- Details
- Category: Discipline
- Maindonald, J. & Braun, W. J. (2010). Data analysis and graphics using R: an example-based approach. (3rd ed.). United Kingdom: Cambridge University Press. Murteira, B. & Antunes, M. (2012). Probabilidades e Estatística. (Vol. 1). Lisboa: Escolar Editora.
- Data Science
- 6321
- 620
- Probabilities and Statistics
- ISMAT6321-620
- 1
- 8
- 0
- 12
- Não
- Português
- The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises, with and without the use of a computer. Whenever appropriate, statistical software is used to explore concepts, interpret results and support the solution of real-world data analysis problems.
- Mandatory
- At the end of this course unit, students should be able to: LO1: Distinguish concepts and solve problems involving probabilities; LO2: Characterize random variables and use them to solve problems; LO3: Build confidence intervals; LO4: Perform parametric hypothesis tests and verify their assumptions; LO5: Perform non-parametric hypothesis tests; LO6: Adjust, interpret and use simple linear regression models for predictive purposes; LO7: Use R software for statistical data analysis.
- S1: Probabilities Random experiences, space for results and events Definitions of probability and Kolmogorov axiomatic Bayes' theorem S2: Random Variables and Discrete Distributions Discrete random variables and their characteristics Discrete Uniform, Binomial, Poisson, Geometric and Hypergeometric Distributions S3: Random Variables and Continuous Distributions Continuous random variables and their characteristics Continuous Uniform, Normal and Exponential Distributions S4: Joint probability distributions and complements Joint, marginal and conditional distributions Covariance and correlation Central Limit Theorem S5: Estimation Introduction to Inferential Statistics Point and interval estimation S6: Hypothesis testing Parametric hypothesis tests Nonparametric hypothesis tests Normality and homoscedasticity S7: Simple Linear Regression models S8: Data Analysis using Software R
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui a realização de dois testes escritos com uma ponderação de 35% cada, um trabalho de grupo (20%) e a participação ativa nas aulas (10%).
- Semestral
- The curricular unit Probability and Statistics belongs to the scientific area of Statistics and provides the probabilistic and inferential foundations required for data analysis. It develops skills to model random phenomena, perform statistical inference and support data-driven decision making, constituting a fundamental curricular unit in Data Science.
- Details
- Category: Discipline
- IT Engineering
- 587
- 7344
- Human-Machine Interaction
- ISMAT587-7344
- 3
- 6
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Design for Circular Economy
- 6334
- 23776
- The Circularity of Time: Promoting Change for Quality of Life and Well-being
- ISMAT6334-23776
- 1
- 5
- 931
- 12
- Não
- Português
- Optional
- Semestral
- Details
- Category: Discipline
- Sports Sciences
- 6154
- 22459
- Seminar (Internship)
- ISMAT6154-22459
- 3
- 12
- 841
- 12
- Não
- Português
- Optional
- Semestral
- Details
- Category: Discipline
- John V. Guttag, Introduction to Computation and Programming Using Python, 3rd Edition, MIT Press, 2021. Ernesto Costa, Programação em Python - Fundamentos e Resolução de Problemas (2.ª ed.), FCA - Editora de Informática, 2024. McKinney, W., Python para análise de dados: Tratamento de dados com pandas, NumPy & Jupyter (3ª ed.). O'Reilly, 2023.
- Data Science
- 6321
- 22356
- Programming Laboratory
- ISMAT6321-22356
- 1
- 6
- 0
- 12
- Não
- Português
- Since it is a laboratory curricular unit, the weekly theoretical class aims to present new subjects, frame the practical exercises, take stock of the learning situation and reflect on the results achieved. Students solve programming problems or carry out longer work, with a script, with accompaniment during practical classes. The evaluation corresponds to two practical evaluation works (20% + 20%) and one more theoretical-practical frequency (60%). The student will obtain approval if he / she has a grade equal to or higher than 9.5 values.
- Mandatory
- At the end of this curricular unit, the approved students will have consolidated the elementary knowledge of programming acquired in the previous subject (Introduction to Programming) and should be able to: - Develop programs with increased autonomy using the Python language. - Proficiently master the Python language and its data structures. - Know the fundamentals of vector and matrix programming using NumPy. - Take advantage of SciPy's numerical programming features. - Take advantage of the basic techniques of graphic programming (such as simple representation of points in 2D and 3D, histograms, bar graphs) using Matplotlib. - Use features of data manipulation libraries (pandas).
- 1. Python programming add-ons. 2. Introduction to programming applied to Datascience using Python.
A avaliação é composta pelas seguinte componentes:
Descrição
Data limite
Ponderação
Trabalho prático 1
A definir
20%
Trabalho prático 2
A definir
20%
Teste de avaliação
A definir
60%
- Semestral
- The primary objective of the Programming Laboratory course is to consolidate and deepen the fundamental programming knowledge acquired in the preceding course, Introduction to Programming, while fostering greater autonomy in developing programs using Python. As a laboratory-based course, the emphasis is on practical problem-solving, the development of small programs, and guided assignments, enabling students to apply the concepts they have learned in real-world contexts-specifically within the field of Data Science.
