Análise e Tratamento de Dados

Knowledge Mathematics and Probability and Statistics at BSc. level in Engineering.

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.

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.

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.

The student can choose between continuous evaluation or evaluation by final exam.

• Continuous evaluation - consists of a project and evaluation of regular class atendance. The student can get approval if the sum of the previous components is equal to or greater than 9.5 values.

The project will be about the study, application and development of program's related topics, report deliver and its presentation, the quotation will be of 8 values. Due date: exams' preparation period.

The evaluation of the regular class atendance will be materialized through problems proposed during the semester for a total of 12 values. The dates of realization of these problems will not be announced in advance to students.

• Final Exam - consists of conducting an exam quoted for 20 values, which will evaluate all chapters of the program contents, in particular R or Python and Excel usage skills. Students who have not passed the continuous assessment or who have not chosen continuous assessment and those who are allowed to improve their rating may access the exam.

In any of the evaluation methods, the student with a final grade higher than 16 values will have to undergo a special test of defense of the grade, otherwise, will get the grade of 16.

NAO