notes for the lectures are missing.In my opinion the notes, which includes the video materials could be very useful.the course was good. I learnt some new concepts in bayesian thinking.\n\nM\n\nMM\n\n4\n\n·\n\nReviewed on Sep 25, 2019\n\nVery clear and informative. 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Boilerpipe Text	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.
Markdown	- [For Individuals](https://www.coursera.org/) - [For Businesses](https://www.coursera.org/business?utm_content=corp-to-home-for-enterprise&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out) - [For Universities](https://www.coursera.org/campus?utm_content=corp-to-landing-for-campus&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out) - [For Governments](https://www.coursera.org/government?utm_content=corp-to-landing-for-government&utm_campaign=website&utm_medium=coursera&utm_source=header&utm_term=b-out) Explore [Degrees](https://www.coursera.org/degrees) [Log In](https://www.coursera.org/learn/bayesian-statistics?authMode=login) [Join for Free](https://www.coursera.org/learn/bayesian-statistics?authMode=signup) Join for Free ![University of California, Santa Cruz](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/http://coursera-university-assets.s3.amazonaws.com/50/f80f825a5946daa0b06ea1bc802748/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=28&h=28) ## Bayesian Statistics: From Concept to Data Analysis - [About](https://www.coursera.org/learn/bayesian-statistics#about) - [Outcomes](https://www.coursera.org/learn/bayesian-statistics#outcomes) - [Modules](https://www.coursera.org/learn/bayesian-statistics#modules) - [Recommendations](https://www.coursera.org/learn/bayesian-statistics#recommendations) - [Testimonials](https://www.coursera.org/learn/bayesian-statistics#testimonials) - [Reviews](https://www.coursera.org/learn/bayesian-statistics#reviews) 1. [Browse](https://www.coursera.org/browse) 2. [Data Science](https://www.coursera.org/browse/data-science) 3. [Probability and Statistics](https://www.coursera.org/browse/data-science/probability-and-statistics) Ends soon: Grow your skills with Coursera Plus for \$239/year (usually \$399). [Save now](https://www.coursera.org/courseraplus/special/global-spring-2026?utm_medium=coursera&utm_source=bluebanner&utm_campaign=2026MarchQ1SpringTentpole). ![University of California, Santa Cruz](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/http://coursera-university-assets.s3.amazonaws.com/05/373af864af42a2bc6e303590742ba2/2021-UC-Santa-Cruz-Logo---Blue-RGB-copy.png?auto=format%2Ccompress&dpr=1&h=45) # Bayesian Statistics: From Concept to Data Analysis This course is part of [Bayesian Statistics Specialization](https://www.coursera.org/specializations/bayesian-statistics) ![Herbert Lee](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-instructor-photos.s3.amazonaws.com/38/6ce6e0f5f211e590ed7d08fe585ef8/lee_herbie-300.jpg?auto=format%2Ccompress&dpr=1&w=75&h=75&fit=crop) Instructor: [Herbert Lee](https://www.coursera.org/instructor/ucsc) 159,482 already enrolled Included with • [Learn more](https://www.coursera.org/courseraplus) [4 modules](https://www.coursera.org/learn/bayesian-statistics#modules) Gain insight into a topic and learn the fundamentals. 4\.6 3,227 reviews Intermediate level Recommended experience ## Recommended experience Intermediate level Some experience with statistical data analysis is recommended. OK Flexible schedule 1 week at 10 hours a week Learn at your own pace 92% Most learners liked this course *** [4 modules](https://www.coursera.org/learn/bayesian-statistics#modules) Gain insight into a topic and learn the fundamentals. 4\.6 3,227 reviews Intermediate level Recommended experience ## Recommended experience Intermediate level Some experience with statistical data analysis is recommended. OK Flexible schedule 1 week at 10 hours a week Learn at your own pace 92% Most learners liked this course - [About](https://www.coursera.org/learn/bayesian-statistics#about) - [Outcomes](https://www.coursera.org/learn/bayesian-statistics#outcomes) - [Modules](https://www.coursera.org/learn/bayesian-statistics#modules) - [Recommendations](https://www.coursera.org/learn/bayesian-statistics#recommendations) - [Testimonials](https://www.coursera.org/learn/bayesian-statistics#testimonials) - [Reviews](https://www.coursera.org/learn/bayesian-statistics#reviews) ## What you'll learn - Describe & apply the Bayesian approach to statistics. - Explain the key differences between Bayesian and Frequentist approaches. - Master the basics of the R computing environment. ## Skills you'll gain - [Probability](https://www.coursera.org/courses?query=probability) - [Statistical Methods](https://www.coursera.org/courses?query=statistical%20methods) - [Statistical Modeling](https://www.coursera.org/courses?query=statistical%20modeling) - [Statistics](https://www.coursera.org/courses?query=statistics) - [Data