Programming in python
A.A. 2024/2025
Obiettivi formativi
The course introduces students to imperative programming by referring to the Python language. The course is divided in two parts: the first presents Python and its object-oriented features, the second focuses on libraries that can be useful in scientific computation and data analysis, in particular NumPy and pandas.
Risultati apprendimento attesi
Students will acquire the ability to write and tune a program that automatizes simple computational tasks; they will be able to understand how a small piece of Python code works, to find the reasons of a malfunction and to correct it appropriately. Moreover, students will be able to use the NumPy and pandas library to analyze tabular data.
Periodo: Primo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Primo semestre
Programma
The Python 3 programming language. Native data types, typing hints. Functions, selections and iterations. Basic data structures: lists, tuples, dictionaries, and sets. Files. Object-oriented encapsulation, iterators. Numpy multi-dimensional arrays and matrices. Data manipulation and analysis with pandas and libraries for visualization (matplotlib). Basics of probabilistic programming.
Prerequisiti
Students are required to be able to solve computational problems in an algorithmic way.
Metodi didattici
The course has lectures, which present the subject and interactively discuss problem solutions and laboratory sessions to experiment with tools. Course attendance is highly recommended.
Materiale di riferimento
Any Python3 book can be used to support the learning of the general part, for example J. Hunt "A Beginners Guide to Python 3 Programming" (https://minerva.unimi.it/permalink/39UMI_INST/i9q3jt/alma991017213265306031). NumPy (https://numpy.org/) and pandas (https://pandas.pydata.org/) have excellent documentation online.
Course website is linked from https://myariel.unimi.it/course/view.php?id=3137
Course website is linked from https://myariel.unimi.it/course/view.php?id=3137
Modalità di verifica dell’apprendimento e criteri di valutazione
The examination is based on a laboratory exercise. A final mark (on a 30 point scale) is given, by taking into account: knowledge of the subject and tools, and clarity of solutions.
Siti didattici
Docente/i