Course Description

This course will enable you to acquire the basics of the Python language and apply them to AI and data analysis. You will learn to manipulate, clean, and visualize data using the most popular Python libraries.

Learning Objectives

By the end of this course, participants will be able to:
Understand Data Science fundamentals: get familiar with data analysis basics, data types, and statistical methods.
Work with large datasets: clean, transform, and organize complex datasets.
Master predictive analytics techniques: apply statistical models and machine learning algorithms to make predictions from historical data.
Build machine learning skills: learn to construct, evaluate, and deploy supervised and unsupervised models.
Use advanced Data Science tools: gain hands-on expertise with popular tools and libraries in Python, R, and cloud platforms.

Who Is This Course For?

The course is intended for a broad audience, including:
Data Analysts: For those who want to deepen their data science skills.
Aspiring Data Scientists: Those seeking to develop advanced skills using statistical tools and AI models.
IT Professionals and Developers: Who want to specialize in data analysis and modeling with a focus on artificial intelligence.
Project Managers and Managers: With projects or teams handling large datasets who want to understand data science methodologies.

Prerequisites

Basic knowledge of information systems.

Program

The course program is structured around several main modules:
Introduction to Data Science and Statistical Data Analysis

• Introduction to Data Science: lifecycle, data preparation, and tools such as Python, R, Jupyter Notebooks.
• Statistical Analysis: descriptive statistics, hypothesis testing, and data visualization techniques.

Data Preparation and Exploration Techniques

• Data Cleaning: handling missing values, data transformation, normalization.
• Exploration and Visualization: charts, box plots, heatmaps with Matplotlib and Seaborn.

Introduction to Machine Learning

• Supervised Learning: linear/logistic regression, model evaluation.
• Unsupervised Learning: clustering (K-means), dimensionality reduction (PCA).

Deep Learning, Deployment, and Practical Applications

• Deep Learning: neural networks, TensorFlow, Keras.
• Deployment: productionizing models with Flask, APIs, and cloud deployment.

Course Benefits

Teaching approach: alternating theory and practice.
Qualified instructors with real-world experience.
Varied tools and learning materials.
Course accessible to all, without advanced technical prerequisites.

Teaching 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.

Assessment

Assessment is carried out in different ways:

• End-of-course multiple-choice quiz to test understanding of concepts.
• Practical case studies and group discussions.
• Continuous assessment during practical sessions.

Normative References

ISO/IEC 27001: Information security management.
GDPR: General Data Protection Regulation (EU).
ISO 22301: Business continuity management.
SOC 2: Criteria for security, availability, and confidentiality in cloud services.