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.
Besoins technologiques pour la formation en ligne: avoir un ordinateur connecté à internet et un microphone.
