Tipo Insegnamento:
Opzionale
Durata (ore):
60
CFU:
9
SSD:
INFORMATICA
Sede:
BRESCIA
Url:
MONETA, FINANZA E RISK MANAGEMENT/CORSO GENERICO Anno: 1
MONETA, FINANZA E RISK MANAGEMENT/CORSO GENERICO Anno: 2
Anno:
2024
Course Catalogue:
Dati Generali
Periodo di attività
Primo Quadrimestre (01/10/2024 - 22/01/2025)
Syllabus
Obiettivi Formativi
The educational goals of the course are consistent with those of the master degree in “Analytics and Data Science for Economics and Management” as students will acquire the design and computational thinking skills necessary to manage the current informational challenges affecting data-driven domains such as the economic, business and social ones.
The course has a double fold aim: introducing to students the principles, methods and applications of a formal language for coding such as Python, and providing students with the design and computational mindset necessary to undergo the acquisition, the representation, the algorithmic processing and the interpretation of small and big data. A learning-by-doing approach is adopted as a means to reconcile the theoretical aspects of learning a computational language and its immediate application for solving problems independently. Real case applications and discussions will reinforce the critical capability of knowing how to communicate with (by programming) and through (by processing information) digital interfaces.
KNOWLEDGE AND UNDERSTANDING
Through a step-by-step introduction to the main concepts, methods and application of coding, students will manage design and computational thinking problem-solving. They will acquire a robust knowledge of the Python programming language, with reference to the essential notions of data types and structures, programming paradigms, algorithmic design and computational complexity.
APPLIED KNOWLEDGE AND UNDERSTANDING
Students will be able to understand and model data types and structures, importing data from structured and unstructured data sources, analyze problems and design formal algorithmic solutions; translate algorithmic solutions into computer programs; use Python libraries and functions for data acquisition, manipulation, modelling, processing; analyse, interpret, and communicate data properly.
MAKING JUDGEMENT
Students will be able to apply his autonomous and critical capabilities to the problem of design and develop the best computational solution for the data at hand, including the capacity of evaluating the management, acquisition, processing, analyzing and interpretation of the available information, and to evaluate critically the results, in order to define practical policies and make correct decision in concrete socioeconomic and business situations.
COMMUNICATION SKILLS
Students will be able to use the design and computational thinking aspects of coding to understand the description of a data processing problem, and to discuss and communicate the solution with technical experts as well as business experts.
LEARNING SKILLS
At the end of the course, students will be able to: (1) identify and frame design and computational thinking problems for data processing and decision; (2) identify the data import, processing and interpretation tools suitable for dealing with it; (3) reach a sufficient maturity enabling them to autonomously learn and understand innovative approaches and solutions.
The course has a double fold aim: introducing to students the principles, methods and applications of a formal language for coding such as Python, and providing students with the design and computational mindset necessary to undergo the acquisition, the representation, the algorithmic processing and the interpretation of small and big data. A learning-by-doing approach is adopted as a means to reconcile the theoretical aspects of learning a computational language and its immediate application for solving problems independently. Real case applications and discussions will reinforce the critical capability of knowing how to communicate with (by programming) and through (by processing information) digital interfaces.
KNOWLEDGE AND UNDERSTANDING
Through a step-by-step introduction to the main concepts, methods and application of coding, students will manage design and computational thinking problem-solving. They will acquire a robust knowledge of the Python programming language, with reference to the essential notions of data types and structures, programming paradigms, algorithmic design and computational complexity.
APPLIED KNOWLEDGE AND UNDERSTANDING
Students will be able to understand and model data types and structures, importing data from structured and unstructured data sources, analyze problems and design formal algorithmic solutions; translate algorithmic solutions into computer programs; use Python libraries and functions for data acquisition, manipulation, modelling, processing; analyse, interpret, and communicate data properly.
MAKING JUDGEMENT
Students will be able to apply his autonomous and critical capabilities to the problem of design and develop the best computational solution for the data at hand, including the capacity of evaluating the management, acquisition, processing, analyzing and interpretation of the available information, and to evaluate critically the results, in order to define practical policies and make correct decision in concrete socioeconomic and business situations.
COMMUNICATION SKILLS
Students will be able to use the design and computational thinking aspects of coding to understand the description of a data processing problem, and to discuss and communicate the solution with technical experts as well as business experts.
