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Description
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, youâll learn how Bayesian data analysis works, how it differs from the classical approach, and why itâs an indispensable part of your data science toolbox. Youâll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, youâll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.
Tags
Syllabus
The Bayesian way
-Take your first steps in the Bayesian world. In this chapter, youâll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, youâll build your first Bayesian model to draw conclusions from randomized coin tosses.
Bayesian estimation
-Itâs time to look under the Bayesian hood. Youâll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, youâll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
Bayesian inference
-Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. Youâll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
Bayesian linear regression with pyMC3
-In this final chapter, youâll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, youâll apply what youâve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
-Take your first steps in the Bayesian world. In this chapter, youâll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, youâll build your first Bayesian model to draw conclusions from randomized coin tosses.
Bayesian estimation
-Itâs time to look under the Bayesian hood. Youâll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, youâll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
Bayesian inference
-Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. Youâll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
Bayesian linear regression with pyMC3
-In this final chapter, youâll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, youâll apply what youâve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
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TypeOnline Courses
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ProviderDataCamp
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, youâll learn how Bayesian data analysis works, how it differs from the classical approach, and why itâs an indispensable part of your data science toolbox. Youâll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, youâll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.
Bayesian data analysis is an increasingly popular method of statistical inference, used to determine conditional probability without having to rely on fixed constants such as confidence levels or p-values. In this course, youâll learn how Bayesian data analysis works, how it differs from the classical approach, and why itâs an indispensable part of your data science toolbox. Youâll get to grips with A/B testing, decision analysis, and linear regression modeling using a Bayesian approach as you analyze real-world advertising, sales, and bike rental data. Finally, youâll get hands-on with the PyMC3 library, which will make it easier for you to design, fit, and interpret Bayesian models.
The Bayesian way
-Take your first steps in the Bayesian world. In this chapter, youâll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, youâll build your first Bayesian model to draw conclusions from randomized coin tosses.
Bayesian estimation
-Itâs time to look under the Bayesian hood. Youâll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, youâll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
Bayesian inference
-Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. Youâll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
Bayesian linear regression with pyMC3
-In this final chapter, youâll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, youâll apply what youâve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
-Take your first steps in the Bayesian world. In this chapter, youâll be introduced to the basic concepts of probability and statistical distributions, as well as to the famous Bayes' Theorem, the cornerstone of Bayesian methods. Finally, youâll build your first Bayesian model to draw conclusions from randomized coin tosses.
Bayesian estimation
-Itâs time to look under the Bayesian hood. Youâll learn how to apply Bayes' Theorem to drug-effectiveness data to estimate the parameters of probability distributions using the grid approximation technique, and update these estimates as new data become available. Next, youâll learn how to incorporate prior knowledge into the model before finally practicing the important skill of reporting results to a non-technical audience.
Bayesian inference
-Apply your newly acquired Bayesian data analysis skills to solve real-world business challenges. Youâll work with online sales marketing data to conduct A/B tests, decision analysis, and forecasting with linear regression models.
Bayesian linear regression with pyMC3
-In this final chapter, youâll take advantage of the powerful PyMC3 package to easily fit Bayesian regression models, conduct sanity checks on a model's convergence, select between competing models, and generate predictions for new data. To wrap up, youâll apply what youâve learned to find the optimal price for avocados in a Bayesian data analysis case study. Good luck!
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