93202 - Computer Science Lab-based Course

Learning outcomes

By the end of the course, the students know the basics of programming in Python and the usage of the main Python libraries for statistical and scientific computation, and have the skills to perform  analyses of case studies using the Python language.

Course contents

The Python language. Expressions, tuples, lists, comprehensions, sets, dictionaries. Repetitive and branching instructions. The NumPy and Panda packages and comparison with the R language. Processing examples from the Machine Learning literature.

Readings/Bibliography

Recommended readings:

Parker, J. R. (2016). Python: An Introduction to Programming. Mercury Learning & Information.

Zhang, Y. (2015). An Introduction to Python and Computer Programming. Senegal: Springer Singapore. Warning:
this book is based on Python v. 2, which slightly differs from Python v. 3.

Both books are free to download (using student institutional credentials) E-books searchable in

http://sba.unibo.it | Online resources | E-books | Ricerca un e-book nel Catalogo A-Link

Teaching methods

NOTE: As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online.

The lessons of the course are divided into
• frontal lessons in a lecture room, 2/3 of the course hours
• lessons in a laboratory, 1/3 of the course hours

Assessment methods

Oral examination, in which the student must demonstrate: knowledge of the specific vocabulary relevant to the elements of the Python language, and mastery of Python programming techniques, applied both to general purpose algorithms and analysis cases in Machine Learning.

Teaching tools

Slide presentations, laboratory of PCs with a Python interpreter

Office hours

See the website of Stefano Lodi

See the website of Tommaso Pirini