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Introduction to Bayesian statistics in Python (online)

This course empowers data professionals to use a Bayesian Statistics approach in their workflow using the large set of tools available in Python.

About this course

This course is a collaboration between UTS and Coder Academy, aimed at data professionals with some prior experience with Python programming, and general knowledge of statistics.

Bayesian statistics is closely tied to probabilistic inference - the task of deriving the probability of one or more random variables taking a specific value or set of values - and allows data analysts and scientists to update their models not only with new evidence, but also with new beliefs expressed as probabilities.

Data scientists who can model the likelihood that a new product or service will be successful, and also update that model to account for new data and new beliefs, can have a large impact at their organisations.

Being able to create algorithms that update themselves with each new piece of feedback (i.e. new customers, new purchases, new survey responses, etc.), is a valuable skill to have in today’s technologically-driven business landscape.

This course aims to provide you with the necessary tools to develop and evaluate your own models using a powerful branch of statistics, Bayesian Statistics.

For more information on the UTS & Coder Academy course collaboration, or to contact the Coder Academy team directly, follow this link.

Course structure

This intensive course is conducted over two three-hour evening sessions and covers:

  • Intro to Bayesian Statistics
    • Short history of Bayesian Statistics
    • Bayes Theorem
    • Bayesian vs Frequentist Statistics
    • Applications of Bayesian Statistics
  • Tools for Bayesian Inference
    • PyMC3
    • Tensorflow probability
  • Intro to probability in Python
  • Bayesian inference
  • Markov Chain Monte Carlo (MCMCs)
  • Interpretation and reporting of results
  • Making sure anyone can reproduce our results using the same data


Learning outcomes

  • Explain the main differences between Bayesian statistics and the classical (frequentist) approach
  • Articulate when the Bayesian approach is the preferred or the most useful choice for a problem
  • Conduct your own analysis using the PyMC package in Python
  • Understand how to create reproducible results from your analysis

Who is this course for?

This course is designed for professionals, data analysts or researchers with a working knowledge of Python who need to make decisions in uncertain scenarios - participants might include business analysts, consultants, data analysts, digital marketers, data journalists, librarians, and researchers.



13 October




6 hrs

Meet the Expert

Ramon Perez

Ramon Perez

Ramon is a data scientist and instructor at Coder Academy and a research associate at INSEAD. He works at the intersection of education, data science, and research in the areas of entrepreneurship and strategy. He has previously worked in consumer behaviour and development economics research in professional and academic settings, helping multinational companies understand their customers better and developing new methods to study the levels of financial literacy across the globe. Ramon holds a BSc in economics, finance and marketing, and an MA in Economics. In his spare time, he enjoys cycling, baseball, CrossFit, and finding new coffee shops around Sydney.

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Book a session

Tue 13 Oct 2020 -
Thu 15 Oct 2020
Expert: Ramon Perez
  • Online via Canvas virtual classroom
  • Online
  • 2 sessions, 6 hours total

This online course runs for a small group of up to 50 participants, and registration for each session closes at midnight the day prior to the class.

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