Probabilistic Programming and Bayesian Computing with PyMC Course Overview

Probabilistic Programming and Bayesian Computing with PyMC Course Overview

Unlock the power of Probabilistic Programming and Bayesian Computing with PyMC with our comprehensive 3-day course. You’ll start with basic concepts such as Bayesian Statistics and PyMC syntax, moving on to more advanced topics like Markov Chain Monte Carlo (MCMC) Methods, Hierarchical Modeling, and Variational Inference. Each session is designed to be hands-on, ensuring you can build and validate complex Bayesian models. Dive into practical applications like Time Series Modeling and Bayesian A/B Testing, and complete an end-to-end project to solidify your learning. Suitable for professionals aiming to make data-driven decisions in fields like marketing, healthcare, and finance.

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1,150 (USD)
  • Live Training (Duration : 24 Hours)
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  • Live Training (Duration : 24 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

Minimum Required Prerequisites for the Probabilistic Programming and Bayesian Computing with PyMC Course:


To successfully undertake the Probabilistic Programming and Bayesian Computing with PyMC course, students should have the following foundational knowledge:


  • Basic Understanding of Statistics: Familiarity with fundamental statistical concepts such as probability distributions, mean, variance, and standard deviation.
  • Introduction to Python Programming: Proficiency in Python programming, including basic syntax, data structures (lists, dictionaries, etc.), and essential libraries like NumPy and Pandas.
  • Preliminary Knowledge of Bayesian Statistics: An introductory understanding of Bayesian concepts such as Bayes' theorem, prior, likelihood, and posterior distributions.

These prerequisites will ensure that students can fully engage with the course materials, hands-on sessions, and advanced topics covered over the three days.


Target Audience for Probabilistic Programming and Bayesian Computing with PyMC

1. Introduction:
This course dives into probabilistic programming with PyMC, designed for professionals seeking to master Bayesian statistics and advanced modeling techniques.


2. Target Audience and Job Roles:


  • Data Scientists
  • Machine Learning Engineers
  • Statisticians
  • Data Analysts
  • Research Scientists
  • Quantitative Analysts
  • Academics and Educators in Data Science
  • Financial Analysts
  • Healthcare Data Analysts
  • Software Engineers specialized in AI/ML
  • Business Intelligence Professionals
  • Product Managers with a focus on data-driven decision-making
  • Economists
  • Bioinformaticians
  • Marketing Analysts specializing in A/B testing and consumer behavior
  • Operations Research Analysts


Learning Objectives - What you will Learn in this Probabilistic Programming and Bayesian Computing with PyMC?

Course Introduction

The "Probabilistic Programming and Bayesian Computing with PyMC" course offers comprehensive training on probabilistic modeling and Bayesian inference using the PyMC library, covering foundational theories, advanced modeling techniques, and real-world applications.

Learning Objectives and Outcomes

  • Understand the fundamental concepts of probabilistic programming and its significance.
  • Gain proficiency in Bayesian statistics, including the application of Bayes' Theorem.
  • Learn how to set up and utilize the PyMC library for probabilistic modeling.
  • Develop skills in defining random variables, distributions, and constructing Bayesian models.
  • Master Markov Chain Monte Carlo (MCMC) methods and their applications in sampling.
  • Build and analyze hierarchical models in PyMC for complex real-world scenarios.
  • Perform model diagnostics to ensure MCMC convergence and validate models.
  • Explore advanced PyMC features, including custom distributions and PyMC3 extensions.
  • Implement variational inference techniques and compare them with MCMC methods.
  • Apply Bayesian methods to A/B testing, time series analysis, and decision-making processes.

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