Data Analytics

The classes are theoretical-practical in which the concepts and statistical techniques are introduced and explained, followed by the
presentation of application examples. Activities involving the presentation, resolution and discussion of problems with cross-sectional and
temporal data are developed using an appropriate software.

The statistical analysis of data is essential for decision making in scenarios of uncertainty, with many applications in the most diverse
scientific fields. In this curricular unit, it is intended that students understand and apply the studied statistical techniques, and know how to
interpret their results in a critical way.
At the end of the semester, the students should be able to
– create a data dictionary;
– perform a summary evaluation of the quality of the data based on the defined quality dimensions;
– perform univariate and bivariate descriptive analysis;
– transform and/or create new variables to resolve some more technical issues of the analysis and/or bring new insights;
– detect anomalies in a data set;
– perform descriptive analysis of a time series.
At the same time, the student must be able to perform the tasks using appropriate software.

1. Framework: objectives of descriptive and diagnostic analysis; particularities of temporal data.
2. Descriptive analysis
2.1 Univariate analysis
2.2 Bivariate analysis
3. Diagnostic analysis
3.1 Transformation and creation of new variables
3.2 Anomaly detection: missing values and outliers – first approach
4. Introduction to time series
4.1 Components
4.2 Trend lines
4.3 Decomposition

NAO