Modelling and Data Analysis I
Keywords |
Classification |
Keyword |
OFICIAL |
Management Studies |
Instance: 2024/2025 - 2S 
Cycles of Study/Courses
Teaching Staff - Responsibilities
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
Modules:
- Tools: Python and Jupyter;
- Modelling and Simulation in Economic and Financial models;
- Data Analysis, introduction to Big Data Analysis:
- Numerical Errors: practical consequences.
Mandatory literature
Yves Hilpisch; Python for Finance: Analyze Big Financial Data, O'Reilly, 2014. ISBN: 9781491945285
Clifford Ang; Analyzing Financial Data and Implementing Financial Models Using R, Springer, 2015. ISBN: 978-3-319-14075-9
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 explortation).
Software
Jupyter
Python
R
Julia
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% project (work group)