Module I: INTRODUCTION TO PYTHON FOR DATA SCIENCE (3 HOURS)
- Introduction to Google Colab.
- Python Fundamentals: Variables and Data Structures.
- Numpy and Pandas.
- Visualization with Matplotlib and Seaborn.
Module II: INTRODUCTION TO STATISTICS FOR DATA SCIENCE (3 HOURS)
- Descriptive statistics.
- Fundamentals of inferential statistics.
- Estimation and hypothesis testing.
Module III: UNSUPERVISED LEARNING (3 HOURS)
- K-means clustering: theory and practice.
- DBSCAN.
- Principal Component Dimensionality Reduction: Theory and Practice.
Module IV: SUPERVISED LEARNING FOR CLASSIFICATION (6 HOURS)
- Classification fundamentals: coding categorical and metric variables to assess classification.
- Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors, Bagging.
Module V: SUPERVISED LEARNING FOR REGRESSION (6 HOURS)
- Linear and non-linear (logistic) regression.
- Metrics for model evaluation and selection: RMSE and R-squared.
- Modern topics in regression with high-dimensionality data: regularization.
- Random Forest Regression
Module VI: DEEP LEARNING (3 HOURS)
- Theoretical foundations of Deep Learning: activation functions, neurons, Gradient Descent, layers.
- Practical application in the classroom.
Module VII: RECOMMENDATION SYSTEMS (3 HOURS)
- Fundamentals of recommendation systems.
- Collaborative Filtering, Matrix Factorization and Nearest Neighbors/Clustering.
- Practical applications.