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Advanced Topics in Data Science

Code: CC4061     Acronym: CC4061     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 2S Ícone do Moodle Ícone  do Teams

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
M:BBC 8 The study plan since 2018 1 - 6 42 162
M:CTN 0 Official Study Plan since 2020_M:CTN 1 - 6 42 162
M:DS 16 Official Study Plan since 2018_M:DS 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Rita Paula Almeida Ribeiro

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
Rita Paula Almeida Ribeiro 2,00
Luís Fernando Rainho Alves Torgo 1,00
Mais informaçõesLast updated on 2021-02-12.

Fields changed: Calculation formula of final grade, Provas e trabalhos especiais, Melhoria de classificação, Componentes de Avaliação e Ocupação, Tipo de avaliação, Lingua de trabalho, Obtenção de frequência

Teaching language

Portuguese and english


Identification and application of data mining techniques to extract knowledge from different data sources (e.g. transactions, web, text).

Learning outcomes and competences

The student is able to:
- recognize different problems solvable through the use of data mining techniques discussed and detailed in content;
- identify and specify data mining tasks similar to those discussed;
- obtain and pre-process data for the algorithms and tasks addressed;
- understand and use data mining algorithms;
- obtain, interpret, evaluate and use data mining models;
- Implement some of the algorithms and propose changes to improve them.

Working method


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

Students should be familiar with the basic concepts of data mining and have knowledge of programming languages used in data mining tasks, such as the R or Python language.


1. Association Pattern Mining
• frequent itemsets and association rules
•  Apriori algorithm
•  itemsets summarization and rules selection
•  FP-Growth algorithm 

2. Sequential Pattern Mining
•  GSP algorithm
•  PrefixSpan algorithm

3. Web Mining
• recommender systems
• link analysis
• information retrieval

4. Text Mining
• document clustering
• document classification

5. Outlier Mining 
• challenges 
• unsupervised techniques
• semi-supervised techniques
• supervised techniques

Mandatory literature

Liu Bing 1963-; Web data mining. ISBN: 978-3-642-19459-7
Hand David 1950-; Principles of data mining. ISBN: 978-0-262-08290-7

Complementary Bibliography

Charu C. Aggarwal; Data Mining - The Texbook, Springer, 2015. ISBN: 978-3-319-14141-1

Teaching methods and learning activities

Theoretical-practical classes where the themes covered in the program will be exposed and some practical examples of application will be provided.



Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Trabalho prático ou de projeto 30,00
Exame 70,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Elaboração de projeto 35,00
Estudo autónomo 84,00
Apresentação/discussão de um trabalho científico 1,00
Frequência das aulas 42,00
Total: 162,00

Eligibility for exams

The practical assignment is mandatory with a minimum grade of 30%.

Calculation formula of final grade

The assessment of the course is distributed, consisting of a final exam and a practical assignment at the end of the semester.

The final grade is calculated by the weighted average of the practical and theoretical grades through the formula:

FG = 0.70 * FE + 0.30 * PA 

FE is the grade of the final exam and
PA is the grade of the practical assignment.

Students who do not obtain a minimum of 30% in each component, i.e. 6 out of 20, will not be approved.

The supplementary exam will be quoted to 70% (14 out of 20) of the final grade.

Examinations or Special Assignments

The practical assignment will be announced in the middle of the semester and should be completed and presented by the end of the semester.


Classification improvement

The evaluation of the practical assignment is not subject to improvement.

The student can improve in the theoretical grade by taking the supplementary exam.

The supplementary exam will have two parts, each corresponding to a set of topics covered in the course.
Students can choose to improve both or one of the parts of the supplementary exam.
The final grade will be the best of the obtained grades.


All the provided material (slides, recommended books, etc.) is in the English language.
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