Python for Data Science and Machine Learning (EN) 2021

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Event details

Date 23.11.2021 25.11.202108:3017:00  
Speaker Jean-Philippe Forestier
Category Internal trainings
Event Language English
During this course you will learn how to use (hands-on experience) various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit-Learn, Seaborn, … to tackle different stages of a Data Science project.

This course follows a pragmatic approach: it is organized into several simple but realistic examples and exercises that demonstrate how to use the Data Science-related Python modules and tools.

What you will learn
  • see how Python helps to analyze large and unstructured data with different tools and modules
  • learn different ways to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format
  • learn the basics of Machine Learning (supervised and unsupervised approach) and how to use its essential algorithms (Regression, Classification and Clustering)
 
  • Tooling: Anaconda, Jupyter Notebook
  • Data extraction: from XML file (lxml), from CSV file parsing (csv, pandas), from Excel file (openpyxl), from Text file (re),
  • Web Scraping : BeautifulSoap, Scrapy
  • Data munging and wrangling with Pandas
  • Data visualization : Matplotlib, Bokeh, seaborn, altair, plotly dash
  • Machine Learning (regression, decision trees, random forest, …) : Scikit-Learn, statsmodels, numpy, scipy
  • Overview of how Python help when you need to work with large datasets (Big Data) using Hadoop and Spark (PyDoop and PySpark modules)

Targeted audience
This fast-paced class is intended for aspiring (or practicing) data scientists and data analysts interested in using Python for their day-to-day work.

Pre-requesites:
Participants must be already familiar with the Python syntax and with the main Data Science concepts

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Practical information

  • Informed public
  • Registration required
  • This event is internal

Organizer

  • Learning & Development Team

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