Decision Support Systems

Scholar Year: 2020/2021 - 1S

Courses

Head Teacher Responsability Cláudio Miguel Garcia Loureiro dos Santos Sapateiro Head

Weekly workload Hours/week T TP P PL L TC THE EL OT OT/PL TPL S Type of classes 3 0 Lectures Type Teacher Classes Hours Theorethical and Practical classes Totals 1 3,00

Teaching language

Portuguese

Intended learning outcomes (Knowledges, skills and competencies to be developed by the students)

Provide the students with knowledge related to decision support systems (DSS) and the decision making. Will
be discussed topics such as the life cycle of a DSS, Data Warehouses, analysis, design and implementation,
Data Mining, its objectives, models and associat

Syllabus

1. FROM INFORMATION TO KNOWLEDGE
2. ORGANIZATIONAL KNOWLEDGE
3. KNOWLEDGE MANAGEMENT ORGANIZATIONAL
4. BUSINESS INTELLIGENCE
4.1. Data Warehousing
4.2. Exploring a Data Warehouse
4.3. Technological Infrastructure Support Business Intelligence
5. KNOWLEDGE DISCOVERY IN DATABASES
5.1. Process Phases
5.1.1. Selection of Data
5.1.2. Data Treatment
5.1.3. Pre-processing of data
5.2. Data Mining
5.3. Interpretation of Results
6. DATA MINING
6.1. Data Mining Tasks
6.1.1. Classification
6.1.2.Segmentation
6.1.3. Summary
6.1.4. Modelling Dependencies
6.2. Data Mining Techniques
6.2.1. Decision Trees
6.2.2. Association Rules
6.2.3. Linear Regression
6.2.4. Artificial Neural Networks
6.2.5. Genetic Algorithms
6.2.6. Nearest Neighbours
7. DATA ANALYSIS WITH OLAP TOOLS
8. DATA ANALYSIS WITH DATA MINING TECHNIQUES

Software

MS SQL Server 2012

MS SQL Server 2012

Demonstration of the syllabus coherence with the UC intended learning outcomes

Provide the students with knowledge related to decision support systems (DSS) (1,2 and 3) and decision making
(4 and 5). Will discuss topics such as the life cycle of a DSS, Data Warehouses, analysis, design and
implementation (5), Data Mining, its objectives, models and associated technology (6). Data Mining Algorithms
(6). Using data mining tools and practical work with tools to support decision making (7 and 8).

Teaching methodologies

In practical classes the method used will be the expository.
In laboratory classes the method will use the lecture method to matters considered important for understanding the tools to use. Will be held computer exercises with the support of teachers, some of them with a report and deliver implementation and monitoring of a final project that covers the subject given.

Demonstration of the teaching methodologies coherence with the curricular unit's intended learning outcomes

To impart knowledge & familiarize students with principal concepts & theories there will be used expositoryparticipatory
method; to anchor the matter there will be presented & discussed practical cases; emphasis will
be placed upon active methodologies; for students to demonstrate creativity, autonomy & to involve them into a scientific investigation, as well as to integrate knowledge acquired during the classes, there will be developed
individual projects on selected topics.

Assessment methodologies and evidences

The final grade is obtained by the contribution of the following components:
1) Laboratory (NL)
2) Examination (NE)
The lab grade will be at least 10 points and consists of papers throughout the semester and the final paper.
Note: The final work of the module will discuss mandatory if it does not perform, the work is considered as
undeliverable (minimum score 10 points).
The exam has the minimum score 8 points.
Final grade
Final Rating = 60% NE + 40% NL

Primary Bibliography

Secondary Bibliography