Python for Data Science and Machine Learning (EN) 2021
Event details
| Date | 23.11.2021 › 25.11.2021 |
| Hour | 08:30 › 17:00 |
| 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
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
Links
Practical information
- Informed public
- Registration required
- This event is internal
Organizer
- Learning & Development Team