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Bayesian Statistics

Master probabilistic programming and inference. Learn to update beliefs with data using Markov Chain Monte Carlo (MCMC) methods and the PyMC library.

Bayesian statistics offers a powerful alternative to traditional frequentist methods by incorporating prior knowledge and updating beliefs as new data arrives. This advanced course teaches you to build probabilistic models that quantify uncertainty in real-world scenarios. You will master the mathematics of Bayes' Theorem, implement Markov Chain Monte Carlo (MCMC) algorithms, and use the PyMC library to solve complex inference problems. This approach is essential for decision-making under uncertainty, A/B testing, and risk modeling.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
Stats Dept
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Complete Course Syllabus

  • 1
    Bayesian Thinking
    Understanding priors, likelihoods, and updating beliefs with data.
  • 2
    Probabilistic Models
    Defining stochastic variables and distributions in PyMC code.
  • 3
    MCMC Algorithms
    How Metropolis-Hastings and NUTS samplers navigate probability space.
  • 4
    Model Diagnostics
    Checking convergence with trace plots and r-hat statistics.
  • 5
    Hierarchical Models
    Building multi-level models that share information across groups.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$120k - $160k

Career Paths

Quantitative Analyst $130k-$180k
Data Scientist $120k-$160k
Research Scientist $110k-$150k

What You Will Learn

Build complex probabilistic models to quantify uncertainty in data
Implement MCMC algorithms to sample from posterior distributions
Perform Bayesian A/B testing for more interpretable results
Use PyMC to define stochastic variables and run inference
Diagnose model convergence using trace plots and statistics

Skills You Will Gain

Bayesian Inference Probabilistic Programming MCMC PyMC Statistical Modeling

Who Is This For

Data Scientists
Statisticians
Quantitative Researchers

Prerequisites

Calculus
Python Programming

Bayesian Statistics FAQs

Is this hard?

Conceptually different from standard stats, but we build intuition.

Why Bayesian?

Better handling of uncertainty and small data samples.

Math required?

Calculus and probability theory are necessary foundations.

Industry usage?

Used heavily in finance, pharma, and marketing science.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

PyMC
ArviZ
Python
Jupyter
NumPy

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