ISLA IPGT 25289

Data Science for Finance

• ApresentaçãoPresentation

  The Data Science for Finance curricular unit allows you to apply a set of data science techniques to the financial sector, including statistical analysis and data modeling. The aim is to develop knowledge in financial data analysis, with support for Python, apply statistical and machine learning methods to financial problems, and interpret analytical results.
• ProgramaProgramme

  Description of contents: 1. Algorithm and data structure 2. Introduction to Python Programming 2.1. History and overview 2.2. Installation and versions 2.3. Data and variable types 2.4. Data list 2.5. Functions and cycles 3. Python Modules 3.1. Module overview and installation 3.2. Numpy, Scipy, Matplotlib, Statsmodels, Pandas 3.3. Other useful Python modules for finance 4. Data sources and data extraction output 4.1. Data sources (financial APIs, Excel file, text file) 4.2. Extracting data output to a text, csv and Excel file using Pandas Dataframe 5. Financial Applications 5.1. Net present value and internal rate of profitability 5.2. Stock price movements, profitability rate distributions 5.3. Bond Valuation 5.4. CAPM model and Beta coefficient calculation 5.5. Time series analysis 5.6. Portfolio theory 5.7. options 5.8. VaR and Expected Shortfall 5.9. Monte Carlo simulation
• ObjectivosObjectives

  This course introduces students to data science for financial applications using the Python programming language and a set of packages from its ecosystem. Objectives of the UC: Characterize data analysis to improve financial decision-making; Provide students with a basis for performing data analysis in research areas related to the financial sector and other areas not related to the financial sector.
• BibliografiaBibliography

  Berk, Jonathan e Peter DEMarzo: Corporate Finance (5th ed.), Person Education, 2019. Yan, Yuxing: Python for Finance (2nd ed.), Packt, 2017. Hilpisch, Yves: Python for Finance (2nd ed.), O´Reilly, 2018.
• MetodologiaMethodology

  The course is structured in two main components that complement each other: theoretical and expository classes and practical and experimental classes. In the theoretical classes, the defined syllabus is explored, and students are invited to actively participate in the classes. In the practical classes, works are proposed with the use of the Python platform, for the application of the knowledge acquired in the theoretical classes.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.

  Exemplo:

  Descrição	Data limite	Ponderação
  Teste de avaliação		60%
  Trabalho de avaliação		40%

   

  Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...