Data Processing

Recommended Prerequisites

Calculus I.

Teaching Methods

The teaching methods are based on a combination of conventional classes where the topics are motivated and introduced, with the support of slides, software and illustrations (theoretical classes) and classes demonstrating concepts and their computational implementation (practical classes). In the course of the classes, the students consolidate the learning outcomes with projects carried out individually or in groups, where the methods and tools are applied autonomously under the supervision of the teacher. Tutorial lessons provide further support to the students’ efforts.

Learning Outcomes

This unit aims acquiring basic competences of exploratory data analysis, modeling of variability and its expression in a consistent and rigorous way. It is therefore an introduction to the data universe, presenting methodologies to visualize and summarily describe data, in order to extract the main patterns of variability. Then, such patterns are abstracted into mathematical models that are able to describe their behavior with reasonable accuracy, providing the basis for other higher level tasks (probabilistic models). Methodologies to quantify data quality rigorously are also taught, via the expression of data uncertainty and the way it propagates through mathematical expressions.

Work Placement(s)

No

Syllabus

1. Exploratory data analysis

a. Charts, tables and statistics

b. Computational tools (for example: spreadsheet applications; statistical analysis software; advanced calculus platforms).

2. Main probability distributions

a. Continuous Distributions

b. Discrete Distributions

3. Making decisions in contexts of uncertainty / variability

a. Hypothesis testing for population means

b. Hypothesis testing for quality of fit (distributions)

4. Measurement uncertainty and propagation.

Head Lecturer(s)

Marco Paulo Seabra dos Reis

Assessment Methods

Assessment
Resolution Problems: 20.0%
Project: 30.0%
Exam: 50.0%

Bibliography

Reis, M. S. Estatística Para a Melhoria de Processos – A Perspectiva Seis Sigma. Coimbra: Imprensa da Universidade de Coimbra, 2016.

Montgomery, D. C.,  Runger, G. C. Applied Statistics and Probability for Engineers. 6th ed. New York: Wiley, 2014.

Vining, G.,  Kowalski, S. M. Statistical Methods for Engineers. 3rd ed. Duxbury: Thomson, 2010.

JCGM. Evaluation of measurement data - Guide to the expression of uncertainty in measurement (JCGM 100:2008, GUM 1995 with minor corrections) Paris: JCGM, 2008.