Publication in the Diário da República: Despacho n.º 11262/2016 - 19/09/2016
8 ECTS; 1º Ano, Anual, 36,0 PL + 8,0 TP + 4,0 S + 32,0 OT + 10,0 O
- Sandra Maria Gonçalves Vilas Boas Jardim
A. Be able to extract the data to be integrated into the information/knowledge pyramid,
B. Be able to apply data pre-processing techniques
C. Know the main analytic models and the algorithms that implement them
D. Design and implement algorithms for the creation of analytical models
1. Data acquisition and pre-processing (data quality, aggregation, sampling, dimensionality reduction, feature subset selection, feature creation, discretization and binarization, attribute transformation)
2. Analytics techniques: forecasting techniques, descriptive analytics (clustering technique), predictive analytics (machine learning, regression and text mining), decision optimization techniques
3. Algorithms: algorithms for implementing the techniques discussed in point 2.)
4. Visualization and interpretation of results from analytics systems (dashboards)
Written test (30%), Weekly individual tasks (30%), Final practical assignment (40%)
Exam-based assessment (First attempt and Resit)
Written test (50%), Practical Assignment (50%)
In all evaluation periods, the minimum grade for each component is 7 values.
In each evaluation period, approval to the course implies a final classification equal to or greater than 10 values.
- Turban, E. e Sharda, R. e Dursun, D. e King, D. (2011). Business Intelligence: a managerial approach. New Jersey: Prentice Hall
- Howson, C. (2008). Successful business intelligence : secrets to making BI a killer app.. New York: Mc Graw-Hill
- Eckerson, W. (2006). Performance Dashboards: Measuring, Monitoring, and Managing Your Business. New Jersey: John Wiley & Sons
Lectures and practical sessions.
Software used in class
Keras, Tensorflow, productivity tools Elearning platform.