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Introdução à Aprendizagem Automática e Ciência de Dados com Python

Código 17018
Ano 1
Semestre L0
Créditos ECTS 2
Carga Horária O (32H)/PL(12H)/TP(12H)
Área Científica Informática
Learning outcomes The Introduction to Machine Learning and Data Science with Python course aims to provide students with a comprehensive understanding of the techniques and methodologies used in data analysis and processing, as well as in the implementation of predictive models. At the end of the course, students should be able to:
- Understand and use essential tools for data analysis in Python.
- Read, process and transform data using specific libraries.
- Visualize data effectively for preliminary insights.
- Implement machine learning models for predictive analysis.
- Evaluate and improve the performance of automatic and deep learning models.
Syllabus 1. Python Fundamentals and Development Environment

2. Basic Python Programming Concepts

3. Data Manipulation and Analysis with Pandas

4. Data Visualization with Matplotlib, Seaborn and Plotly

5. Introduction to Machine Learning with Scikit-Learn

6. Advanced Machine Learning Models

7. Introduction to Deep Learning with PyTorch
Main Bibliography 1. Joel Grus, Data Science from Scratch: First Principles with Python, 2nd Edition, O'Reilly Media, 2019.
2. Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media, 2017.
3. Jake VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016.
4. Andreas C. Müller and Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly Media, 2016.
5. D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman, Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hall/CRC, 2019.
6. Hui Lin, Ming Li, Practitioner’s Guide to Data Science, 2023.
Language Portuguese. Tutorial support is available in English.
Data da última atualização: 2025-06-11
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