Planeamento
Aulas do tipo Teórico-Prático
Class 01 - Welcome! What is data science
- Welcome, presentation and course organization
- Discussion about what is Data Science?
Class 02 - Data Science Environment
- Data Science Environment
- Exercise on bash terminal
- Exercise on git and GitHub
- Exercise on the structure of a Data Science project
- Data Science Culture
- GBIF example
Class 03 - Data Science Methodology
- Data Science Methodology
- Data Management Plan
- FAIR Principles
Class 04 - Hands-on session
- Hands-on session exercises 1, 2, 3, 4
Data Science Hackathon Workshop 4th edition - 2026
Participation at the workshop - Data Science Hackathon Workshop 4th edition - 2026
CHALLENGES AND PROBLEM SOLVING WITH DATA SCIENCE IN NATURAL RESOURCES AND ENVIRONMENT
Class 05 - Tools for Data Science - programming
- main data science programming languages
- main libraries for data science tasks in Pyhton
- Jupyter
- demo jupyter notebook
Class 06 - Tools for Data Science - data
- Open data
- Data sources
- Web scraping
- Application Programming Interface (API)
Class 07 - Tools for Data Science - ML - supervised learning
- Machine Learning
- Supervised Learning Algorithms
Class 08 - Tools for Data Science - Unsupervised ML
- Unsupervised Learning
Class 09 - Evaluation Test
- Test for continuous evaluation
- Work on FADS project
Class 10 - Seminar, Tools for Data Science - semi-supervised lerarning
- Seminar
- Semi-supervised Learning
- Reinforcement Learning
Class 11 - Generative models
- Introduction to LLMs, building and use
Class 12 - Communicating results
- Data visualization workflow
- The importance of storytelling
- Elements of a data story
- Storytelling process
- Report structure
Class 13 - Evaluation Test
- Test for continuous evaluation
- Brief mention to ethics in data science