Business Intelligence

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. In half of the lectures, it will be held a written test (5 minutes) focusing in the content of several articles made available to students as homework.

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)

No

Syllabus

Decision Support Systems in business and organizations

Data warehousing and OLAP

- Introduction to Data Warehouses

- Multidimensional Analysis and Star Schemas

- Data Warehouse Project

- Data Extraction, Transformation and Loading

- Optimization and Administration of Data Warehouses

- Multidimensional Databases and OLAP

- Advanced topics and new paradigms for data processing problems

Data Mining

- Introduction

- Data Mining project

- Data Selection and business analysis

- Data Pre-processing

- Selection and Application of Data Mining Algorithms

- Evaluation of Generated Data Models

- Application of Data Models.

Head Lecturer(s)

Bruno Miguel Brás Cabral

Assessment Methods

Assessment
Mini Tests: 15.0%
Exam: 35.0%
Project: 50.0%

Bibliography

―The Data WarehouseLifecycleToolkit‖, Ralph Kimbal et.al, J. Wiley& Sons, Inc, 2nd Edition, 2008.

―Data Mining―, Witten Frank. Morgan Kaufman, 2nd Edition, 2005.