Computational Physics Laboratory

A.Y. 2019/2020
6
Max ECTS
66
Overall hours
SSD
FIS/01 FIS/02 FIS/03 FIS/04 FIS/05 FIS/06 FIS/07 FIS/08
Language
Italian
Learning objectives
The aim of the course is to provide basic notions for breaking the ice with some computational tools (C ++, shell and scripting languages, python, latex), and "Data Science" skills in the sense of reasoned analysis and data visualization.

The philosophy of the course is that the way one learns to use and conceptualize computational tools is not with frontal lessons, but using them, with a project and a purpose. The course provides some essential tools for taking off and some projects, in order to create the motivation to use the tools "on the field".
Expected learning outcomes
The student should master a technical toolbox that includes
C++, Shell scripting, AWK, Python, Data Visualization
Furthermore the student must be able to face "data challenge" projects that start from a dataset and aim to extract the main trends and clearly communicate them in a written report.
Single course

This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.

Course syllabus and organization

Single session

Lesson period
Second semester
Course syllabus
Part I "Toolbox"
Ia Technical toolbox:
C++
Shell scripting / AWK
Python Rudiments
LaTeX

Ib Scientific toolbox
Data Visualization
Probability and Null Models

Part II "Data Challenges"
Three data challenges lasting one week each
Prerequisites for admission
Basic knowledge of programming and computer science
Teaching methods
The philosophy of the course is that the way one learns to use and conceptualize computational tools is not with frontal lessons, but by using them, with a project and a purpose. The course provides some essential elements for carrying out projects, in order to create the motivation to use some tools "on the field".
Teaching Resources
Useful books - as reference.

Jeroen Janssens. Data Science at the Command Line: Facing the Future with Time-Tested Tools

Steve Blair. Python Data Science

William S. Cleveland. The Elements of Graphing Data
Assessment methods and Criteria
The assessment is based on "data challenges", to be carried out in the laboratory that start from a dataset to achieve a certain number of objectives.

At the end of each data challenge students must submit a DI MAX 3 PAGES report written in LaTeX which describes the results achieved and includes the plots.
FIS/01 - EXPERIMENTAL PHYSICS
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
FIS/03 - PHYSICS OF MATTER
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM
FIS/07 - APPLIED PHYSICS
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS
Laboratories: 54 hours
Lessons: 12 hours
Professor(s)
Reception:
By appointment, in-person and via Teams or other platforms.