R Package to Quickly and Neatly Summarize Data

A Python library for calculating a large variety of metrics from text

Analytics & Machine Learning R Sidekick

An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

An open source book to learn data science, data analysis and machine learning, suitable for all ages!

R package to create "Table 1", description of baseline characteristics with or without propensity score weighting

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…

Easy and thorough description of datasets

Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.

Personal repository of data science demonstrations and references

I will update this repository to learn Machine learning with python with statistics content and materials

The furniture R package contains table1 for publication-ready simple and stratified descriptive statistics, tableC for publication-ready correlation matrixes, and other tables #rstats

Kai Sheng Teh - Udacity Data Analyst Nanodegree

A small collection of lesser-known statistical measures

Generate descriptive statistics

R package to easily build publication-ready univariate or bivariate descriptive tables from a data set.

Statistics for Data Science and Machine Learning Handwritten Notes

Introduction to Statistics Stanford University

This repository should help people that would like to code in R and work with the National Health and Nutrition Examination Survey (NHANES). Some topics corved are SQL , logistic regression.... etc

A Crystal shard to perform descriptive statistics and sampling on popular distributions