Introduction to Computational Statistics for Data Scientists Specialization
Practical Bayesian Inference. A conceptual understanding of the techniques and the tools used to perform scalable Bayesian inference in practice with PyMC3.
Skills you will gain
About this Specialization
Applied Learning Project
Implement Distributions in Python and visualize it statically using Matplotlib or Seaborn and interactively using Plot.ly.
Implement Monte Carlo Sampling algorithms in Python.
Learn the basics of PyMC3 for various Bayesian modeling including Linear Regression, Hierarchical Regression, Classification, Robust models and assessing the quality of models.
Use PyMC3 to model the disease dynamics of and infer the parameters of an SIR model of COVID-19 from real-world data.
How the Specialization Works
A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.
Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.
Earn a Certificate
When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
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