Tutorials
=========

This section contains a step-by-step by example tutorial of how to use
Scikit-Criteria

Contents:

.. toctree::
   :maxdepth: 1

   quickstart.ipynb
   sufdom.ipynb
   rankcmp.ipynb
   rankrev.ipynb
   extend.ipynb


Extra tutorials
---------------

This section is a collection of articles, blog-posts and other curated
materials, written outside of core developers.


.. toctree::
    :maxdepth: 1

    A Data-Driven Method to Reduce Employee Survey Length <https://towardsdatascience.com/a-data-driven-method-to-reduce-employee-survey-length-8aecedcf5df9>
    Ranking algorithms - know your multi-criteria decision solving techniques! (OLD API) <https://towardsdatascience.com/ranking-algorithms-know-your-multi-criteria-decision-solving-techniques-20949198f23e>


Scientific articles
-------------------


Scientific articles or paper is an academic work that is usually published in
an academic journal. It contains original research results or reviews existing
results. Such a paper, also called an article, will only be considered valid if
it undergoes a process of peer review by one or more referees who check that
the content of the paper is suitable for publication in the journal :cite:p:`enwiki:1134614840`.

Several bibliographic databases organize digital collections of references to
published literature, including journal and newspaper articles and conference
proceedings. The following links contain publications that cite the
Scikit-Criteria paper :cite:p:`scikit-criteria`, and present novel applications of multi-criteria models
to different scientific areas.


.. toctree::
   :maxdepth: 1

   Google Scholar <https://scholar.google.com/scholar?cites=5131381794263090018>
   Semantic Scholar <https://www.semanticscholar.org/paper/Scikit-Criteria%3A-colecci%C3%B3n-de-m%C3%A9todos-de-an%C3%A1lisis-Cabral-Luczywo/0f66f92e7a52f36b371b8cab37e72241368f2de2#citing-papers>



.. seealso::

    If you're new to Python, you might want to start by getting an idea of what
    the language is like. Scikit-criteria is 100% Python, so if you've got minimal
    comfort with Python you'll probably get a lot more out of our project.

    If you're new to programming entirely, you might want to start with this
    `list of Python resources for non-programmers <https://wiki.python.org/moin/BeginnersGuide/NonProgrammers>`_

    If you already know a few other languages and want to get up to speed with
    Python quickly, we recommend `Dive Into Python <http://www.diveintopython3.net/>`_. If that's not quite your
    style, there are many other `books about Python <https://wiki.python.org/moin/PythonBooks>`_.

    At last, if you're already familiar with Python and eager to explore the scientific stack further, be sure to check out 
    the `Scipy Lecture Notes <http://www.scipy-lectures.org/>`_