{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "05a1be05-80b3-a7d1-903f-a78bd4c00ad8"
      },
      "source": [
        "## New to Kernels? Start here!\n",
        "\n",
        "Whether you're a Pythonista or a useR, there are five ways to use scripts and notebooks to start exploring datasets published on Kaggle using the in-browser code execution environment, Kernels. Fork any of the following kernels to quickly get your analyses off the ground.\n",
        "\n",
        "###Scripts\n",
        "\n",
        "Scripts are a no-frills way to experiment with code. Most users rely on scripts when they want to train machine learning algorithms and write out a prediction file. But you can save data visualizations as well. Study and/or fork either the Python or R script below:\n",
        "\n",
        "* [Python Script][1]\n",
        "* [R Script][2]\n",
        "\n",
        "### Notebooks\n",
        "\n",
        "Notebooks are appealing because they combine narrative text written using easy-to-use markdown syntax with executable code. They create attractive reports that work well for tutorial-style content where code and text are both important. Study and/or fork either the Python or R notebook below:\n",
        "\n",
        "* [Python Notebook][3]\n",
        "* [R Notebook][4]\n",
        "\n",
        "### Rmarkdown\n",
        "\n",
        "Finally, Rmarkdown is perfect for creating visually appealing reports where the author wants more aesthetic control. You can choose to hide code in places where it's unnecessary for the write-up making Rmarkdown a great choice for data journalism articles that emphasize narrative and data visualization. Study or fork this Rmarkdown below (click on the \"code\" tab to see the underlying code):\n",
        "\n",
        "* [Rmarkdown Report][5]\n",
        "\n",
        "## What else?\n",
        "\n",
        "There are a few other helpful things to know about using Kernels:\n",
        "\n",
        "* You can write out a prediction file to make a submission to the competition from any kernel.\n",
        "* Are we missing a package/library/module? Make a request on our [Product Feedback forum][6] and we'll work to get it added.\n",
        "* Plus, don't forget to upvote others' kernels you find helpful! You can find upvoted kernels on the \"Kernels\" tab of your profile for future reference.\n",
        "\n",
        "\n",
        "  [1]: https://www.kaggle.com/mrisdal/digit-recognizer/starter-kernel-python-script\n",
        "  [2]: https://www.kaggle.com/bhanudaybirla/digit-recognizer/digit-recgnizer/code\n",
        "  [3]: https://www.kaggle.com/apapiu/digit-recognizer/t-sne\n",
        "  [4]: https://www.kaggle.com/olhacher/digit-recognizer/multinomial-logit-with-gradient-descent\n",
        "  [5]: https://www.kaggle.com/anthonykenny/digit-recognizer/quick-look-at-the-digits-data\n",
        "  [6]: https://www.kaggle.com/product-feedback"
      ]
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}