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Moved statistical inference section below scikit-learn section.

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Donne Martin 2015-07-04 08:36:49 -04:00
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README.md
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@ -21,10 +21,10 @@ This repo is a collection of IPython Notebooks I reference while working with da
* [amazon web services](#aws) * [amazon web services](#aws)
* [kaggle-and-business-analyses](#kaggle-and-business-analyses) * [kaggle-and-business-analyses](#kaggle-and-business-analyses)
* [scikit-learn](#scikit-learn) * [scikit-learn](#scikit-learn)
* [statistical-inference-scipy](#statistical-inference-scipy)
* [pandas](#pandas) * [pandas](#pandas)
* [matplotlib](#matplotlib) * [matplotlib](#matplotlib)
* [numpy](#numpy) * [numpy](#numpy)
* [statistical-inference-scipy](#statistical-inference-scipy)
* [python-data](#python-data) * [python-data](#python-data)
* [command lines](#commands) * [command lines](#commands)
* [misc](#misc) * [misc](#misc)
@ -114,6 +114,20 @@ IPython Notebook(s) demonstrating scikit-learn functionality.
| [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Gaussian mixture models. | | [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Gaussian mixture models. |
| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Validation and model selection. | | [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Validation and model selection. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png">
</p>
## statistical-inference-scipy
| Notebook | Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |
| [effect_size](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/effect_size.ipynb) | Effect size. |
| [sampling](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/numpy.ipynb) | Random sampling. |
| [hypothesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/numpy.ipynb) | Hypothesis testing. |
<br/> <br/>
<p align="center"> <p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/pandas.png"> <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/pandas.png">
@ -154,20 +168,6 @@ IPython Notebook(s) demonstrating NumPy functionality.
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [numpy](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. | | [numpy](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png">
</p>
## statistical-inference-scipy
| Notebook | Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |
| [effect_size](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/effect_size.ipynb) | Effect size. |
| [sampling](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/numpy.ipynb) | Random sampling. |
| [hypothesis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scipy/numpy.ipynb) | Hypothesis testing. |
<br/> <br/>
<p align="center"> <p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/python.png"> <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/python.png">