added readme and license

LICENSE

+MIT License
+
+Copyright (c) Information Services, the University of Edinburgh 2018
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

+# Python for Data Science
+This is an introductionary course for Using Python for Data Science applications. In the recent years Python has became extremely popular within the data science communities mainly due to its ease of use, open-source nature and the fact that it is completely free. This couse aims to introduce newcomers to the most popular packages used today - numpy, pandas and matplotlib. Note that it assumes basic knowledge of python (i.e. lists, dicts, indexing).
+
+This is entirely self-contained and self-pased. You can do it in your own time and it shouldn't take more than 6 hours to go throught all of the material. However, this course is by no means a complete guide to using Python for data science applications. It serves the purpose of an introduction into the world of data analysis and make you comfortable with looking at seemingly random numbers and trying to extract meeting from them.
+
+The course is based on the wonderful Jupyter Notebooks which you can install from [here](http://jupyter.org/install). Alternatively, if you are from the University of Edinburgh you can access the programming environment using [Noteable](https://noteable.edina.ac.uk/) which can be accessed through the accompanying learn course (search for *Python for Data Science* coourse on [Learn](https://learn.ed.ac.uk)).
+
+### Setting up in Noteable
+If you are using Noteable, then the easiest way to get the necessary files in the course is by running the following command in a notebook **within Noteable**.
+```
+!git clone https://git.ecdf.ed.ac.uk/digital-skills/python-data-science
+```
+
+## Structure
+The course is split into 6 different notebooks:
+
+### Notebook 0
+Short intro to Jupyter Notebooks.
+
+### Notebook 1
+Python basic concepts refresher.
+
+### Notebook 2
+Challenges you to do sentiment analysis on a sample text.
+
+### Notebook 3
+Introduction to vectorised computing and dealing with large data.
+
+### Notebook 4
+Fun exercise to test your understanding of how data flows through Python.
+
+### Notebook 5
+Introduction to the numpy package and structing large data.
+
+### Notebook 6
+Introduction to plotting in python using matplotlib.
+
+## Authors
+This course was developed by Ignat Georgiev and Patrick Kinnear from the Digital Skills team at Information Services at the University of Edinburgh.
+
+## Credits
+The development of this course wouldn't have been possible without the help and provided materials by:
+- Alisdair Tullo, School of Philosophy, Psychology and Language Sciences, the University of Edinburgh
+- Magnus Hagdorn, School of Geosciences, the University of Edinburgh