Class Applied Programming for Data Science

The course unit Applied Programming for Data Science is part of the Master's in Teaching Informatics, within the scientific area coded AD, taught in the 2nd semester. It aims to equip future Informatics teachers with fundamental Python programming knowledge, enabling them to solve computational problems and to manipulate, clean and analyse data, including school data, using specialised libraries such as NumPy, Pandas and Matplotlib. It also promotes the ethical use of information and digital citizenship, in line with PASEO. Taught in the 2nd semester, it complements students' technical training in data science, providing programming competences applied to teaching Informatics throughout the study cycle.

Not applicable

CP1 - Introduction to programming. CP2 - Introduction to the Python language and its syntax. CP3 - Work environments: Jupyter Notebook, Google Colab and Moodle CodeRunner. CP4 - Python syntax: variables, operators and simple data types (numeric, strings and their methods). CP5 - Flow control: decision structures and loops. CP6 - Functions, modules and packages. CP7 - Composite data types: lists, tuples, sets and dictionaries. CP8 - File manipulation and management (text, JSON, CSV). CP9 - Data visualisation with Matplotlib. CP10 - Object-oriented programming: classes.

ME1 - Dialogic lectures, presenting key concepts through slides and live-coding demonstrations, encouraging active participation (LO1). ME2 - Practical labs with Jupyter Notebook worksheets, enabling immediate experimentation with real or simulated datasets (LO2). ME3 - Project-oriented learning, with autonomous weekly exercises and projects and exploratory challenges, fostering autonomy and reflection (LO3). ME4 - Advanced activities and practical challenges, with mini-projects that encourage integrating complex concepts and innovation in programming (LO4).

Dinov (2023). Data Science and Predictive Analytics: Biomedical and Health Applications Using R (2nd ed.). Springer. Gil (2023). Programación de Inteligencia Artificial: Curso Práctico. RA-MA. Grus (2015). Data Science from Scratch: First Principles with Python. O'Reilly Media. Hen et al. (2024). Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns. Martins (2019). Programação em Python (3.ª ed.). IST Press. Neto & Maciel (2021). Python Para Data Science e Machine Learning Descomplicado. Vasconcelos (2015). Python: Algoritmia e Programação Web. FCA.