.. math essentials main file

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Mathematical Essentials of Data Science
=============================================

If we use a transportation metaphor we might say that statistics and
`machine learning <https://en.wikipedia.org/wiki/Machine_learning>`_
are the vehicles that take us to both new and familiar places.
We could also think of programming and `software engineering
<https://en.wikipedia.org/wiki/Software_engineering>`_ as the roads
and infrastructure necessary to make travel even possible.  Linear
algebra on the other hand aligns well with the motors and engines that
propel our vehicles.

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Most of us seldom think about *what is under the hood* as we hum
steadily along a nicely paved road, but success as a data scientist
requires at least a fundamental understanding.  Here we present the
essentials of `linear algebra
<https://en.wikipedia.org/wiki/Linear_algebra>`_ with the goal that
you become prepared for an educational experience in data science.
You may even come to believe that viewing math through the lens of
matrices and vectors **makes things easier**.

.. important:: The principal focus of this contents is to introduce
   numeric computing and **linear algebra** through Python.  A portion
   of the second day will provide a basis for the **calculus** that
   you will need as a data scientist.


.. toctree::
   :maxdepth: 1
   :caption: Day 1:

   introduction
   matrix-operations
   linear-algebra-1

|
   
.. toctree::
   :maxdepth: 1
   :caption: Day 2:

   linear-algebra-2	     
   matrix-decomposition
   pca-and-svd

|


.. toctree::
   :maxdepth: 1
   :caption: Appendices:

   helpful-math
   calculus-1
   sympy
   install-python	     
   numpy-commands
   extra-numpy
   references