Merge branch 'devel' into 'master'

Devel: final cleanup.

See merge request !17

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 an introductory course for using Python for Data Science applications. In the recent years Python has become popular within the data science community 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-paced. 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 run by Digital Skills at the University of Edinburgh and takes place many times throughout the year. You can find the schedule [here](https://www.ed.ac.uk/information-services/help-consultancy/is-skills/programmes-courses-and-resources/classroom-based-courses).
+
+However, the course content is entirely self-contained and can be studied without the taught course. 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* course on [Learn](https://learn.ed.ac.uk)).
 
@@ -13,27 +15,30 @@ If you are using Noteable, then the easiest way to get the necessary files in th
 For an overview of how Jupyter notebooks work, you can check out the short notebook `python-data-jupyter-readme.ipynb`.
 
 ## Structure
-The course is split into 6 different notebooks:
+The course is split into 4 core notebooks followed by a couple of *extra* notebooks which allow you to apply everything you have learned to practical tasks in various fields.
 
 ### Notebook 0
-A recap of the assumed Python knowledge for the course.
+A recap of the assumed Python knowledge for the course. Skip this if you're a pro in Python already.
 
 ### Notebook 1
 Python warm-up with some text analysis exercises.
 
 ### Notebook 2
-Introduction to vectorised computing and dealing with large data with numpy.
+Introduction to vectorised computing and dealing with large data with **numpy**.
 
 ### Notebook 3
-Introduction to plotting in Python with matplotlib.
+Introduction to plotting in Python with **matplotlib**.
 
 ### Notebook 4
 Introduction to pandas and dealing with tabular data.
 
 ### Extra notebooks
-At the end of the course, there are notebooks starting with `extra` which over a wide
-range of applied data science topics. Usually students are expected to do one
-of their choice but feel free to go through all of them.
+At the end of the course, there are notebooks starting with `extra` which over a wide range of data science topics.
+- `python-data-extra-machine-learning`, introducing machine learning with scikit-learn.
+- `python-data-extra-networks`, introducing network analysis with NetworkX.
+- `python-data-extra-regex`, which introdices regular expressions, a powerful tool for working with text data.
+- `python-data-extra-text-analysis`, introducing tools for analysing text and performing sentiment analysis (see also `python-data-extra-regex`)
+- `python-data-extra-scipy`, introducing SciPy and exploring its signal processing module.
 
 ## Authors
 This course was developed by Ignat Georgiev and Patrick Kinnear from the Digital Skills team at Information Services at the University of Edinburgh.

attlist.txt

-Attributes
-Digeridoo - CC BY (freesound)
-chord-11 - CC-BY (freesound, mtcband)
-no - CC0 (freesound, allietron)
-no_male - CC0 (freesound, dragunnitum)
-yes_male - CC0 (freesound, draguunitem)
\ No newline at end of file

data/scipy_audio_licensing.txt

+The following audio files were obtained from Freesound.
+
+File                                                            Author                  License
+
+data-chord-11.wav                                               mtcband                 CC-BY
+
+data/no_male.wav                                                Dragunnitum             CC0
+data/yes_male.wav                                               Dragunnitum             CC0
+
+data/yes/104758__tim-kahn__yes-yes_0.wav                        tim.kahn                CC-BY
+data/yes/157506__matteusnova__the-word-yes.wav                  MatteusNova             CC-BY
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+data/yes/242640__reitanna__yesss.wav                            Reitanna                CC0
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+data/yes/345422__artmasterrich__male-yes-01.wav                 Artmasterrich           CC0
+data/yes/71466__noisecollector__yes.wav                         NoiseCollector          CC-BY
+data/yes/ulqb8-0qyby_dragunnitum.wav                            Dragunnitum             CC0
+
+data/no/269542__allietron__no.wav                               alietron                CC0
+data/no/343891__reitanna__another-loud-no.wav                   Reitanna                CC0
+data/no/344047__reitanna__low-no.wav                            Reitanna                CC0
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+data/no/379496__katt16__no.wav                                  KATT16                  CC0
+data/no/431608__prucanada__no-questioning.wav                   prucanada               CC-BY
+data/no/74482__dobroide__20090617-no.wav                        dobroide                CC-BY
+data/no/hytw4-edq6s_dragunnitum.wav                             Dragunnitum             CC0
+data/no/katt16_1.wav                                            KATT16                  CC0
+
+All other audio files were recorded by the author, and are released under the MIT license.
\ No newline at end of file

data/scipy_test_files.csv

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\ No newline at end of file

data/scipy_training_files.csv

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\ No newline at end of file

data/scipy_training_licensing.txt

-TODO: Fill in details here.
\ No newline at end of file