Explore the foundations of data science and AI, their history, evolution, and real-world applications. You will learn about different types of artificial intelligence, the life cycle of a data project, and the technological trends transforming the world.

Learn the basics of programming with Python, the most widely used language in data science. You’ll work with syntax, control structures, functions, and essential libraries like NumPy and Pandas, along with coding best practices.

Get familiar with the most common tools used by data scientists, such as Jupyter Notebook, Google Colab, and VS Code. Learn to use Git for version control, integrate your projects with cloud platforms, and maintain a professional project structure.

Discover how to obtain, clean, and organize data from various sources: APIs, databases, CSV files, or web scraping. Learn about structured and unstructured data, storage options, and best practices for efficient data management.

Understand the statistical principles that underpin data science. You’ll study descriptive statistics, probability distributions, statistical inference, hypothesis testing, and data visualization techniques.

Develop your ability to explore and interpret datasets using visual and statistical techniques. Identify outliers, correlations, and patterns using tools like Seaborn and Matplotlib.

Learn how to prepare data for machine learning models. You’ll practice normalization, variable encoding, handling missing data, and creating new, relevant features for model improvement.

Introduction to supervised learning models. You’ll study linear and logistic regression, decision trees, Random Forests, KNN, and SVM, along with key evaluation metrics to measure model performance.

Deepen your knowledge with advanced techniques: hyperparameter optimization, ensemble methods (Boosting, Bagging, XGBoost), cross-validation, and basic model interpretability to improve accuracy and robustness.

Explore machine learning without labeled data. Learn clustering methods (K-means, DBSCAN, Hierarchical) and dimensionality reduction techniques (PCA, t-SNE) to uncover hidden patterns and intelligently segment data.

Dive into the fundamentals of deep learning. Understand neural networks, multilayer perceptrons, and backpropagation, and start building your own projects using TensorFlow and Keras. You’ll train and evaluate neural network models.

Learn to work with human language and text. Master text-cleaning techniques, tokenization, TF-IDF, n-grams, word embeddings, and transformers, applying them in projects like chatbots and sentiment analysis.

Discover how machines “see” the world. Work with image processing, convolutions (CNNs), classification, and object detection, applying these concepts in hands-on projects.

Learn to turn your models into functional applications. Practice model serialization (Pickle, Joblib), build APIs using Flask/FastAPI, and deploy projects to the cloud using AWS, GCP, or Streamlit. Introduction to MLOps concepts.

Reflect on the responsibility of using AI ethically: bias, privacy, transparency, and data governance. Conclude the course by developing an integrative project that brings together all stages of the data science lifecycle.