OVERVIEW

This practical teaching unit introduces programming using Python for Engineering students. No prior coding experience needed. Students will learn to automate tasks, analyze data, and solve real-world problems through hands-on labs and projects.

AIMS AND CONTENT

LEARNING OUTCOMES

Elements of Computer Programming” provides foundations of using Computer Languages for implementing basic logic flows, functions, processes, data analysis and management. The Course introduces mostly in the practical use of Phyton by writing, executing and testing the code created by the Students.

AIMS AND LEARNING OUTCOMES

The main objective of the teaching unit is to provide students the foundational programming skills necessary to enhance their data analysis, automation, and problem-solving capabilities in a state-of-the-art industrial context.

Core Objectives include:

• Foundational Skills: introduce the core concepts of programming—variables, logic, and functions—using Python, ensuring all students build confidence regardless of their starting point.
• Data Proficiency: develop the ability to import, clean, explore, and summarize real-world industrial datasets (e.g., production logs, quality checks) using essential libraries like Pandas.
• Applied Problem-Solving: directly apply programming to solve representative engineering problems, such as calculating performance metrics, aggregating shift data, and identifying bottlenecks.
• Automation & Reporting: learn to automate repetitive data tasks and create visualizations and reports to communicate findings effectively.
• Future Foundation: establish a practical skill set that serves as a springboard for further exploration in data science, simulation, manufacturing, etc.

PREREQUISITES

Familiarity with computers, installation of software packages

TEACHING METHODS

Lectures face-to-face, with use of slides, and examples/exercises carried out on the PC, mainly using the numpy, pandas and matplotlib libraries, in python language. Student reception. Proposal, implementation and discussion of a project.

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova (https://unige.it/en/commissioni/comitatoperlinclusionedeglistudenticondisabilita)

SYLLABUS/CONTENT

• Introduction to the teaching unit
• Tool set-up (Anaconda, Jupyter Lab/Notebook, VS Code)
• Built-in data structures
• Control-flow
• Functions
• Numpy for efficient numerical operations
• Pandas for data manipulation (storage, cleaning & preparation, analysis and aggregation)
• Data visualization with Matplotlib

RECOMMENDED READING/BIBLIOGRAPHY

J. VanderPlas, “Python Data Science Handbook”, O’Reilly

W. McKinney, "Python for Data Analysis", O’Reilly

A. Sweigart, "Automate the Boring Stuff with Python"

Documentation of the used libraries

Lecture notes and other material suggested by the lecturer during the course

Non-attending students may contact the lecturer to agree on the best arrangements

TEACHERS AND EXAM BOARD

Exam Board

FRANCESCO BELLOTTI (President)

RICCARDO BERTA (President Substitute)

CARMINE CERRONE (President Substitute)

ANTONIO GIOVANNETTI (President Substitute)

LESSONS

LESSONS START

To access to the lesson calendar, please the following link: https://corsi.unige.it/en/corsi/11994/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Project work on an application example agreed between the teachers and the student.

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities, or other educational needs are invited to contact the teacher and the DITEN contact person for disability to agree on the possible  use  of  specific  modalities  and  supports  that  will  be  determined  on  a  case-by-case  basis, according to the University regulation for the inclusion and right to study of students with disabilities or specific learning disorders.

ASSESSMENT METHODS

The exam aims at verifying through a project the knowledge acquisition and solid understanding of the foundations of pyhton programming for data science and engineering applications.

Evaluation will take place in various steps: project definition talks, design/implementation of the solution, code inspection (incl. documentation), assessment of the results, final discussion of a short document describing the work done.

The lecturer will also take into account the student's participation during the course (attention, questions, answers).

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities, or other educational needs are invited to contact the teacher and the DITEN contact person for disability to agree on the possible  use  of  specific  modalities  and  supports  that  will  be  determined  on  a  case-by-case  basis, according to the University regulation for the inclusion and right to study of students with disabilities or specific learning disorders.

Exam schedule

FURTHER INFORMATION

Please ask the professor for any other information.