Computational physics laboratory

A.Y. 2020/2021
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 course aims to provide basic notions of some computational tools (C++, shell and scripting languages, python, LaTex), and "Data Science" skills, in the sense of reasoned and model-driven data analysis, data visualization and effective communication of scientific results. The main features of this course are the use and conceptualization of advanced data analysis tools through their use, with a clear plan and clear objectives. The course provides the technical and scientific background essential to work on the "data challenge" projects.
Expected learning outcomes
At the end of the course, the student will have to master an essential technical background which includes C++, Shell scripting, AWK, Python, data visualization and statistical data analysis tools. S/he will also be able to use technical skills in "data challenges", projects that start from a dataset and aim to extract the main trends. This also implies the acquisition of critical skills in the interpretation and understanding of trends in the data. Finally it is expected that the students will be able to communicate their results and their work in reports (written in English) that include plots and illustrative figures.
Course syllabus and organization

Single session

Lesson period
Second semester
Zoom lectures interactive slack space
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
Lectures
Practical and exercise sessions
Hands-on "data challenge" projects.
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 specific scientific and communication objectives.

At the end of each data challenge students must submit MAX 3-PAGE report written in LaTeX which describes the results achieved and includes the plots.

The evaluation criteria are the following:
Logical structure and communication
Data visualization
Technical aspects of the analysis
Scientific aspects and support of the scientific claims
FIS/01 - EXPERIMENTAL PHYSICS - University credits: 0
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS - University credits: 0
FIS/03 - PHYSICS OF MATTER - University credits: 0
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS - University credits: 0
FIS/05 - ASTRONOMY AND ASTROPHYSICS - University credits: 0
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM - University credits: 0
FIS/07 - APPLIED PHYSICS - University credits: 0
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS - University credits: 0
Laboratories: 54 hours
Lessons: 12 hours