Educational objectives Learning goals
Develop analytical and computational skills to solve statistical decision problems.
Knowledge and understanding
At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory.
Applying knowledge and understanding
Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems.
Making judgements
One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills.
Communication skills
Students acquire the ability of presenting written reports of their practical laboratories.
Learning skills
The students acquire a series of skills useful for future academic and professional activities.
|
Educational objectives General Objectives.
The objective of this course is to present the basics necessary for the use of a general-purpose imperative programming language. In particular, the use of the Python 3 programming language will be demonstrated.
Specific objectives
(a) Knowledge and understanding skills.
Students will know the basic constructs of the Python 3 language, will be able to understand a simple program written in Python 3 and to write programs in the same language. They will also be able to use an integrated development environment (IDE).
(b) Ability to apply knowledge and understanding.
At the end of the course, students will be able to solve simple algorithmic problems using the Python 3 programming language, correct syntactic and semantic errors using an IDE, and evaluate the correctness and complexity of the identified solutions.
(c) Autonomy of judgment.
Students will develop the ability to formalize algorithms using a programming language, choosing the constructs best suited to solve the individual problem. They will be able to evaluate the correctness, readability and generality of the solutions identified.
(d) Communication skills.
Students will acquire the ability to formally express a mental procedure for solving a problem, and to understand the crucial points of an algorithm.
(e) Learning skills
Students will be able to easily learn the use of imperative programming languages, appreciating similarities and differences from the Python 3 language.
|
Educational objectives General goals
The course aims to introduce HPC (High Performance Computing) systems, their architecture, and their principles of operation. Additionally, the course aims to introduce parallel and distributed programming, with the goal of reducing resolution times for particularly complex problems through the
coordinated use of a large number of computing units.
Knowledge and comprehension
Students will understand the principles underlying HPC systems and how to organize a resolution strategy for an algorithm that can benefit from the
presence of multiple computing units.
Applying knowledge and comprehension
Upon completion of the course, students will be able to create simple parallel and distributed applications that leverage the increased computing capacity of an HPC system. Students will also be able to execute the developed algorithms using an existing computing infrastructure.
Judgement skills
Students will develop the ability to identify particular types of problems for which the use of a parallel or distributed approach is significantly helpful.
Communication skills
The students will acquire the technical-scientific language commonly used in this discipline, also thanks to the study and to the practice.
Learning skills
Students who pass the exam will have learned the paradigms to apply parallel and distributed computing techniques to solve complex problems, utilizing the computing capabilities of an HPC system.
|
Educational objectives The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.
Specific objectives
a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.
b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.
c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.
d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.
e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.
Statistica Applicata
Obiettivi generali
L'obiettivo formativo primario dell’insegnamento è lo studio di Temi di Statistica Applicata. Data la vastità del dominio applicativo si focalizzerà in particolare sull'analisi quantitativa applicata al settore della finanza e delle metodologie e algoritmi di automazione dei mercati. Gli studenti dovranno saper risolvere i problemi analitici necessari per applicare i suddetti metodi e saper interpretare i risultati che discendono dalla loro applicazione a dati reali.
Obiettivi specifici
a) Conoscenza e capacità di comprensione
Dopo aver frequentato il corso gli studenti devono acquisire un profilo completo di analista quantitativo ("quant"), sia per quanto concerne la conoscenza delle metodologie, sia per quanto riguarda la capacità di implementarle nell'ambito di linguaggi moderni di programmazione, quali ad esempio c#, c++, vb.net, j# in generale l'ambiente
integrato di sviluppo Visual Studio.
b) Capacità di applicare conoscenza e comprensione
Al termine del corso gli studenti sono in grado modellizzare tutte le fasi dell'analisi quantitativa, inclusi i processi di simulazione, modellizzazione delle strategie, calcolo di indici di performance e costruzione delle distribuzioni empiriche di probabilità dei principali indici di performance.
c) Autonomia di giudizio
Gli studenti sviluppano capacità critiche attraverso la creazione di nuove strategie e il corrispondente studio simulativo, sia con tecniche di backtesting che forward testing.
Sia con dati simulati, mediante misture di processi aleatori, sia mediante le serie storiche osservate nel passato.
d) Abilità comunicativa
Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, che deve essere opportunamente utilizzato sia nelle prove scritte intermedie e finali che nelle prove orali. Le abilità comunicative vengono sviluppate anche attraverso attività programmazione in laboratorio e anche attività di ricerca in gruppi.
e) Capacità di apprendimento
Gli studenti che superano l’esame hanno appreso i metodi di analisi che consentono loro di affrontare problemi concreti di "quantitative analysis" e se necessario di poter intervenire su qualunque fase del complesso processo che va dall'acquisizione dello stream di dati finanziari in tempo reale, alla sua analisi statistica ai fini della creazione di strategie di automazione, alla valutazione quantitativa delle metodologie. Hanno inoltre la capacità di implementare qualunque metodologia richiesta in un ambiente di sviluppo moderno, di programmazione OOP, e interfacce grafiche avanzate.
The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.
Specific objectives
a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.
b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.
c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.
d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.
e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.
|
Educational objectives The primary educational objective of the laboratory is students' learning and practice of the main
tools for Data Driven Decision Making, that is the use of computer tools to analyze data and
formalize optimization or decision models and produce decisions that create value.
Knowledge and ability to understand
After attending the laboratory, students will be able to use decision support methods (like,
the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer
algorithms for modelling multicriteria decision and optimization problems.
Ability to apply knowledge and understanding
The models are formalized in the realm of problems. The most appropriate quantitative
method, experimenting with the effectiveness of the problem.
Autonomy of judgment
Students develop critical skills through the application of modeling, analysis and
optimization to a broad set of decision problems. They also develop the critical sense
through the comparison between alternative solutions to the same problem using
methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.
Communication skills
Students, through the study and the carrying out of the practical exercises, acquire the
technical-scientific language of the course, which should be used in the tests.
Communication skills are also developed through group activities.
Learning ability
Students who pass the exam have acquired the main methods of analysis and optimization
of decision problems that allow them to face decision-making and quantitative
management in competitive nowadays enterprises.
|
