| Code |
14528
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
OT(15H)
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| Scientific area |
Informatics
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Entry requirements |
N/A
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Learning outcomes |
O1) Understanding the general concepts related to decision support systems.
O2) Identification of different phases associated with the discovery of information from data: pre / post-processing, pattern discovery, clustering and classification.
O3) Training critical and analytic thinking in the evaluation of information discovered, and ability to compare different methodologies.
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Syllabus |
1. Information discovery process
2. Pre-processing
3. Post-processing
4. Patterns discovery
5. Clustering
6. Classification
7. Advanced Topics: time series, sequence analysis, recommender systems, social network analysis, and data mining
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Main Bibliography |
- Data Mining and Analysis: Fundamental Concepts and Algorithms: Mohammed J. Zaki, Wagner Meira, Jr. 2014 Cambridge University Press
- Ralph Kimball e Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Wiley, 2ª edição, 2002, ISBN 0471200247
-Ian Witten, Eibe Frank, e Mark Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kauffman, 3ª edição, 2011, ISBN 0123748569
- Wes McKinney Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O’Reilly, 2ª edição, 2018, ISBN 1491957662
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Teaching Methodologies and Assessment Criteria |
Students must propose a research topic related to their area of interest and correlated with decision support systems. The development of this topic must to include published scientific works, and / or case studies. Fortnightly each student presents the evolution of his/her study.
The final evaluation will be obtained on both quality of the document produced (75%) and presentations (25%).
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Language |
Portuguese. Tutorial support is available in English.
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