Probabilities and Statistics

Category: Discipline
  • Não
  • This Curricular Unit aims to provide a solid understanding of the fundamental concepts of Probability Theory and Statistics, with a focus on the applications of the CTeSP course in Computer Applications for Data Science. Students are introduced to the necessary mathematical formulation and to statistical and probabilistic tools that enable them to solve problems related to data analysis, information synthesis, statistical inference and hypothesis testing. In addition, the course highlights the importance of studying and modeling theoretical and sampling distributions, promoting the deepening of knowledge through the practical application of real cases. This approach reinforces the relevance of the curricular unit in the context of the higher vocational-technical course in which it is integrated, preparing students to face challenges related to the analysis and interpretation of data in the real world.  
  • Semestral

  • Descrição dos instrumentos de avaliação testes e trabalho prático com respetivas datas de entregae e apresentação. Ponderação na nota final.

    Descrição

    Data limite

    Ponderação

    Primeira frequência (1/ 2)

    25-11-2025

    40%

    Segunda frequência (2/ 2)

    12-01-2025

    40%

    Monografia

    A definir

    10%
    Assiduidade   10%

     

     
  • Description of contents     Probability Theory     Random Variables and Distribution Functions     Expected Values and Parameters     Theoretical Distributions     Sampling and Sampling Distributions     Parametric Estimation     Estimation Methods     Confidence Intervals and Regions     Significance Tests     Tests of Hypothesis     Analysis of Variance
  • Understand the fundamental concepts of Probability theory. Evaluate random variables (ordinal, discrete, and continuous) and their associated distribution functions. Apply and evaluate the use of different theoretical and sampling distributions. Develop skills associated with sampling and estimation techniques. Enable students to conclude on the significance and reliability of data analysis scenarios. To teach and systematize hypothesis testing and analysis of variance methodologies.
  • Mandatory
  • This course has two assessment methods: Continuous Assessment and Non-Continuous Assessment. 1. Continuous Assessment: The Continuous Assessment of this Curricular Unit is made up of two frequencies (F1 and F2), attendance (A) and a paper (P). The final mark is calculated as the arithmetic mean of the frequencies (NF = 0.4 * F1 + 0.4 * F2 + 0.1 * A + 0.1 * P).   2. Non-continuous assessment: Students can opt for a final exam. As an innovative methodology to support the teaching-learning process, this curricular unit incorporates the transposition of all theoretical material into the JAMOVI programme. JAMOVI is a freely accessible tool capable of mathematically and graphically supporting databases that can be described and interrogated as the syllabus of the lectures progresses.   Migration of theoretical concepts to Python for the creation of descriptive statistics workflow.
  • Português
  • Murteira,B., Antunes, M (2012) Probabilidades e Estatística, Vol I. Escolar Editora (ISBN: 9789725923559 Murteira,B., Antunes, M. (2012) Probabilidades e Estatística, Vol II. Escolar Editora (ISBN: 9789725923597) Navarro DJ and Foxcroft DR (2019). learning statistics with jamovi: a tutorial for psychology students and other beginners. (Version  0.70). DOI: 10.24384/hgc3-7p15
  • 4
  • 0
  • 6
  • 1
  • IPLUSO6382-620
  • Probabilities and Statistics
  • 620
  • 6382
  • Computer Applications for Data Science

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