Base Knowledge
Knowledge Mathematics and Probability and Statistics at BSc. level in Engineering.
Teaching Methodologies
In the theoretical classes, the expository method with discussion will be used. Practical classes will be dedicated to problem solving under the guidance of the teacher. Some of the problems to be addressed will allow the introduction to the R or Python language and the manipulation and analysis of data with Excel by the students.
Learning Results
Objectives
Understanding the basic concepts to perform data analysis, using computational tools such as Excel, R or Python.
Competences
• Acquire the essential “language” related to data treatment and analysis allowing students to autonomously
develop their
future professional projects, as well as the capacity to integrate multidisciplinary teams of experts and clients.
• Know how to code using the programming languages (R or Python) and report results of data analysis.
Program
1. Introduction to data analysis
Design of experiments. Data types. Importance of Statistic importance. Milestones of a statistical study.
2. Descriptive statistics
Data summary and display. Indicators of central location and variability. Indicators of
symmetry and skewness. Correlation and independence.
3. Statistical inference
Estimation and hypothesis testing. Inference on parameters of a Normal population and others. Statistical models. Goodness of fit and independence.
4. Reliability
Basic concepts. Most relevant parametric models. Applications.
5. Regression models
Simple and multiple linear regression. Checking model adequacy. Nonlinear models.
6. Classification methods.
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Recommended (available for free online)
Professor’s notes, available in Moodle.
Several authors (2020), ALEA – Ação Local Estatística Aplicada, Instituto Nacional de Estatística, http://www.alea.pt
Dunn, K. (2020) – Process Improvement Using Data, https://learnche.org/pid/
Complementary
Farinha, J. (2018) – Asset Maintenance Engineering Methodologies, CRC Press.
Ross, Sheldon (2014) – Introduction to Probability and Statistics for Engineers and Scientists, Elsevier
Ryan, T. (2007) – Modern Engineering Statistics, Wiley
R Core Team (2022)- An Introduction to R – Notes on R: A Programming Environment for Data Analysis and Graphics, https://cran.r-project.org/doc/manuals/R-intro.pdf, Version 4.2.1, 23/06/2022
Shaw, Z. (2017) – Learn Python 3 the Hard Way, Addison-Wesley Professional.
Pedrosa, A. e Gama, S. (2018) – Introdução Computacional à Probabilidade e Estatística com Excel, Porto Editora