The following link should solve all problems with Windows and SciPy; just choose the appropriate download. I was able to pip install the package with no problems. Every other solution I have tried gave me big headaches.

Source: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy

Command:

 pip install [Local File Location]\[Your specific file such as scipy-0.16.0-cp27-none-win_amd64.whl]

This assumes you have installed the following already:

1. Install Visual Studio 2015/2013 with Python Tools
   (Is integrated into the setup options on install of 2015)

2. Install Visual Studio C++ Compiler for Python
   Source: http://www.microsoft.com/en-us/download/details.aspx?id=44266
   File Name: VCForPython27.msi

3. Install Python Version of choice
   Source: python.org
   File Name (e.g.): python-2.7.10.amd64.msi

My python's version is 2.7.10, 64-bits Windows 7.

1. Download scipy-0.18.0-cp27-cp27m-win_amd64.whl from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
2. Open cmd
3. Make sure scipy-0.18.0-cp27-cp27m-win_amd64.whl is in cmd's current directory, then type pip install scipy-0.18.0-cp27-cp27m-win_amd64.whl.

It will be successful installed.

The solution to the absence of BLAS/LAPACK libraries for SciPy installations on Windows 7 64-bit is described here:

http://www.scipy.org/scipylib/building/windows.html

Installing Anaconda is much easier, but you still don't get Intel MKL or GPU support without paying for it (they are in the MKL Optimizations and Accelerate add-ons for Anaconda - I'm not sure if they use PLASMA and MAGMA either). With MKL optimization, numpy has outperformed IDL on large matrix computations by 10-fold. MATLAB uses the Intel MKL library internally and supports GPU computing, so one might as well use that for the price if they're a student ($50 for MATLAB + $10 for the Parallel Computing Toolbox). If you get the free trial of Intel Parallel Studio, it comes with the MKL library, as well as C++ and FORTRAN compilers that will come in handy if you want to install BLAS and LAPACK from MKL or ATLAS on Windows:

http://icl.cs.utk.edu/lapack-for-windows/lapack/

Parallel Studio also comes with the Intel MPI library, useful for cluster computing applications and their latest Xeon processsors. While the process of building BLAS and LAPACK with MKL optimization is not trivial, the benefits of doing so for Python and R are quite large, as described in this Intel webinar:

https://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python

Anaconda and Enthought have built businesses out of making this functionality and a few other things easier to deploy. However, it is freely available to those willing to do a little work (and a little learning).

For those who use R, you can now get MKL optimized BLAS and LAPACK for free with R Open from Revolution Analytics.

EDIT: Anaconda Python now ships with MKL optimization, as well as support for a number of other Intel library optimizations through the Intel Python distribution. However, GPU support for Anaconda in the Accelerate library (formerly known as NumbaPro) is still over $10k USD! The best alternatives for that are probably PyCUDA and scikit-cuda, as copperhead (essentially a free version of Anaconda Accelerate) unfortunately ceased development five years ago. It can be found here if anybody wants to pick up where they left off.