Analysis of Complex Data

Code:

MECD11

Acronym:

ADC

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Data Science and Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MECD	22	Syllabus	2	-	6	42	162

Teaching language

English

Objectives

The general aim of the course is to create skills in the treatment of complex data. The goal is to develop the ability to process data that are not simply table of i.i.d. observations. The types of complex data (CD) covered include those that are important today (graphs, and spatio-temporal data). However, the course will be flexible to accommodate new types or sources of data. Students will be prepared to develop techniques for new types of data they might be confronted with in their professional lives.

Learning outcomes and competences

The learning outcomes are:

• understand the nature of common complex data types and their impact on data analysis methodologies, in particular regarding algorithms and evaluation.
• understand the most popular approaches as well as the state of the art for analyzing the most common types of complex data.
• configure and use technologies for the analysis of complex data types.
• develop (create/adapt) methodologies for the analysis of new types and sources of complex data.

Working method

Presencial

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

Program

Advanced topics in recommender Systems

Natural Language Processing/Text mining

Social network analysis

Analysis of time-series data

Analysis of spatio-temporal data

Trends in the analysis of complex data

Mandatory literature

Burke, R.; Recommender Systems: An Introduction, 2012 (https://doi.org/10.1080/10447318.2012.632301)
David Easley; Networks, crowds, and markets
Hyndman, R.J. & Athanasopoulos, G.; Forecasting: Principles and Practice (3rd edition), 2021 (https://otexts.com/fpp3/)
Dan Jurafsky and James H. Martin; Speech and Language Processing (https://web.stanford.edu/~jurafsky/slp3/)

Teaching methods and learning activities

Content presentation by the teacher.

Solving theoretical and practical exercises by the students.

Discussion of scientific articles.

Software

Python
R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	73,00
Estudo autónomo	50,00
Frequência das aulas	39,00
Total:	162,00

Eligibility for exams

In case of a missing component of the evaluation, the respective grade is 0 (zero) values.

Minimum grade in each component: 8.0 (out of 20).

Calculation formula of final grade

50% Mini-tests + 50% Project

Special assessment (TE, DA, ...)

Students with worker or equivalent status, who are exempt from class attendance must fulfill every evaluation component, and should contact the teachers for regular meetings regarding their progress in the practical work.

Classification improvement

Grade improvement for the mini-tests may be done in the special season (recurso) of the year in which the student is approved.