Statistics for Data Science

Category: Discipline
  • Não
  • This course introduces the fundamental concepts of statistics for data science, including probability distributions, sampling techniques, statistical inference, exploratory data analysis and curve fitting. In addition, the course introduces statistical modeling techniques, including linear regression and analysis of variance, as well as multivariate analysis techniques, such as principal component analysis and clustering.
  • Semestral

  • 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

    Primeira Frequência

    30-04-2025

    40%

    Segunda Frequência

    11-06-2025

    40%

    Apresentação de trabalho Fração A

    		              07-05-2025

    25%
                                            Apresentação de trabalho Fração B              16-06-2025                   25%
                                                           Participação                     10%

    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...

     
  • 1. introduction to statistics and probability 2. Sampling techniques 3. Statistical inference 4. Exploratory data analysis 5. Statistical decision theory, hypothesis tests and significance tests. 6. Theory of small samples. Student's t distribution and Chi square distribution. 7. Curve fitting and the least squares method 8. Statistical modeling 9. Multivariate analysis
  • 1. Understand the fundamental concepts of probability and probability distributions; 2. Apply sampling techniques to collect and analyze data; 3. Perform statistical inference, including hypothesis testing and confidence intervals; 4. Perform exploratory data analysis using graphs and descriptive statistical measures; 5. Apply statistical modeling techniques, including linear regression and analysis of variance; 6. Apply multivariate analysis techniques, such as principal component analysis and clustering.
  • Mandatory
  • The teaching methodology will be based on lectures, practical examples and exercises, as well as the use of data analysis software (e.g. R commander or Jamovi). Students will also be encouraged to develop practical projects involving the analysis of real data, with a focus on interpreting and communicating the results. Assessment will be based on individual and group work, as well as two tests. Students will also be assessed on their ability to apply statistical concepts to real problems and their ability to communicate data analysis results clearly and concisely with appropriate technical terminology.
  • Português
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. Springer. Steele, B., Chandler, J., & Reddy, S. (2018). Statistics for data science: A comprehensive introduction. O'Reilly Media, Inc. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • 4
  • 0
  • 6
  • 1
  • IPLUSO6382-23558
  • Statistics for Data Science
  • 23558
  • 6382
  • Computer Applications for Data Science

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