Bayesian InferencePython ProgrammingMCMCPyMC3ScipyvisualizationStatisticsBayesianScikit-LearnMonte Carlo Method
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.
Offered By
What you will learn
The basics of Bayesian modeling and inference.
A conceptual understanding of the techniques used to perform Bayesian inference in practice.
Learn how to use PyMC3 to solve real-world problems.
The basics of Probability, Bayesian statistics, modeling and inference.
Skills you will gain
About this Specialization
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The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:
The basics of Bayesian statistics and probability
Understanding Bayesian inference and how it works
The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly
A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3
With this goal in mind, the content is divided into the following three main sections (courses).
Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1.
Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2.
PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios.
The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.
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.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
Approximately 3 months to complete
Suggested pace of 4 hours/week
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
Approximately 3 months to complete
Suggested pace of 4 hours/week
English
Subtitles: English
How the Specialization Works
Take Courses
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.
Hands-on Project
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.
There are 3 Courses in this Specialization
Introduction to Bayesian Statistics
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Bayesian Inference with MCMC
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Introduction to PyMC3 for Bayesian Modeling and Inference
The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Offered by
Databricks
Databricks is the data and AI company. Founded by the creators of Apache Spark™, Delta Lake and MLflow, organizations like Comcast, Condé Nast, Nationwide and H&M rely on Databricks’ open and unified platform to enable data engineers, scientists and analysts to collaborate and innovate faster.
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