Estatística e Gestão de Dados Urbanos

Knowledge Mathematics at the first year of BSc. level and Probability and Statistics at a non higher education
level.

The following teaching methods will be used in the classes: expository with discussion; problem solving under the guidance of the teacher; flipped classroom. Some of the problems will allow the use of common computational tools in data analysis. All the elements of the syllabus will be covered within the framework of a case study on urban data that will be developed throughout the semester.

Understand the management and governance processes of urban management platforms.
Understand the different types of data, descriptive methods and statistical inference.
Understand methodologies and processes for analyzing and processing data.
Know how to use data analysis software.

1.Urban management platforms
Case studies.
Considerations on platform architecture and data governance.

2.Descriptive Statistics
Urban data sources: sensors, images, administrative data.
Frequency distribution: tables and graphical representation.
Summary measures of a variable.
Measures of association between two variables.

3.Main probability distributions
Probability topics.
Concept of random variable.
Discrete distributions: binomial and Poisson.
Continuous distributions: uniform, normal and exponential.

4.Notions of statistical inference
Confidence intervals for the mean, proportion and variance.
Hypothesis tests for mean, proportion and variance.

5.Data analysis process and CRISP-DM methodology
Application to case studies.

The student may choose between continuous assessment or final exam assessment.
In all assessment moments, the student may consult the tables prepared by the DFM, a sheet prepared by themselves, and use a calculator.

• Continuous Assessment – consists of a group assignment (mini research project) and the assessment of regular participation in class activities. The student may obtain approval if the sum of the grades of the previous components is equal to or greater than 9.5 out of 20.

The mini research project will consist of the study, application, or development of topics related to the course unit, the submission of the corresponding report, and its presentation/defense, with a grade of up to 8 points. The presentation of the project must take place no later than during the exam support period. The assessment of regular participation in class activities will be carried out through problems proposed throughout the semester, with a total grade of 12 points. The dates for these problems will not be announced to students in advance.

• Final Exam – consists of an exam graded out of 20 points, which will cover all chapters of the syllabus, in particular the ability to use computational tools for data analysis. Students who did not pass the continuous assessment, who did not choose continuous assessment, or who meet the conditions to improve their grade may take the final exam.

In either method of assessment, any student with a final grade higher than 17 out of 20 must undergo a special defense exam; otherwise, their grade will be recorded as 17.

NAO