In this hands on guest post you will learn how to carry out probabilistic programming (e.g., Bayesian Statistics) using python, ArViz, and PyMC3.

9 Data Visualization Techniques You Should Learn in Python - Erik Marsja

9 Data Visualization Techniques You Should Learn in Python - Erik Marsja

9 Data Visualization Techniques You Should Learn in Python - Erik Marsja

9 Data Visualization Techniques You Should Learn in Python - Erik Marsja

Statistics Cheat Sheet Roth

Probablistic programming is an expressive and flexible way to build Bayesian statistical models in code. It's an entirely different mode of programming that involves using stochastic variables defined using probability distributions instead of concrete, deterministic values. Stochastic variables can be composed together in expressions and functions, just like in normal

Variance explained the easy way: see how low variance keeps data tight while high variance spreads it wide. Understand what variance really measures, why it matters for spread and standard deviation, and how to compute it in seconds. Perfect for stats students and data learners. Free, no signup needed. #Statistics #DataAnalysis

Correlation matrices can be visualized using correlograms! Learn how to do this plot using Seaborn and Python. #visualization

Create nice data visualizations using Seaborn in Python. Here, we learned how to increase the plot size.

Learn the most important probability distributions and visualization techniques used in data science, including Normal, Uniform, Exponential, Poisson, Binomial, Skewed, Bimodal, and Multimodal distributions, plus Histograms, KDE Plots, QQ Plots, and ECDF Plots. A practical visual guide for data science, statistics, and machine learning learners. #DataScience #Statistics #Probability #DistributionAnalysis #NormalDistribution #PoissonDistribution #BinomialDistribution #DataVisualization #MachineLearning #EDA #KDEPlot #QQPlot #ECDF #DataAnalytics #StatisticalAnalysis