Commit aa37f5a6 authored by Thibaut.Lunet's avatar Thibaut.Lunet

TL: reorganized presentation files, added abstract for second seminar

parent dd8a6b19
# Python presentation for PhD seminar (19/03/2018, 17-18h)
Or, how to introduce python to applied & pure mathematics PhD students ...
Or, how to introduce python to applied & pure mathematics students and researchers ...
## presentation
## presentations
Source files of the presentation, see the inside
Presentations and source files for each given seminar, see the inside each directory.
## examples
An (other one) introduction to Python
You may have heard of it, you may fear it, love it, or simply you just do not care ... in any case,
the Python graduate seminar is back !
Why ? Because it is fun ! And also has a large probability to be usefull in you future career.
Python is not only one programming language in an ocean of other solutions (Matlab, Mathematica, R, ...) :
it is a licence-free solution, providing numerous package for mathematical applications, among them linear algebra,
optimization, data vizualisation, symbolic computation, and many other ... its application scope goes even way beyond
scientific computing, and present similarities with many other programming languages.
Furthermore, Python is supported by a huge community, which provides quick answers to any of your question when you
write it on a Google reseach bar.
In this seminar, we intend to introduce the main features of Python programming, by means of on-hand examples and applications during which each participant could try some basic Python coding on their personal laptop.
Hence it is highly recommended to bring your own laptop, and to install on it, prior to the seminar, to install the Anaconda distribution for Python 3.6 (see :, in order to provide the main Python packages
and the Spyder development editor.
Three main themes will be covered :
- what is Python, and what's hide behind it
- basics Python programming (variables, functions, loops, ...)
- introduction on Object Oriented Programming for polynomials computation
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