General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

SF Brigade's Data Science Working Group.

Explore "Statistics" and "Probability Theory" Concepts and Their Implementations in "Python"

Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descri…

Exploratory, Inferential and Predictive data analysis. Feel free to show your ❤️ by giving a star ⭐

Hypothesis and statistical testing in Python

Parametric and non-parametric statistical tests

Utilizing Kaggle Data and Real-World Data for Data Science and Prediction in Python, R, Excel, Power BI, and Tableau.

A Library for Conformal Hyperparameter Tuning

New dedicated repo! This marketing data science app helps brands statistically identify their organic -and influential- ambassadors, thereby nullifying the need for paid brand ambassadors. :)

A MATLAB package for multivariate permutation testing and effect size measurement

Introduction to Statistics Stanford University

Visualise the Results of Inferential Statistics using 'ggplot2'

Statistics for Data Science and Machine Learning Handwritten Notes

This repository has scripts and other files that are part of the lecture notes and assignments of the course "Advanced Statistical Inference" taught at FME, UPC Barcelonatech.

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, you will be working to understand the results of an A/B test run by an e-commerce website. Your goal is to work through this notebook to help the company understand if th…

A poisson e-test for python.

Biomedical Feature Selection for Machine Learning Models

Ensemble Empirical Mode Decomposition Significance Test

as taught in 2011 MIT 6.00SC Introduction to Computer Science and Programming