Advanced Topics in Data Science

Code:

CC4061

Acronym:

CC4061

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master 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
E:BBC	1	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:A_ASTR	5	Study plan since academic year 2024/2025	1	-	6	42	162
			2
M:BBC	22	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	21	Official Study Plan since 2018_M:DS	1	-	6	42	162
M:ECAD	4	Study plan since 2021/2022.	2	-	6	42	162

Teaching language

Portuguese and english
Obs.: All course material are provided in English

Objectives

Identification and application of data mining techniques for knowledge extraction from various data sources. The focus will be on association rules, sequence mining, recommendation systems, link analysis, information retrieval, and text minin

Learning outcomes and competences

At the end of the course, the student should be 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

Presencial

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.

Program

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

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

3. Information Retrieval
• pre-processing
• retrieval models
• retrieval evaluation

4. Text Mining
• document representation in vector spaces
• document clustering
• document classification
• sentiment and emotion analysis

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 topics covered in the program will be exposed and some practical examples of application will be provided. Solving exercises in the practical part and carrying out group work with final presentation and discussion of the results.

Software

R
RStudio

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	40,00
Exame	60,00
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%.

At least 70% attendance in theoretical classes and practical laboratory sessions.

Minimum grade of 30% in final exam.

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 = 0.60 * FE + 0.40 * PA 

where,
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 will not be approved.

The supplementary exam will be quoted to 60% (12 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.

 

Special assessment (TE, DA, ...)

The student can improve only the theoretical grade by taking the supplementary exam.
The requirement for minimum attendance in classes does not apply.

Classification improvement

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

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

Observations

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