Master the methods of analysis and modelling
data to make informed decisions!
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, participants will be able to:
Understanding the Fundamentals of Data Science: To become familiar with databases of data analysis, data types and statistical methods.
Learn how to manipulate massive data: Clean, transform and organize complex data sets.
Mastering predictive analysis techniques: Apply statistical models and machine learning algorithms to perform predictions based on historical data.
Developing skills in machine learning: Learning to build, evaluate and deploy supervised and unsupervised learning models.
Using Advanced Data Science Tools: Acquiring practical expertise with tools and libraries popular in Python, R, and cloud platforms.
Who is this training for?
The training is aimed at a wide audience, including:
Data Analysts:For those who want to deepen their skills in data science.
Data Scientists in Progress:Those who seek to develop advanced skills in the use of statistical tools and AI models.
IT Professionals and Developers:Who want to specialize in data analysis and modelling with a focus on artificial intelligence.
Project Managers and Managers:Having projects or teams that process massive data and want to understand the methodologies behind data science.
Prerequisites
Basic knowledge of information systems.
Programme
The training programme is structured around several main modules:
Introduction to Data Science and Statistical Data Analysis
- Introduction to Data Science: Life cycle, data preparation, and tools like Python, R, Jupyter Notebooks.
- Statistical analysis: Descriptive statistics, hypothesis tests, and data visualization techniques.
- Data cleaning: Management of missing values, data transformation, standardization.
- Exploration and Visualization: Graphs, box pads, heatmaps with Matplotlib and Seaborn.
- Supervised Learning: Linear regression, logistics, model evaluation.
- Unsupervised learning: Clustering (K-means), dimensionality reduction (PCA).
- Deep Learning: Neuron Networks, TensorFlow, Keras.
- Deployment: Production of models with Flask, API, and cloud deployment.
Training assets
Pedagogical approach: Alternative between theory and practice.
Qualified stakeholders with field experience.
Various educational tools and materials.
Training accessible to all, without advanced technical prerequisites.
Pedagogical methods and tools used
Live demonstrations on Data Science tools and platforms.
Real case studies with analysis of concrete solutions.
Group work to share insights and experiences.
Continuous feedback during hands-on sessions.
Evaluation
The evaluation is carried out in a number of ways:
- End-of-training QCM to test understanding of concepts.
- Practical case studies and group discussions.
- Ongoing evaluation during practical sessions.
Normative References
ISO/IEC 27001:Information security management.
GDPR:General Regulation on the protection of personal data (EU).
ISO 22301:Management of business continuity.
SOC 2:Criteria for security, availability and confidentiality in cloud services.