Data Analysis in Software Engineering

Code:

MESW0009

Acronym:

ADES

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Software Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MESW	50	Syllabus since 2016/17	1	-	6	42	162

Teaching language

English

Objectives

Background:
After a period in which different companies/institutions invested in data collection by digitalization of their operations (e.g. sensors, GPS systems), and in which many and varied new data sources have emerged (e.g. social networks), there is now the need to place such data at the service of those companies. The goal is to be able to extract knowledge from these data in order to improve efficiency in the processes and gain competitive advantage. The Data Analysis and Software Engineering (ADES) course stems from this need .

Objectives:
The student should be able to: develop simple descriptive and predictive data mining (DM) projects involving the most traditional tasks: clustering, association, classification, and regression.

Learning outcomes and competences

As a learning result,students should be able to:
1. identify problems that can be solved with DM;
2. follow an appropriate methodology to solve DM simple problems;
3. superficially understand the behavior of the methods involved;
4. evaluate results, both from a technical and   application domain perspectives.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

It is not required to have attended any specific course. It, however, important to have some background in probabilities and statistics.

Program

• Introduction to data mining.


• DM projects: methodology; exploratory data analysis.


• Clustering: introduction; algorithms.


• Classification: introduction; evaluation measures.


• performance estimation; distance-based and probabilistic algorithms.


• Data preparation: preprocessing methods.


• Binary classification: evaluation; dealing with class imbalance.


• Regression: introduction; evaluation measures; basic algorithms.


• Search-based and optimization-based algorithms for predictive models; ensemble learning.


• Frequent pattern mining and Recommender Systems.


• AutoML & Metalearning.

Mandatory literature

João Pedro Carvalho Leal Mendes Moreira; A^general introduction to data analytics. ISBN: 978-1-119-29624-9

Complementary Bibliography

Provost Foster; Data science for business. ISBN: 978-1-449-36132-7
Charu C. Aggarwal; Data mining. ISBN: 978-3-319-14142-8
João Gama, André Ponce de Leão Carvalho, Katti Faceli, Ana Carolina Lorena, Márcia Oliveira; Extração de conhecimento de dados, Edições Sílabo, Lda., 2012. ISBN: 978-972-618-698-4
Matthew North; Data mining for the masses, 2012. ISBN: 0615684378

Comments from the literature

Tere will be additional documentation authored by the lecturers of the curricular unit.

Teaching methods and learning activities

The classes are used to discuss the corresponding topics and to carry out exercises and the project.

Software

Rapid Miner
R language
Python

keywords

Physical sciences > Computer science > Cybernetics > Artificial intelligence
Physical sciences > Computer science > Systems design > Neural networks

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Participação presencial	0,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	60,00
Estudo autónomo	60,00
Frequência das aulas	44,00
Total:	164,00

Eligibility for exams

NA

Calculation formula of final grade

0.5*Exam + 0.5*Assignment;
Minimum grades: Exam >= 7.0; Assignment >= 7.0.

Examinations or Special Assignments

The assignment consists of a group project. The grade may be different for each element of the group.

Internship work/project

NA

Special assessment (TE, DA, ...)

Students taking exams under special regimes are expected to previously submit the project required for this course as ordinary students.Students not atteding the classes have to submit and present their work in the established deadlines. These later students should take the initiative to establish with the teatcher periodic meetings to report work progress.

Classification improvement

Students may improve their exam grade only. This can be done in the appeal exam on the current edition of the course or in the subsequent one.