# Content: References, Learning Guides, Etc.

Change Search Criteria:### Statistics and Probability

Courses / Curricula / References, Learning Guides, Etc. / Self-paced online courseA list of courses from the Khan Academy covering the following topics: Introduction to statistics Analyzing categorical data Displaying and comparing quantitative data Summarizing quantitative data Modeling data distributions Exploring bivariate numerical data Study design Probability Counting, permutations, and combinations Random variables Sampling distributions One-sample confidence intervals One-sample z and t significance tests Two-sample inference for the difference between groups Inference for categorical data (chi-square tests) Advanced regression (inference and … Continue Reading

### Mastering Markdown for GitHub

Cheat Sheets / References, Learning Guides, Etc. / Tutorial“Markdown is a lightweight and easy-to-use syntax for styling all forms of writing on the GitHub platform. What you will learn: How the Markdown format makes styled collaborative editing easy How Markdown differs from traditional formatting approaches How to use Markdown to format text How to leverage GitHub’s automatic Markdown rendering How to apply GitHub’s unique Markdown extensions

### GitHub Markdown Cheatsheet

Cheat Sheets“This is intended as a quick reference and showcase” for using Markdown for text on GitHub. Table of Contents: Headers Emphasis Lists Links Images Code and Syntax Highlighting Tables Blockquotes Inline HTML Horizontal Rule Line Breaks YouTube Videos

### Jupyter Notebook Keyboard Shortcuts

Cheat Sheets / References, Learning Guides, Etc.A list of keyboard shortcuts for both command mode and edit mode of Jupyter notebooks.

### MIT Statistics Cheat Sheet

Cheat Sheets / References, Learning Guides, Etc.A long list of definitions, equations, and examples for common statistical terms and tests, including: Variance Standard Deviation & Error T-tests Chi-Square Tests Probability Distributions

### Machine Learning Libraries Cheat Sheet

Cheat Sheets / References, Learning Guides, Etc.Organized into an interactive ‘periodic table’, this website collates machine learning libraries for R, Julia, Python, C/C++, Java, Scala, big data, computer vision and natural language processing (NLP), and Lua/JS/Clojure.

### Cheatsheet – 11 Steps for Data Exploration in R (with codes)

Cheat Sheets / References, Learning Guides, Etc.“Data Exploration not only uncovers the hidden trends and insights, but also allows you to take the first steps towards building a highly accurate model. Considering the popularity of R Programming and its fervid use in data science, I’ve created a cheat sheet of data exploration stages in R. This cheat sheet is highly recommended for beginners who can perform data exploration faster using these handy codes. All you need to do … Continue Reading

### R Reference Card for Data Mining

Cheat Sheets / References, Learning Guides, Etc.This cheat sheet covers the following aspects in data mining in R: Association Rules & Frequent Itemsets Sequential Patterns Classification & Prediction Regression Clustering Outlier Detection Time Series Analysis Text Mining Social Network Analysis and Graph Mining Spatial Data Analysis Statistics Graphics Data Manipulation Data Access Big Data Parallel Computing Generating Reports Interface to Weka Editors/GUIs Other R Reference Cards RDataMining Website, Package, Twitter & Groups

### RStudio Cheat Sheets

Cheat Sheets / References, Learning Guides, Etc.“These cheat sheets below make it easy to learn about and use some of our favorite packages.” Data Import Cheat Sheet: “reminds you how to read in flat files…,work with the results as tibbles, and reshape messy data with tidyr. Use tidyr to reshape your tables into tidy data, the data format that works the most seamlessly with R and the tidyverse.” Data Transformation Cheat Sheet: “dplyr provides a grammar … Continue Reading

### PySpark Cheat Sheet: Spark in Python

Cheat Sheets / References, Learning Guides, Etc.“This PySpark cheat sheet covers the basics, from initializing Spark and loading your data, to retrieving RDD information, sorting, filtering and sampling your data. But that’s not all. You’ll also see that topics such as repartitioning, iterating, merging, saving your data and stopping the SparkContext are included in the cheat sheet.”

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