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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
- Statistical Methods
- Python Programming
- Sampling (Statistics)
- Simulations
- Statistics
- Data Science
- Model Evaluation
- Jupyter
- Probability
- Logistic Regression
- Regression Analysis
- Bayesian Statistics
- Databricks
- Probability Distribution
- Applied Machine Learning
- Predictive Modeling
- Statistical Analysis
- Statistical Programming
- Statistical Modeling
- Markov Model
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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
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Financial aid available,