Learn how to use Python for data analysis,
from manipulation to visualization through
Machine Learning model application!
Eligible CPF and multi-financing up to 100%
To be recalled Access to the programmeApproach 3P
Our training centre guides you in identifying the ideal training, helping you maximize funding opportunities.
We put all the keys in hand for a start with confidence.
Experience an immersive and intensive training experience, designed to dive into practical workshops and real case studies.
Learn by doing, and develop concrete skills directly applicable to your future projects.
At the end of your career, we evaluate your acquired skills, issue certification attesting to your expertise, and accompany you to ensure your success in your professional projects.
You are now ready to excel!
Description of the training
This training will allow you to acquire the basics of Python language and apply it to AI and data analysis. You will learn how to manipulate, clean and view data using the most popular Python libraries.
Objectives of training
At the end of this training, you will be able to:
- Master the basics of Python language.
- Handle and clean data with Pandas and NumPy.
- Create data visualizations .
- Apply Python to practical AI projects and analysis of actual data.
- Understand the basic principles of machine learning algorithms.
Who is this training for?
Training is open to all, in particular:
- Beginner Developers
- Data analytics
- Data Scientists
- Consultants Data
Prerequisites
Basic knowledge of information systems.
Training programme
Introduction to Python and data manipulation
- Introduction to Python language and its environment (Juppyter, Anaconda)
- Python data structures : Lists, dictionaries, sets
- Python Functions : Creation and use
- Introduction to Matplotlib: Creating simple graphics.
- Customizing graphics with Seaborn: Box pads, heatmaps, etc.
- Creating advanced visualizations with Seaborn.
- Analysis of real data sets (sales, weather, etc.).
- Introduction to statistical algorithms and Scikit-learn.
- Practical workshops: implementation of simple machine learning models.
Training assets
- Training both theoretical and practical, with concrete workshops.
- Experienced and specialized trainers in data analysis with Python.
- Access to interactive educational tools and live demonstrations.
Pedagogical methods and tools used
- Interactive courses with live demonstrations.
- Real case studies to put acquired skills into practice.
- Using tools like Jupyter Notebook and Anaconda for a full practical experience.
Evaluation
Evaluation is ongoing throughout the course:
- MCQ to verify understanding of the concepts addressed.
- Practical exercises to apply the acquired skills.
Normative References
The training complies with Python's best programming practices according to the Python Enhancement Proposal (PEP 8), guaranteeing clear and effective codes.