Groupe d’études et de recherche en analyse des décisions

Data Science Workshop using Python

Vahid Partovi Nia Professeur adjoint, Département de mathématiques et de génie industriel, Polytechnique Montréal, Canada

Mohammad Sajjad Ghaemi Polytechnique Montréal, Canada

The aim of this workshop is to introduce some elementary data science concepts such as data loading, data pre-processing, and data analysis. We practice some basic Python libraries such as numpy, scipy, scikit-learn, matplotlib, and IPython-notebook to execute several supervised, unsupervised, and semi-supervised learning algorithms. We'll go over the details of the implemented tools that are widely used in the data science domain, along with the algorithm background, mathematical insights, and statistical intuition.

As a data scientist you must be able to communicate or collaborate with any potential data-related project effectively, using powerful programming tools. Python is not only famous because of its computational power, but also because of its easy integrity in the web and mobile applications. Python codes are easily readable and effectively maintainable. It is an easy-to-understand language developed by computer engineers which can be learned by experts from various backgrounds such as statistics, mathematics, business, finance, etc. This workshop enables you to build a comprehensive tool that unifies every part of your data-driven workflow. Python and its libraries are platform independent, and are run on Windows, Linux, or Mac OS X identically.

Data Science is a wrap of statistics combined with strong computer skills. This set of skills provides extraction of knowledge from large volumes of structured or unstructured data. Data science employs techniques and theories from diverse fields, e.g. mathematics, statistics, and computer science. Machine learning is a practical tool and a subfield of artificial intelligence with the purpose of discovering the underlying pattern of data through predictive models. The goal of data science is to use data preprocessing, statistics, and machine learning methods in order to investigate extensive problems and draw conclusions from large amount of data.


Vous devez vous inscrire afin de participer à l'atelier. Veuillez noter que le nombre de participant est limité et que le dîner ne sera pas fourni.