Online Course – Certified Professional Internship in Computational Visualization of Statistics for Data Scientists from Google, Databricks

Internship – 3-part course series

The goal of the course series is to teach the fundamentals of computational statistics to enable inference for students or new data scientists. The courses do not cover the fundamentals of statistics and probability, nor do they cover frequentist statistical techniques.

Topics covered:

• Fundamentals of Hyasian Statistics and Probability
• Understanding Hyasian Inference and How It Works
• Minimal toolset and knowledge required to perform Haysian inference in Python:
• NumPy
• Pandas
• Scipy
• Matplotlib
• Seaborn
• Plot.ly
• An advanced Python framework for performing Haysian inference: PyMC3

Course content:

• Introduction to Haysian statistics: fundamentals of probability, Haysian models, and inference.
• Introduction to Monte Carlo Methodologies: Lectures on making inferences when exact calculations are not possible.
• PyMC3 for Hyasian Modeling and Inference: Applying PyMC3 to Real-Life Problems.

Practical Learning Project:

• Implementing distributions in Python and visualizing them statically using Matplotlib or Seaborn and interactively using Plot.ly.
• Applying Monte Carlo sampling algorithms in Python.
• Learn PyMC3 basics for various Haysian models including linear regression, hierarchical regression, classification, robust models, and model quality assessment.
• Using PyMC3 to visualize COVID-19 disease dynamics and infer SIR model parameters from real-world data.