Python Training for Data Science | scientific python
To work in Python, you have to manage the independent modules. Install and import a module is to buy a new box of legos. She gives more objects. So we can enrich the features like the ability to process a CSV file or a spreadsheet. Python must interact with the computer. It is therefore necessary to know how to administer the operating system: fi x the working directory from which to import the data, for example. Imported data, fi les or the web are rarely perfect. They must be cleaned, prepared and rectified. In a spreadsheet, we use features such as Find and Replace. That's what the regex module does and more. In many areas, time is at the heart of the data (time series). The datetime module also makes it possible to take the first steps in data science since a time-sensitive data set is the basis of a forecasting model (machine learning). In fact, right from the data rectification stage, we are entering the data science gateway with the Numpy and Pandas modules. Other topics are covered in the appendix, including an overview of object-oriented programming to speed up computations, doctest to document source code, and microPython for microcontrollers (part of the Internet of Things). We will revise them quickly.
This training is only available in private, contact us for more details.
Introduction to Python Training
Introduction to Python – Block 1
7h + 2h
- The types of variables (integer, decimal, boolean, etc.) and basic operators;
- Data structures (list, tuple, dictionary, etc.);
- Flow checks (if-else, try-except-finally);
- Loops (for, while);
- Control-loop combinations;
- Functions and methods (function (object) and object.method ());
- Importing plugins to access more functions-methods;
- Syntax and good practices.
Introduction to Python – Block 2
5h
- Manage modules;
- Administer the operating system
- Pull data from the web (web scraping);
- Rectify the data with the regex module;
- Integrate time with the datetime module.
Initiation to the science of data
14h
- Jupyter Notebook, Spyder and others;
- Introduction to Numpy;
- Introduction to Pandas – Series Object;
- Introduction to Pandas – DataFrame Object;
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- Extra: Introduction to Pandas – Instructions;
- Extra: Tidy Data;
- groupby and aggregations;
- Data collection with Web API and analysis;
- Graphic visualization;
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- Extra: Map view.
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Autre(s) cours dans cette catégorie
→ R Language Course: RStudio, Reporting
→ Python Training for Data Science | scientific python
→ Python Training – Data Science (Numpy Pandas Matplotlib)
Préalables :
- Good knowledge of the computer tool
Objectifs :
- Take in hand and write Python code
- Know the standard modules
- Being able to create to organize its code in the form of module and package
- Introduction to data science with Python
Montréal :
Québec :
Gatineau / Ottawa :
Moncton :
Classe virtuelle (en ligne) :
1495 $
per participant
Duration :4 day(s), is 28 hours.
Hours: 9 am-5pm, 2 coffee breaks.
Locations: Montreal, Gatineau / Ottawa, Quebec City.
Regular price: 1759 $
*The preferential rate applies if you register at least two participants in the same session.
Mark Plouffe, Gouvernement du Canada/ Government of Canada
“ I want to thank you both for providing my resources some excellent training(Cobol) over the past 3 days. Mamadou, thank you for being so accommodating on such short notice and for sending your facilitator to Gatineau for this customised and personalised training course. We’ll look forward to continuing our partnership for future training needs. “
Maryse Duguay - Analyste fonctionnelle Base de donnée - Montréal
“J’ai grandement apprécié les méthodes d’enseignement du prof. Le fait que nous soyons un petit groupe a grandement facilité les apprentissages. Il s’adapte à son audience et les exercices sont formateurs. Je recommande fortement. “
Martin Côté - Analyste Assurance Qualité - Travailleur autonome – Montréal
“ Ce fut un plaisir de faire affaires avec Doussou Formation. Ce qui fait LA différence est le service personnalisé totalement à l'écoute des participants ainsi que l'adaptation aux besoins de formation. Flexibilité / Adaptabilité / Professionnalisme / Courtoisie. Merci!“