Modeling](https://www.coursera.org/courses?query=data%20modeling) - [Statistical Visualization](https://www.coursera.org/courses?query=statistical%20visualization) - [Data Analysis](https://www.coursera.org/courses?query=data%20analysis) - [Probability Distribution](https://www.coursera.org/courses?query=probability%20distribution) - [Predictive Modeling](https://www.coursera.org/courses?query=predictive%20modeling) - [Analytical Skills](https://www.coursera.org/courses?query=analytical%20skills) - [Statistical Inference](https://www.coursera.org/courses?query=statistical%20inference) - [Regression Analysis](https://www.coursera.org/courses?query=regression%20analysis) - [Probability & Statistics](https://www.coursera.org/courses?query=probability%20%26%20statistics) - [Bayesian Statistics](https://www.coursera.org/courses?query=bayesian%20statistics) - [Data Visualization](https://www.coursera.org/courses?query=data%20visualization) - Show all ## Tools you'll learn - [R Programming](https://www.coursera.org/courses?query=r%20programming) - [R (Software)](https://www.coursera.org/courses?query=r%20$software$) - [Microsoft Excel](https://www.coursera.org/courses?query=microsoft%20excel) ## Details to know ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/images/31ebcba3851b87d1d8609abf15d0ff7e.png?auto=format%2Ccompress&dpr=1&w=24&h=24) Shareable certificate Add to your LinkedIn profile Assessments 18 assignments Taught in English 22 languages available # See how employees at top companies are mastering in-demand skills [Learn more about Coursera for Business](https://www.coursera.org/business?utm_medium=coursera&utm_source=xdp&utm_campaign=website&utm_content=c4b-xdp-thin-card&utm_term=out) ![ logos of Petrobras, TATA, Danone, Capgemini, P\&G and L'Oreal ](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/images/74c8747e8210831049cf88dd4eefe26c.png?auto=format%2Ccompress&dpr=2&blur=200&px=8&max-w=320) ## Build your subject-matter expertise This course is part of the [Bayesian Statistics Specialization](https://www.coursera.org/specializations/bayesian-statistics) When you enroll in this course, you'll also be enrolled in this Specialization. - Learn new concepts from industry experts - Gain a foundational understanding of a subject or tool - Develop job-relevant skills with hands-on projects - Earn a shareable career certificate ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/images/a7c5400e51272c78b710ce9b56fd3178.png?auto=format%2Ccompress&dpr=2&blur=200&px=8&max-w=562) ## There are 4 modules in this course 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. ### Probability and Bayes' Theorem Module 1•3 hours to complete Module details In this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables. #### What's included 8 videos4 readings5 assignments Show info about module content ##### 8 videos•Total 38 minutes - 🎥 Course introduction•4 minutes - 🎥 Lesson 1.1 Classical and frequentist probability•6 minutes - 🎥 Lesson 1.2 Bayesian probability and coherence•3 minutes - 🎥 Lesson 2.1 Conditional probability•4 minutes - 🎥 Lesson 2.2 Bayes' theorem•6 minutes - 🎥 Lesson 3.1 Bernoulli and binomial distributions•5 minutes - 🎥 Lesson 3.2 Uniform distribution•5 minutes - 🎥 Lesson 3.3 Exponential and normal distributions•3 minutes ##### 4 readings•Total 36 minutes - 📖 Module 1 objectives, assignments, and supplementary materials•3 minutes - 📖 Background for Lesson 1•10 minutes - 📖 Supplementary material for Lesson 2•3 minutes - 📖 Supplementary material for Lesson 3•20 minutes ##### 5 assignments•Total 97 minutes - ✍️ Lesson 1: Demonstrate your knowledge•30 minutes - ✍️ Lesson 2: Demonstrate your knowledge•12 minutes - ✍️ Lesson 3.1: Demonstrate your knowledge•30 minutes - ✍️ Lesson 3.2-3.3: Demonstrate your knowledge•10 minutes - ✍️ Module 1 Honors •15 minutes ### Statistical Inference Module 2•3 hours to complete Module details This module introduces concepts of statistical inference from both frequentist and Bayesian perspectives. Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. Lesson 5 introduces the fundamentals of Bayesian inference. Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes’ theorem to update the prior with data to obtain posterior probabilities. This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals. #### What's included 11 videos5 readings4 assignments Show info about module content ##### 11 videos•Total 59 minutes - 🎥 Lesson 4.1 Confidence intervals•5 minutes - 🎥 Lesson 4.2 Likelihood function and maximum likelihood•7 minutes - 🎥 Lesson 4.3 Computing the MLE•3 minutes - 🎥 Lesson 4.4 Computing the MLE: examples•4 minutes - 🎥 Introduction to R•7 minutes - 🎥 Plotting the likelihood in R•5 minutes - 🎥 Plotting the likelihood in Excel•5 minutes - 🎥 Lesson 5.1 Inference example: frequentist•4 minutes - 🎥 Lesson 5.2 Inference example: Bayesian•7 minutes - 🎥 Lesson 5.3 Continuous version of Bayes' theorem•4 minutes - 🎥 Lesson 5.4 Posterior intervals•8 minutes ##### 5 readings•Total 38 minutes - 📖 Module 2 objectives, assignments, and supplementary materials•3 minutes - 📖 Background for Lesson 4•10 minutes - 📖 Supplementary material for Lesson 4•5 minutes - 📖 Background for Lesson 5•10 minutes - 📖 Supplementary material for Lesson 5•10 minutes ##### 4 assignments•Total 74 minutes - ✍️ Lesson 4: Demonstrate your knowledge•8 minutes - ✍️ Lesson 5.1-5.2: Demonstrate your knowledge•30 minutes - ✍️ Lesson 5.3-5.4: Demonstrate your knowledge•30 minutes - ✍️ Module 2 Honors •6 minutes ### Priors and Models for Discrete Data Module 3•2 hours to complete Module details In this module, you will learn methods for selecting prior distributions and building models for discrete data. Lesson 6 introduces prior selection and predictive distributions as a means of evaluating priors. Lesson 7 demonstrates Bayesian analysis of Bernoulli data and introduces the computationally convenient concept of conjugate priors. Lesson 8 builds a conjugate model for Poisson data and discusses strategies for selection of prior hyperparameters. #### What's included 9 videos2 readings4 assignments Show info about module content ##### 9 videos•Total 66 minutes - 🎥 Lesson 6.1 Priors and prior predictive distributions•4 minutes - 🎥 Lesson 6.2 Prior predictive: binomial example•5 minutes - 🎥 Lesson 6.3 Posterior predictive distribution•4 minutes - 🎥 Lesson 7.1 Bernoulli/binomial likelihood with uniform prior•4 minutes - 🎥 Lesson 7.2 Conjugate priors•5 minutes - 🎥 Lesson 7.3 Posterior mean and effective sample size•7 minutes - 🎥 Data analysis example in R•13 minutes - 🎥 Data analysis example in Excel•16 minutes - 🎥 Lesson 8.1 Poisson data•8 minutes ##### 2 readings•Total 13 minutes - 📖 Module 3 objectives, assignments, and supplementary materials•3 minutes - 📖 R and Excel code from example analysis•10 minutes ##### 4 assignments•Total 68 minutes - ✍️ Lesson 6: Demonstrate your knowledge•30 minutes - ✍️ Lesson 7: Demonstrate your knowledge•15 minutes - ✍️ Lesson 8: Demonstrate your knowledge•15 minutes - ✍️ Module 3 Honors •8 minutes ### Models for Continuous Data Module 4•3 hours to complete Module details This module covers conjugate and objective Bayesian analysis for continuous data. Lesson 9 presents the conjugate model for exponentially distributed data. Lesson 10 discusses models for normally distributed data, which play a central role in statistics. In Lesson 11, we return to prior selection and discuss ‘objective’ or ‘non-informative’ priors. Lesson 12 presents Bayesian linear regression with non-informative priors, which yield results comparable to those of classical regression. #### What's included 9 videos5 readings5 assignments Show info about module content ##### 9 videos•Total 69 minutes - 🎥 Lesson 9.1 Exponential data•4 minutes - 🎥 Lesson 10.1 Normal likelihood with variance known•4 minutes - 🎥 Lesson 10.2 Normal likelihood with variance unknown•3 minutes - 🎥 Lesson 11.1 Non-informative priors•8 minutes - 🎥 Lesson 11.2 Jeffreys prior•3 minutes - 🎥 Linear regression in R (Datasets included in Downloads)•17 minutes - 🎥 Linear regression in Excel (Analysis ToolPak)•14 minutes - 🎥 Linear regression in Excel (StatPlus by AnalystSoft)•14 minutes - 🎥 Conclusion•1 minute ##### 5 readings•Total 33 minutes - 📖 Module 4 objectives, assignments, and supplementary materials•3 minutes - 📖 Supplementary material for Lesson 10•10 minutes - 📖 Supplementary material for Lesson 11•5 minutes - 📖 Background for Lesson 12•10 minutes - 📖 R and Excel code for regression•5 minutes ##### 5 assignments•Total 63 minutes - ✍️ Lesson 9: Demonstrate your knowledge•12 minutes - ✍️ Lesson 10: Demonstrate your knowledge•20 minutes - ✍️ Lesson 11: Demonstrate your knowledge•10 minutes - ✍️ Regression: Demonstrate your knowledge•15 minutes - ✍️ Module 4 Honors •6 minutes ### Earn a career certificate Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review. ### Instructor Instructor ratings ## Instructor ratings We asked all learners to give feedback on our instructors based on the quality of their teaching style. OK 4\.6 (525 ratings) ![Herbert Lee](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-instructor-photos.s3.amazonaws.com/38/6ce6e0f5f211e590ed7d08fe585ef8/lee_herbie-300.jpg?auto=format%2Ccompress&dpr=1&w=75&h=75&fit=crop) [Herbert Lee](https://www.coursera.org/instructor/ucsc) University of California, Santa Cruz 1 Course•159,482 learners ### Offered by ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/http://coursera-university-assets.s3.amazonaws.com/50/f80f825a5946daa0b06ea1bc802748/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=38&h=38&fit=fill) [University of California, Santa Cruz](https://www.coursera.org/partners/ucsc) Learn more ## Offered by ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/http://coursera-university-assets.s3.amazonaws.com/50/f80f825a5946daa0b06ea1bc802748/UCSC-Logo-White-reversed-copy.png?auto=format%2Ccompress&dpr=1&w=88&h=88&fit=fill) [University of California, Santa Cruz](https://www.coursera.org/partners/ucsc) UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience. OK ## Explore more from Probability and Statistics Recommended Specializations Related - ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/8d/9466452687403d977f4cfc177fae55/Bayesian3.