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Modelling and Data Analysis I

Code: 2MiF08     Acronym: MDA 1

Keywords
Classification Keyword
OFICIAL Management Studies

Instance: 2020/2021 - 2S Ícone do Moodle

Active? Yes
Course/CS Responsible: Master in Finance

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIF 63 Official Syllabus after 2020-2021 1 - 3 21 81

Teaching Staff - Responsibilities

Teacher Responsibility
José Abilio de Oliveira Matos

Teaching - Hours

Theoretical and practical : 1,50
Type Teacher Classes Hour
Theoretical and practical Totals 2 3,00
José Abilio de Oliveira Matos 1,50
Paulo José Abreu Beleza de Vasconcelos 1,50

Teaching language

English

Objectives

The course aims to develop the skills to use and program in some of the most important software for modeling and data analysis.

Learning outcomes and competences


  • Implement programs for the analysis, characterization and comparison of financial data.

  • Build algorithms to simulate financial models.

  • Develop critical thinking in data analysis and simulation results.

Working method

Presencial

Program


  1. Introduction

  2. Jupyter working environment

  3. Programming and Data Analysis with R

  4. Python: introduction to Big Data Analysis

Mandatory literature

Clifford Ang; Analyzing Financial Data and Implementing Financial Models Using R, Springer, 2015. ISBN: 978-3-319-14075-9
Yves Hilpisch; Python for Finance: Analyze Big Financial Data, O'Reilly, 2014. ISBN: 9781491945285

Teaching methods and learning activities

The course is organized in lab sessions, based on modules. The teaching methodology in each module is structured as follows:


  • description of the financial problem to solve;

  • identification with explanation of the appropriate computacional methods for their resolution;

  • exercises and simulation (sedimentation and knowledge exploitation).

Software

Jupyter
Python
R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Apresentação/discussão de um trabalho científico 20,00
Teste 30,00
Trabalho prático ou de projeto 50,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 45,00
Frequência das aulas 21,00
Trabalho escrito 15,00
Total: 81,00

Eligibility for exams

According to the General Regulation for the Assessment of First Degreeand Master’s Degree students at the School of Economics and Management of the University of Porto all students enrolled in a course unit fulfill attendance requirements. (article 10th point 5)

Calculation formula of final grade

30% individual assessment + 70% group work
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