Class Ciência de Dados Avançada

The Curricular Unit "Advanced Data Science" is a key part of the professional technical course in Computer Applications for Data Science. This course deepens the essential skills in manipulating, analyzing, and interpreting large data sets.

In the field of action, the UC focuses on advanced machine learning techniques, natural language processing and predictive analysis. The area of ¿¿expertise includes data mining, feature engineering, and model optimization. The intervention domain addresses updated tools and frameworks that are pillars in the world of data analysis. Given the growing relevance of data in strategic decisions in various industries, this UC is fundamental in the study cycle, preparing students to be experts capable of extracting valuable insights from raw data and transforming them into impactful solutions.

• Advanced Machine Learning Algorithms: High-level classification, regression and clustering techniques.
• Natural Language Processing (NLP): Tokenization, semantic analysis, and textual representation models.
• Predictive Analysis: Creating models to predict future trends and behaviors.
• Feature Engineering: Techniques for extracting and transforming features that maximize model performance.
• Model Optimization: Hyperparameter tuning, cross-validation and model selection.
• Advanced Data Visualization: Use of tools and libraries to represent complex data in an intuitive way.
• Modern Frameworks: Introduction to tools like TensorFlow, PyTorch and others.
• Integrative Project: Development of a data science project from start to finish, using real data sets.

Practical Labs in Cloud Environments: Access to cloud platforms for real and scalable experimentation with algorithms and datasets.
Interactive Peer Review: Collaborative analysis and feedback of projects between students themselves, promoting mutual learning.
Immersion Journeys: Intensive sessions where real company problems are presented to students for real-time solution.
Project-Based Learning: Development of projects that address the entire data science lifecycle, from acquisition to presentation of insights.
Seminars with Experts: Conferences and workshops with leading professionals in the field, providing a practical and current view of the market.

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. New York, NY: Springer.
• Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.