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CODE 114522
ACADEMIC YEAR 2024/2025
CREDITS
SCIENTIFIC DISCIPLINARY SECTOR GEO/11
TEACHING LOCATION
  • GENOVA
SEMESTER 1° Semester

OVERVIEW

The main objective of the course is to provide the basic general skills needed to analyse, model and interpret data in the field of Earth Sciences using IT procedures.

This objective is achieved through three main modules, all of a practical nature:

1) Introduction to the basic tools of programming aimed at scientific computing through the study of the Python language.

2) Review of existing open-source software and projects created in Python available for the different disciplines of Earth Sciences.

3) Presentation of some of the main regional and global databases of geo-scientific data available online with public access

The teaching is essentially laboratory-based and therefore involves the direct involvement of students during the laboratories and through work 'at home'.

AIMS AND CONTENT

LEARNING OUTCOMES

General objectives of the teaching are:

i) Provide a basic understanding of the Python language and its main libraries

ii) Provide the basic tools to address the main problems of scientific computing to collect, organize, analyze, model and interpret geoscientific data

iii) Highlight the possible applications of Python programming to geophysical, geological and environmental fields

iv) Increase student autonomy in the management, analysis and modeling of geoscientific data via computer

AIMS AND LEARNING OUTCOMES

Attendance and participation in the planned training activities will allow the student to acquire basic knowledge of programming in Python and its main libraries available to apply its potential in the field of Earth Sciences.

 

Specifically, the student will be able to:

 

 Know, describe and apply the characteristics and constructs of the Python language and its main libraries

 Select the correct computational methods and related procedures to represent, analyze and model geoscientific data

 Draft complete and rigorous reports relating to the analysis and modeling of geoscientific data

 Keep yourself up to date on the use of software and data representation and analysis techniques

 

At the end of the course the student must demonstrate that:

 

 Have developed the ability to program, document and test elementary numerical algorithms, correctly interpreting the results

 Know and understand the ideas underlying numerical methods in relation to the application problem to be solved

 Know how to apply the knowledge acquired by designing and independently implementing analysis and modeling algorithms

 Know how to communicate ideas and solutions in a clear, rigorous and effective manner to specialist and non-specialist interlocutors

 

PREREQUISITES

None

TEACHING METHODS

The course consists of lectures and computer laboratory exercises. Since learning tests are foreseen, attendance at lessons and exercises is strongly recommended. The classroom lessons are delivered through multimedia presentations.

The laboratory exercises will be organized possibly in person, possibly with multiple shifts.

All in-person activities will be carried out in compliance with the capacity limits of the classrooms/laboratories and distancing, established by current legislation following the COVID19 emergency.

Please refer to the specific AulaWeb application for the course for any updates due to changes in the health and epidemiological situation.

SYLLABUS/CONTENT

How to get and install Python

Mathematical, logical, relational operators

Control structures

Classes and functions

Writing and reading files

Interactive environments for scientific computing

Main libraries for numerical calculation

Main libraries for 2D and 3D data representation

Review and analysis of Python application libraries for Earth Sciences

RECOMMENDED READING/BIBLIOGRAPHY

All the slides used during the lessons, the handouts used and other teaching material will be available on AulaWeb at the end of each lesson cycle.

Recommended reference texts:

https://github.com/AllenDowney/ThinkPythonItalian/blob/master/thinkpython_italian.pdf

Other documentation is available at:

https://www.python.it/doc/

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Consult the detailed timetable at the following link: https://easyacademy.unige.it/portalestudenti/

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The purpose of the exam is to verify the level of achievement of the previously indicated objectives.

The exam consists of a practical test, which involves the solution of a problem by preparing Python scripts and functions, the results of which must be appropriately commented. The expected time for the test is four hours.

We will evaluate the syntactic and semantic correctness of the scripts, the effectiveness and efficiency of the algorithms used to identify the required solution and the ability to analyze the results. Each question is given a score. The grade for the written/practical test will range from 18 to 30, depending on the quantity of questions solved and the quality of the answers.

ASSESSMENT METHODS

Details on how to prepare for the exam and the level of depth required for each topic will be provided during the lessons.

The written exam will verify the actual acquisition of knowledge and the ability to apply it to concrete cases.

FURTHER INFORMATION

Students with a certification of physical or learning disability filed with the University can find information on support services at the web page https://unige.it/disabilita-dsa/studenti-disturbi-specifici-apprendimento-dsa, provided by the "Services for the Inclusion of Students with Disabilities and with Learning Disorders." They can also contact Professor Sara Ferrando (sara.ferrando@unige.it), the Distav contact for disabilities.