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Updated for PyData Seattle tutorial
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‎1_Introduction.ipynb

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‎2_Data_Science_Basic_Concepts.ipynb

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‎3_Python_Basic_Concepts.ipynb

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‎4_Python_Simple_Decision_Tree.ipynb

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‎5_Next_Steps.ipynb

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{
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"worksheets": [
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Python for Data Science"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[Joe McCarthy](http://interrelativity.com/joe), \n",
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"*Director, Analytics & Data Science*, [Atigeo, LLC](http://atigeo.com)"
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]
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},
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"from IPython.display import display, Image, HTML"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Next steps"
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]
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},
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{
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"metadata": {},
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"source": [
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"Python for Data Science"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[Joe McCarthy](http://interrelativity.com/joe), \n",
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"*Director, Analytics & Data Science*, [Atigeo, LLC](http://atigeo.com)"
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]
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},
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},
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{
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"cell_type": "heading",
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"metadata": {},
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"source": [
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"5. Next steps"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are a variety of Python libraries - e.g., [Scikit-Learn](http://scikit-learn.org/) and [xPatterns](http://atigeo.com/technology/) - 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",
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"\n",
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"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",
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"\n",
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"* [Tentative NumPy Tutorial](http://wiki.scipy.org/Tentative_NumPy_Tutorial)\n",
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"* [SciPy Tutorial](http://docs.scipy.org/doc/scipy/reference/tutorial/)\n",
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"* [Matplotlib PyPlot Tutorial](http://matplotlib.org/1.3.1/users/pyplot_tutorial.html)\n",
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"* [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",
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"\n",
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"There are many machine learning or data science resources that may be useful to help you continue the journey. Here is a sampling:\n",
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"\n",
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"* [An introduction to machine learning with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n",
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"* Olivier Grisel's Strata 2014 tutorial, [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial)\n",
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"* Kaggle's [Getting Started With Python For Data Science](http://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience)\n",
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"* Coursera's [Introduction to Data Science](https://www.coursera.org/course/datasci)\n",
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"\n",
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"Please feel free to contact the author ([Joe McCarthy](mailto:[email protected]?subject=Python for Data Science)) to suggest additional resources."
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]
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},
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{
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"cell_type": "heading",
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"level": 3,
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"metadata": {},
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"source": [
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"Navigation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Notebooks in this primer:\n",
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"\n",
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"* [1. Introduction](1_Introduction.ipynb)\n",
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"* [2. Data Science: Basic Concepts](2_Data_Science_Basic_Concepts.ipynb)\n",
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"* [3. Python: Basic Concepts](3_Python_Basic_Concepts.ipynb)\n",
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"* [4. Using Python to Build and Use a Simple Decision Tree Classifier](4_Python_Simple_Decision_Tree.ipynb)\n",
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"* **5. Next Steps** (*you are here*)"
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]
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}
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],
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"metadata": {}
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"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",
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"\n",
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"* [Tentative NumPy Tutorial](http://wiki.scipy.org/Tentative_NumPy_Tutorial)\n",
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"* [SciPy Tutorial](http://docs.scipy.org/doc/scipy/reference/tutorial/)\n",
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"* [Matplotlib PyPlot Tutorial](http://matplotlib.org/1.3.1/users/pyplot_tutorial.html)\n",
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"* [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",
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"\n",
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"There are many machine learning or data science resources that may be useful to help you continue the journey. Here is a sampling:\n",
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"\n",
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"* Scikit-learn's tutorial, [An introduction to machine learning with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n",
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"* 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",
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"* Kaggle's [Getting Started With Python For Data Science](http://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience)\n",
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"* Coursera's [Introduction to Data Science](https://www.coursera.org/course/datasci)\n",
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"* Olivier Grisel's Strata 2014 tutorial, [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial)\n",
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"\n",
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"Please feel free to contact the author ([Joe McCarthy](mailto:[email protected]?subject=Python for Data Science)) to suggest additional resources."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Navigation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Notebooks in this primer:\n",
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"\n",
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"* [1. Introduction](1_Introduction.ipynb)\n",
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"* [2. Data Science: Basic Concepts](2_Data_Science_Basic_Concepts.ipynb)\n",
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"* [3. Python: Basic Concepts](3_Python_Basic_Concepts.ipynb)\n",
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"* [4. Using Python to Build and Use a Simple Decision Tree Classifier](4_Python_Simple_Decision_Tree.ipynb)\n",
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"* **5. Next Steps** (*you are here*)"
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]
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