Data Scientist Program

Course Description

intensive training to acquire the fundamental skills of data science, covering data collection and cleaning, visualization, exploratory analysis, as well as machine learning and the appropriate tools.

Learning Objectives

By the end of this course, participants will be able to:

• Block 1: Data Science Basics – Understand the basics of data science and the Python ecosystem. Apply statistical concepts and get started with supervised machine learning.
• Block 2: Advanced Machine Learning Techniques – Master unsupervised algorithms and the processing of textual and time-series data. Optimize models and introduce Deep Learning.
• Block 3: Advanced Projects and Industrialization – Apply Deep Learning to computer vision and NLP. Deploy models and monitor them in production.
• Block 4: Final Project and Professional Preparation – Design and implement a complete project. Validate skills and prepare for professional certifications.

Who is this course for?

The course is intended for a wide audience, including:

The course is intended for a wide audience, including:

• Developers and software engineers wishing to specialize in data science.
• Data analysts wanting to strengthen their machine learning skills.
• Data engineers interested in managing and deploying data pipelines.
• Technical project managers seeking to understand data science to better manage AI projects.
• Researchers and students retraining or preparing for professional certifications.

Prerequisites

No specific prerequisites are required.

Course Outline

Block 1: Data Science Basics (5 days)

• Objective: Understand the fundamentals of data science and the Python ecosystem.

Day 1 (Morning)

• Content: Introduction to data science, main tools (Python, Jupyter), data manipulation with pandas.

Day 1 (Afternoon)

• Objective: Explore data structures and types.
• Content: Exploratory analysis, handling missing data, visualization with Matplotlib and Seaborn.

Days 2–3: Statistics and Probability Applied to Data Science

• Objective: Apply mathematical foundations to data analysis.
• Content: Descriptive statistics, hypothesis testing, distributions, introduction to probability.

Days 4–5: Introduction to Machine Learning

• Objective: Discover supervised machine learning techniques.
• Content: Linear regression, logistic regression, model evaluation (metrics, overfitting).

Block 2: Advanced Machine Learning Techniques (5 days) Day 6: Unsupervised Machine Learning

• Objective: Master unsupervised algorithms.
• Content: Clustering (k-means, DBSCAN), dimensionality reduction (PCA, t-SNE).

Day 7: Time-Series and Text Data Processing

• Objective: Work with time-series and NLP-specific data.
• Content: Time series with pandas, introduction to NLP with spaCy and NLTK.

Days 8–9: Training and Optimizing Advanced Models

• Objective: Optimize model performance.
• Content: Ensemble methods (random forests, gradient boosting), hyperparameter tuning with GridSearchCV.

Day 10: Introduction to Deep Learning

• Objective: Understand the basics of neural networks.
• Content: Perceptron, backpropagation, building a model with TensorFlow.

Block 3: Advanced Projects and Industrialization (5 days) Day 11: Computer Vision

• Objective: Apply deep learning to computer vision.
• Content: Convolutional networks, applications to image classification.

Day 12: Natural Language Processing (NLP)

• Objective: Leverage NLP tools.
• Content: Advanced techniques: embeddings, transformers (BERT).

Days 13–14: Deployment and Production

• Objective: Implement a deployment pipeline.
• Content: APIs with Flask, cloud integration (AWS SageMaker, Azure ML).

Day 15: Monitoring and Post-Deployment Optimization

• Objective: Monitor models in production.
• Content: Introduction to MLflow, managing data drift.

Block 4: Final Project and Professional Preparation (3 days) Day 16: Launch of the Final Project

• Objective: Design an end-to-end project.
• Content: Problem definition, data exploration, setting up the processing pipeline.

Day 17: Model Development and Evaluation

• Objective: Implement a high-performing solution.
• Content: Model training, testing, and adjustments.

Day 18: Presentation and Certification

• Objective: Validate skills.
• Content: Project defense, preparation for certifications (AWS, Azure, GCP).

Course Highlights

• Pedagogical and modular approach: Alternating theory and practice for better assimilation of concepts.
• Cloud integration: Strong focus on cloud and distributed solutions.
• Qualified instructors: Trainers specialized with real-world experience in the field.
• Tools and learning materials: Access to online resources, live demonstrations, and real case studies.
• Accessibility: Course open to all, without advanced technical prerequisites.
• Hands-on practice: A complete project at the end of the modules to consolidate learning.
• Industry preparation: Focus on certifications and standard tools used in the professional environment.

Teaching Methods and Tools Used

• Live demonstrations with data science services.
• Hands-on workshops and real case studies across various sectors (industry, retail, healthcare).
• Experience feedback: Sharing best practices and common mistakes in companies.
• Simulations and tools: Use of simulators for interactive workshops.

Assessment

• End-of-course multiple-choice quiz to test understanding of the topics covered.
• Practical case studies or group discussions to apply the knowledge acquired.
• Continuous assessment during practical sessions.
• Hands-on practice: A complete project at the end of the modules to consolidate learning.

Normative References

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.