5_Next_Steps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Joe McCarthy](http://interrelativity.com/joe), \n",
    "*Director, Analytics & Data Science*, [Atigeo, LLC](http://atigeo.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import display, Image, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Next steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a variety of Python libraries - e.g., [Scikit-Learn](http://scikit-learn.org/) - for building more full-featured decision trees and other types of models based on a variety of machine learning algorithms. Hopefully, this primer will have prepared you for learning how to use those libraries effectively.\n",
    "\n",
    "Many Python-based machine learning libraries use other external Python libraries such as [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/scipylib/), [Matplotlib](http://matplotlib.org/) and [pandas](http://pandas.pydata.org/). There are tutorials available for each of these libraries, including the following:\n",
    "\n",
    "* [Tentative NumPy Tutorial](http://wiki.scipy.org/Tentative_NumPy_Tutorial)\n",
    "* [SciPy Tutorial](http://docs.scipy.org/doc/scipy/reference/tutorial/)\n",
    "* [Matplotlib PyPlot Tutorial](http://matplotlib.org/1.3.1/users/pyplot_tutorial.html)\n",
    "* [Pandas Tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) (especially [10 Minutes to Pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html))\n",
    "\n",
    "There are many machine learning or data science resources that may be useful to help you continue the journey. Here is a sampling:\n",
    "\n",
    "* Scikit-learn's tutorial, [An introduction to machine learning with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n",
    "* Kevin Markham's video series (on the Kaggle blog), [An introduction to machine learning with scikit-learn](http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/)\n",
    "* Kaggle's [Getting Started With Python For Data Science](http://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience)\n",
    "* Coursera's [Introduction to Data Science](https://www.coursera.org/course/datasci)\n",
    "* Olivier Grisel's Strata 2014 tutorial, [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial)\n",
    "\n",
    "Please feel free to contact the author ([Joe McCarthy](mailto:joe@interrelativity.com?subject=Python for Data Science)) to suggest additional resources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Navigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks in this primer:\n",
    "\n",
    "* [1. Introduction](1_Introduction.ipynb)\n",
    "* [2. Data Science: Basic Concepts](2_Data_Science_Basic_Concepts.ipynb)\n",
    "* [3. Python: Basic Concepts](3_Python_Basic_Concepts.ipynb)\n",
    "* [4. Using Python to Build and Use a Simple Decision Tree Classifier](4_Python_Simple_Decision_Tree.ipynb)\n",
    "* **5. Next Steps** (*you are here*)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}