Data Analysis and Visualization

Code:

MCI0023

Acronym:

AVD

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 1S

Active?	Yes
Web Page:	https://sigarra.up.pt/feup/pt/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=519399
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Information Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MCI	17	Plano de estudos oficial	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Background:
After a season in which different companies/institutions very invested in data collection by computerizing its 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 and gain competitive advantage. From this need arises the Curricular Unit  (UC) of Data Analysis and Visualization (AVD).

Objectives:
The student should be able to:
(1) Use adequately descriptive statistics for data description;
(2) Use OLAP tools for analysis of data;
(3) To describe the different stages of the process of knowledge discovery CRISP;
(4) to use and analyze the results of some of the main interpretable methods of classification and regression;
(5) to use and interpret cluster analysis methods;
(6) to use and interpret methods of association rules;
(7) To analyze spatio-temporal data;
(8) To evaluate methods of analysis and data visualization.

Learning outcomes and competences

As a learning result, it is intended that students:

- Know the different types of AVD tasks.

- Identify issues for decision support that can be represented as AD tasks.

- Know the main methods for each AVD task type and understand its essential function.

- Apply these methods to problems in decision support.

- Evaluate the results of an AVD project.

Working method

Presencial

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

It is not required to have attended any specific course. It, however, very useful to have some background in basic statistics.

Program

(1) Analysis and visualization of data in the context of information science;
(2) The process of knowledge discovery;
(3) Ferramentas OLAP
(4) Descriptive statistics;
(5) Data quality and pre-processing
(6) Clustering methods;
(7) Association rules;
(8) Methods of classification and regression;
(9) Analysis and visualization of data variables in function of time and space.

Mandatory literature

Matthew North; Data mining for the masses, 2012. ISBN: 0615684378
Moreira, João; Carvalho, André de; Horvath, Tomás; A general introduction to data analytics, WIley, 2018. ISBN: 978-1-119-29626-3

Complementary Bibliography

Aggarwal Charu C.; Data mining. ISBN: 978-3-319-14142-8

Comments from the literature

Students will be provided with materials for the practical lessons.

Teaching methods and learning activities

Theoretical classes are based on the presentation of course unit themes followed by practical exercices.

Software

Rapid Miner

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	0,00
Teste	60,00
Trabalho laboratorial	40,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	44,00
Trabalho laboratorial	60,00
Total:	164,00

Eligibility for exams

N/A

Calculation formula of final grade

MT1 - Minitest 1
MT2 - Minitest 2
PROJ - Group project

Final mark: 0.3 × MT1 + 0.3 × MT2 + 0.4 × PROJ;

Minimum grade: 0.5 × MT1 + 0.5 × MT2 ≥ 7.0.

The project will be assessed in two different moments, and each of them is worth 50% of the project mark.

Examinations or Special Assignments

The project (PROJ) is based on the execution of a group assignment (two people). The grade may be different for each element of the group.

Internship work/project

N/A

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 teacher periodic meetings to report work progress.

Classification improvement

Students may improve their test grades by attending an exam with the same format as the tests. The grade of the group project may only be improved in the next edition of this course.