Add new numpy notebooks to README

Author: donnemartin

README.md

@@ -214,6 +214,16 @@ IPython Notebook(s) demonstrating NumPy functionality.
 | Notebook | Description |
 |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [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. |
+| [Introduction-to-NumPy](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.00-Introduction-to-NumPy.ipynb) | Introduction to NumPy. |
+| [Understanding-Data-Types](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.01-Understanding-Data-Types.ipynb) | Learn about data types in Python. |
+| [The-Basics-Of-NumPy-Arrays](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.02-The-Basics-Of-NumPy-Arrays.ipynb) | Learn about the basics of NumPy arrays. |
+| [Computation-on-arrays-ufuncs](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.03-Computation-on-arrays-ufuncs.ipynb) | Learn about computations on NumPy arrays: universal functions. |
+| [Computation-on-arrays-aggregates](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.04-Computation-on-arrays-aggregates.ipynb) | Learn about aggregations: min, max, and everything in between in NumPy. |
+| [Computation-on-arrays-broadcasting](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.05-Computation-on-arrays-broadcasting.ipynb) | Learn about computation on arrays: broadcasting in NumPy. |
+| [Boolean-Arrays-and-Masks](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.06-Boolean-Arrays-and-Masks.ipynb) | Learn about comparisons, masks, and boolean logic in NumPy. |
+| [Fancy-Indexing](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.07-Fancy-Indexing.ipynb) | Learn about fancy indexing in NumPy. |
+| [Sorting](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.08-Sorting.ipynb) | Learn about sorting arrays in NumPy. |
+| [Structured-Data-NumPy](http://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/02.09-Structured-Data-NumPy.ipynb) | Learn about structured data: NumPy's structured arrays. |
 
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 <p align="center">
@@ -346,6 +356,7 @@ Notebooks tested with Python 2.7.x.
 
 * [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) by Wes McKinney
 * [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakevdp/sklearn_pycon2015) by Jake VanderPlas
+* [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook) by Jake VanderPlas
 * [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel
 * [Statistical Interference Using Computational Methods in Python](https://github.com/AllenDowney/CompStats) by Allen Downey
 * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) by Aymeric Damien