LEARNING SKILLS
At the end of the course, students will be able to: (1) identify and frame design and computational thinking problems for data processing and decision; (2) identify the data import, processing and interpretation tools suitable for dealing with it; (3) reach a sufficient maturity enabling them to autonomously learn and understand innovative approaches and solutions.
Prerequisiti
Basic Information Technology Knowledge. Previous programming experience is desirable but not requested.
Metodi didattici
The educational objectives highlighted here are achieved through lectures, laboratory activities with the use of computers, individual and group projects, and exercises
Verifica Apprendimento
The written exam covers all the topics in the Program and consists of two parts:
(1) The first part concerns a set of questions that aim to verify the student's knowledge of the various topics of the course and the specific conceptual and technical terminology related to design and computational thinking. This part is organized as an online test on the Moodle e-learning platform including four closed-ended questions concerning theoretical topics and/or short applications of the studied concepts; the overall grade of this test amounts to 16 maximum (4 for each of the closed-ended questions);
(2) The second part of the exam is related to the assessment of programming skills, by reading or by writing a program in Python. This second part consists in a short program in Python that students should be able to read or write.
Final Evaluation:
The two parts of the exam (first questionnaire and second programming in Python) are each worth 50% of the total score. The final grade is obtained as the sum of the two grades in the two parts. The exam is passed if the final grade is greater than or equal to 18. It will be given a final grade of 30/30 cum laude to the students who achieve a final score equal to 32.
There are no differences between attending and non-attending students.
The first part of the exam is aimed at verifying skill 1 (Knowledge and understanding).
The second part of the exam (or the program-based test) allows to verify skills 2, 3 and 4 (Applying knowledge and understanding, Making judgements, Communication skills).
The skill concerned with autonomous study (5, Learning skills) is indirectly verified, because passing the exam is also made possible by the autonomous execution of exercises suggested by the teacher for homework.
(1) The first part concerns a set of questions that aim to verify the student's knowledge of the various topics of the course and the specific conceptual and technical terminology related to design and computational thinking. This part is organized as an online test on the Moodle e-learning platform including four closed-ended questions concerning theoretical topics and/or short applications of the studied concepts; the overall grade of this test amounts to 16 maximum (4 for each of the closed-ended questions);
(2) The second part of the exam is related to the assessment of programming skills, by reading or by writing a program in Python. This second part consists in a short program in Python that students should be able to read or write.
Final Evaluation:
The two parts of the exam (first questionnaire and second programming in Python) are each worth 50% of the total score. The final grade is obtained as the sum of the two grades in the two parts. The exam is passed if the final grade is greater than or equal to 18. It will be given a final grade of 30/30 cum laude to the students who achieve a final score equal to 32.
There are no differences between attending and non-attending students.
The first part of the exam is aimed at verifying skill 1 (Knowledge and understanding).
The second part of the exam (or the program-based test) allows to verify skills 2, 3 and 4 (Applying knowledge and understanding, Making judgements, Communication skills).
The skill concerned with autonomous study (5, Learning skills) is indirectly verified, because passing the exam is also made possible by the autonomous execution of exercises suggested by the teacher for homework.
Testi
Course materials and lecture slides available on the e-learning platform.
- Intro to Python for Computer Science and Data Science - P. Deitel, H. Deitel
- Think Python How to Think Like a Computer Scientist - Allen Downey
- Automate the boring stuff with Python, Al Sweigart,available at https://automatetheboringstuff.com/
- Intro to Python for Computer Science and Data Science - P. Deitel, H. Deitel
- Think Python How to Think Like a Computer Scientist - Allen Downey
- Automate the boring stuff with Python, Al Sweigart,available at https://automatetheboringstuff.com/
Contenuti
The course aims at providing conceptual and technical design skills about computational aspects for data analysis. Students will learn how to approach problems according to an algorithmic view. Students will learn some techniques for problem solving and how to use a programming language to provide a sound and formal description of a designed problem solution. They will learn programming skill in Python, giving the basic concepts of algorithms with reference to the use of the Python language (syntax, data structures, data import / export in Python, data visualization in Python) and its applications in data science and analytics.
Lingua Insegnamento
English
Altre informazioni
If the computer lab cannot be used, students are required to bring their own laptops
Corsi
Corsi
MONETA, FINANZA E RISK MANAGEMENT
Laurea Magistrale
2 anni
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