LIVE TRAINING: August 17th

12 PM EST

Save 30% : Register Now

Price: $147

Regular price $210, discounted 10%

  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

30% Discount Ends in:

Subscribe and get an additional 10% to 35% off ALL live training session

View Plans

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.

Join the live session with Allen Downey

SAVE 30%

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 superiorityExercise 1

-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.

Upcoming Live Training

September 30th

Network Analysis Made Simple

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

learn more
Open Data Science

Ai+ | ODSC
One Broadway, 14th Floor
Cambridge, MA 02142
admin_aiplus@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google