TheBenefitsOfMarkdown.ipynb

{"nbformat_minor": 2, "cells": [{"source": "# Transparent Data Analysis\n\n## Using either Rmarkdown or Jupyter\n", "cell_type": "markdown", "metadata": {"collapsed": true, "slideshow": {"slide_type": "slide"}}}, {"source": "# Common themes\n\n+ Code + presentation means repeatable by others\n+ Slide format \u00ef\u0083\u00a8 publication quality output\n+ Makes complex algorithms accessible\n+ Extremely strong process support\n+ Testing, continuous development and delivery\n+ Private/Public version control (Git/GitHub)\n+ Development is by (data) engineers and scientists\n+ Consumption can be anyone\n", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}, {"source": "# Online examples\n+ Rmarkdown http://rmarkdown.rstudio.com/gallery.html\n+ Jupyter Kaggle Kernels https://www.kaggle.com/kernels \n     - Eg https://www.kaggle.com/elisayao/d/aaron7sun/stocknews/visualization\n+ Microsoft Azure https://notebooks.azure.com/ \n- Eg https://notebooks.azure.com/n/7eDnyhnUeBk/notebooks/CNTK_101_LogisticRegression.ipynb\n+ Google DataLab https://cloud.google.com/datalab/ \n", "cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "3.5.1", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython3", "codemirror_mode": {"version": 3, "name": "ipython"}}, "celltoolbar": "Slideshow"}}