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50) Status: Free Trial Free Trial U University of California, Santa Cruz [Bayesian Statistics: Techniques and Models](https://www.coursera.org/learn/mcmc-bayesian-statistics) Course - ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/65/c558c0f50211e5a52493c7e6aee302/baysian_statistics.v2a.png?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50) D Duke University [Bayesian Statistics](https://www.coursera.org/learn/bayesian) Course - ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/58/85f3621fac44fc9030ed8420e25127/shutterstock_1642476472.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50) Status: Free Trial Free Trial A Arizona State University [Bayesian Statistical Concepts and Methods](https://www.coursera.org/learn/bayesian-statistical-concepts-and-methods) Course - ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/50/5ab2b9382e4b04bae268aadd5f6058/data-sci-2.jpg?auto=format%2C%20compress%2C%20enhance&dpr=1&w=320&h=204&fit=crop&q=50) Status: Free Trial Free Trial I Illinois Tech [Bayesian Computational Statistics](https://www.coursera.org/learn/illinois-tech-bayesian-computational-statistics) Course Show 8 more ## Why people choose Coursera for their career ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/growth_testimonials/passionate_learner/Felipe_Moitta.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop) ### Felipe M. 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Learner since 2021 "When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go." ![](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera_assets.s3.amazonaws.com/growth_testimonials/passionate_learner/Chaitanya_Anand.png?auto=format%2Ccompress&dpr=1&w=64&h=64&fit=crop) ### Chaitanya A. "Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits." ## Learner reviews 4\.6 3,227 reviews - 5 stars 67\.33% - 4 stars 25\.03% - 3 stars 5\.23% - 2 stars 1\.27% - 1 star 1\.11% Showing 3 of 3227 D DG 4 · Reviewed on Dec 9, 2019 It was a good course for me to get familiar with the new perspective on statistics. Thank you! Maybe, some extended practice exercise at the end of the course would make it even better) M MD 4 · Reviewed on Feb 19, 2020 the notes for the lectures are missing.In my opinion the notes, which includes the video materials could be very useful.the course was good. I learnt some new concepts in bayesian thinking. M MM 4 · Reviewed on Sep 25, 2019 Very clear and informative. Would like a more extensive and combined reference material (PDF, so less need to lookup e.g. definitions of effective sample size for various distributions). 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You should have exposure to the concepts from a basic statistics class (for example, probability, the Central Limit Theorem, confidence intervals, linear regression) and calculus (integration and differentiation), but it is not expected that you remember how to do all of these items. The course will provide some overview of the statistical concepts, which should be enough to remind you of the necessary details if you've at least seen the concepts previously. On the calculus side, the lectures will include some use of calculus, so it is important that you understand the concept of an integral as finding the area under a curve, or differentiating to find a maximum, but you will not be required to do any integration or differentiation yourself. ### What computing resources are expected for this course? Data analysis is done using computer software. This course provides the option of Excel or R. Equivalent content is provided for both options. A very brief introduction to R is provided for people who have never used it before, but this is not meant to be a course on R. Learners using Excel are expected to already have basic familiarity of Excel. ### When will I have access to the lectures and assignments? To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience. ### What will I get if I subscribe to this Specialization? When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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Readable Markdown	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.
Shard	97 (laksa)
Root Hash	1995246928644532097
Unparsed URL	org,coursera!www,/learn/bayesian-statistics s443