Publication in the Diário da República: Despacho n.º 10344/2023 de 09/10/2023
4 ECTS; 1º Ano, 1º Semestre, 35,0 TP , Cód. 62231.
Lecturer
- Eugénio Manuel Carvalho Pina de Almeida (1)(2)
(1) Docente Responsável
(2) Docente que lecciona
Prerequisites
N/A
Objectives
O1 - Development of a critical spirit that allows understanding, interpreting and applying knowledge in the field of mathematics and statistics related to this area;
O2 Application of logical reasoning to specific problems, using mathematical and statistical tools;
O3 Knowledge and development of skills for analysis and problem-solving in the area of ??security and civil protection, namely in the preliminary study of time series associated with the main extreme natural events.
O4 Develop skills for presenting statistical results in digital environments, namely dashboards, mobile and the internet.
Program
1. Brief Notions of Analysis in R
1.1 Sets of Numbers
1.1.1 Sets of NATURAL Numbers;
1.1.2 Integer Number Sets;
1.1.3 Sets of RATIONAL Numbers;
1.1.4 Sets of REAL Numbers;
1.2 Operations between numbers and their properties
1.2.1 Commutative, associative and distributive properties;
1.2.2 Rules of signs, potentiation and exponentiation
1.3 Concept of real function of real variable (RVF).
1.3.1 Study of the affine function, quadratic function and exponential function.
1.3.2 Graphical representation of functions.
2 Basic notions of statistics
2.1 Distinction between population and sample
2.2 Sampling
2.3 Statistical unit and statistical data
2.4 Classifying data according to its nature
2.5 Methodology for solving a statistical problem
2.6 Proposed exercises
3. Descriptive Statistics
3.1 Forms of tabular and graphical representation
3.1.1 Frequency table for univariate data
3.1.1.1 Discrete qualitative or quantitative data
3.1.1.2 Continuous quantitative data
3.1.2 Graphical representation of univariate data
3.1.3 Contingency table for bivariate data
3.1.4 Graphical representation of bivariate data
3.1.5 Exercises
3.2 Descriptive measures
3.2.1 Measures of location
3.2.1.1 Central tendency
3.2.1.1.1 Arithmetic mean
3.2.1.1.2 Mode
3.2.1.1.3 Median
3.2.1.1.4 Comparing the mean, median and mode
3.2.1.2 Exercises
3.2.1.2 Non-central tendency
3.2.1.2.1 Quantiles
3.2.2 Measures of dispersion
3.2.2.1 Absolute measures
3.2.2.1.1 Total amplitude
3.2.2.1.2 Interquartile range
3.2.2.1.3 Mean absolute deviation
3.2.2.1.4 Variance
3.2.2.1.5 Standard deviation
3.2.2.2 Relative measures
4. Applying Power Business Inteligency (PBI) to data visualization and interpretation
4.1 Data transformation
4.2 Error correction
4.3 Creating and interpolating columns
4.4 Data models
4.5 Visual components - graphs, tables, maps, cards, etc.
4.6 Associating visual components with data
4.7 Formatting visual components
4.8 Composing and publishing reports
4.9 Creating dashboards and pdfs of reports
4.10 Viewing reports and dashboards on various types of devices.
Evaluation Methodology
Grade: 0 to 20 points.
Assessment by Frequency:
- A written test (T1) to assess knowledge;
- A practical assignment (T2) to apply knowledge related to Chapter 4, with preparation and presentation of a report.
- The final frequency grade (NFF) will be the weighted average between the result of the written test, (T1), and the result of the practical assignment to apply knowledge (T2), and is obtained by the formula: NFF = 4/5*T1+1/5*T2
The student is exempted from the exam if he/she has a final assessment grade greater than or equal to 10 points.
Assessment by Exam:
- A written test (E1) to assess knowledge;
- The final frequency grade (NFF) will be the weighted average between the result of the written test, (E1), and the result of the practical assignment to apply knowledge (T2), and is obtained by the formula: NFF = 4/5*E1+1/5*T2
Bibliography
- Andrade, R. e Calapez, T. e Melo, P. e Reis, E. (2021). Estatística Aplicada . (Vol. Vol. 1). Portugal: Edições Sílabo
- Deckler, G. e Powell, B. (2021). Microsoft Power BI Cookbook: Gain expertise in Power BI with over 90 hands-on recipes, tips, and use cases. : Packt Publishing Ltd.
- Knight, D. e Ostrowsky, E. e Pearson, M. e Schacht, B. (2022). Microsoft Power BI Quick Start Guide: The ultimate beginner's guide to data modeling, visualization, digital storytelling, and more. : Packt Publishing Ltd.
- Rocha, H. e Martins, R. e Pascol, R. (2021). Estatística Descritiva para as Ciências Sociais. Portugal: Edições Sílabo
Teaching Method
. Presential
M1: Theoretical classes
M2: Theoretical-practical classes
M3: Mentoring Guidance
M4: e-learning
2. Autonomous:
M5: consultation of resources on the internet
M6: Resolution of additional exercises
Software used in class
Power BI data visualization by Microsoft
