LIVE TRAINING: August 17th
12 PM EST
Price: $189
Regular price $210, discounted 10%
4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion
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Meet Your Instructor
Allen Downey
Allen Downey is a Professor of Computer Science at Olin College of Engineering in Needham, MA. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. Prof Downey has taught at Colby College and Wellesley College, and in 2009 he was a Visiting Scientist at Google. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Course Overview
Bayesian methods are powerful tools for using data to answer questions and guide decision making under uncertainty. This workshop introduces PyMC, which is a Python library for Bayesian inference. We will use PyMC to estimate proportions and rates, and use those estimates to generate predictions. These methods have applications in business, science, and engineering.
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SAVE 10%Course Outline
Module 1: Introduction
-Prerequisites and goals
-Why estimate proportions? Example applications
-Why estimate rates? Example applications.
– Introduction to Jupyter on Colab
Module 2:Notebook 1
-Estimating rates with a grid algorithm
-Estimating rates with PyMC
-Probability of superiority
-Exercise 1
Module 3: Notebook 2
-The posterior predictive distribution
-Generating probabilistic predictions
-Exercise 2
Module 4: Notebook 3
-Estimating proportions with a grid algorithm
-Estimating proportions with PyMC
-The multi-armed bandit problem
-Exercise 3
Module 5: Notebook 4
-Making the model hierarchical
-Science example: the ADHD problem
-Exercise 4
Module 6: Outro
-Summary
-Overview of additional problems
-Next steps and further reading
Key Details
DATE
TIME:
DURATION:
LEVEL:
AUGUST 17TH, 2021
TIME: 12 PM EST, 9 AM PST
4 HOURS
INTERMEDIATE
Prerequisites
Familiarity with Python at an intermediate level.
You should be familiar with basic probability, especially Bayes’s Theorem. As preparation, you might want to read Chapters 1-3 of Think Bayes, or review the Bayesian Decision Analysis workshop.
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