


Introduction to Computational Statistics for Data Scientists Specialization
Practical Bayesian Inference. A conceptual understanding of the techniques and the tools used to perform scalable Bayesian inference in practice with PyMC3.

Instructor: Dr. Srijith Rajamohan
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What you'll learn
The basics of Bayesian modeling and inference.
A conceptual understanding of the techniques used to perform Bayesian inference in practice.
Learn how to use PyMC3 to solve real-world problems.
Skills you'll gain
- Regression Analysis
 - Bayesian Statistics
 - Mathematical Software
 - Statistical Programming
 - Statistical Analysis
 - Statistical Modeling
 - Statistical Inference
 - Statistics
 - Python Programming
 - Sampling (Statistics)
 - Databricks
 - Data Science
 - Probability Distribution
 - Classification And Regression Tree (CART)
 - Simulations
 - Markov Model
 - Jupyter
 - Probability
 - Statistical Visualization
 - Predictive Modeling
 
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Frequently asked questions
This specialization should take about 3 months to complete
Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
The courses should ideally be taken in the following order
Iintroduction to Probability and Distributions
Bayesian Inference with MCMC
Iintroduction to PyMC3 with applications
More questions
Financial aid available,