Data Analytics Technology

Year
1

Academic year
2025-2026

Code
02038767

Subject Area
Informatics

Language of Instruction
Portuguese

Other Languages of Instruction
English

Mode of Delivery
Face-to-face

Duration
SEMESTRIAL

ECTS Credits
6.0

Type
Compulsory

Level
2nd Cycle Studies - Mestrado

Recommended Prerequisites

Programming, Introduction to Artificial Intelligence, Databases, Techniques for Pattern Recognition.

Teaching Methods

Theoretical classes with detailed exposition, using visual aids, of the concepts, principles and fundamental theories of Data Warehousing, OLAP and Data Mining.

Practical-laboratory practices, in which students are required to acquire knowledge about tools and techniques for developing practical applications involving OLAP and Data Mining and that, under the guidance of the teachers, develop a work (project).

Learning Outcomes

The objectives of this course are to study the main methodologies for developing business intelligence solutions and show how the techniques of Data Warehouses (DW), OLAP and Data Mining (DM) can be combined in building software solutions for decision support.
This course aims to boost the development of the following skills:
- Apply knowledge in practice: applying knowledge in the development of decision support systems in real environments;
- Autonomous learning, problem solving and decision-making: ability to identify sources of knowledge, finding solutions to the development of applications for data analysis taking into account the organizational needs, using the methodologies of DW, OLAP and DM;
- Oral and written communication, understanding the language of experts and non experts in the field: to communicate and justify the technical options in understandable language either for specialists or non specialists.

Work Placement(s)

Syllabus

- Introduction to data analysis: information and knowledge

- Data analysis in organizational knowledge management, business intelligence

- Data warehousing

- Introduction

- Multidimensional models

- Dimension tables

- Fact tables

- Extracting, transforming and loading data

- Optimizing and managing data warehouses

- Knowledge discovery - Data mining

- Introduction

- Data mining tasks

- Data mining pipeline

- Data analysis with OLAP and data mining

- Building and analyzing datamarts

- Case studies

Head Lecturer(s)

Catarina Helena Branco Simões da Silva

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

- Rick Sherman, Business Intelligence Guidebook - From Data Integration to Analytics, Morgan Kaufman, 2015
- C. Silva and B. Ribeiro, Aprendizagem Computacional em Engenharia, Imprensa da Universidade de Coimbra, 2020
- Simon Asplen-Taylor, Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy, Kogan Page Editors, 2022
- Ramesh Sharda et al., Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Pearson Education Limited, 2019
- Charu C. Aggarwal, Data Mining, Springer, 2015
- Ralph Kimball and Margy Ross, The Data Warehouse Toolkit, Kimball Group, 2013
- Foster Provost, Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, 2013