The successful taking of this course will enable students to be able to: OA1> Understand the mission and objectives of a business analytics system inside an organization and characterize in a clear way its implementation process, and justifying all the required investments. OA2> Know how to develop a data preparation system for data mining, select the most adequate models and mining techniques to the problem and data in hands, and to know to interpret and evaluate mining results. OA3> Integrate in a business analytics platform specific anaçytical processing and data mining components, reflecting their functioning in the target decision support and business processes.
CP1. Business Intelligence and Business Analytics. CP2. The Business Analytics Model - Views, Components and Architectures. CP3. Business Analytics Competence Centres. CP4. Busness Process Definition and Strategic Levels for Solutions. CP5. Business Analytics Projects Evaluations. CP6. Data MIning Methods and Processes. CP7. Preparing, Combining and Exploring Data. CP8. Data Set Settings and Preparation. CP9. Basic Methods and Algorithms for Data Mining. CP10. Data Mining and Result Analysis.
The total of 64h are divided into 32h of theoretical lectures (T), and 32h of theoretical-practical classes (TP). T lectures are expositional. TP classes are used to consolidate concepts given in T classes, with exercises of dimensional modeling and the required tutoring to each group regarding the practical assignment, as well to practice with the tools required by the practical assignment. In order to succeed in this course it is mandatory that students take a written exam and a practical assignment. The final score is calculated as the average of the scores of the written exam and practical assignment. The minimum score in both components is 10/20. If students have a score in the written exam lower than 10 but higher or equal to 8, then they will be submitted to an oral evaluation. The practical assignment will be performed in groups.
Laursen, G., Thorlund, J., Business Analytics for Managers: Taking Business Intelligence Beyond Reporting (Wiley and SAS Business Series), Wiley, 1 edition, July 13, 2010. Pyle, D., Data Preparation for Data Mining, The Morgan Kaufmann Series in Data Management Systems, Apr 5, 1999. Redman, T., Data Driven: Profiting from Your Most Important Business Asset, Harvard Business School Press, Sep 15, 2008. Witten, I., Frank E., Data Mining (Pratical Machine Learning tools and Techniques), Morgan Kaufmann, 3 edition, Feb, 2011. Han, J., Kamber, M., Data Mining, Concepts and Techniques, 2nd edn. Elsevier, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2006.
The successful taking of this course will enable students to be able to: OA1. Understand the mission and objectives of a data warehousing system inside an organization and characterize in a clear way the process to implement them, justifying all the required investments to its implementation. OA2. Design a data warehousing system since its inception (conceptual modeling), until its effective implementation and exploration. OA3. Apply dimensional modeling techniques in the development of schemas for data warehouses. OA4. Design from scratch an ETL system for a specific data warehouse, as well as all its components for extracting, transforming, and loading of data. OA5. Know how to integrate in a data warehousing system analytical processing services and all the correspondent mechanisms for data navigation and exploration.
CP1. Introduction to Data Warehousing Systems. CP2. Infrastructures and Services of a Data Warehousing System. CP3. Development Lifecycle of a Data Warehousing System. CP4. Methodologies for the Development of a Data Warehousing System. CP5. Planning and Management of a Data Warehousing System Project. CP6. Decision Agents Requirements Gathering and Analysis. CP7. Conceptual, Logical and Physical Modeling of Data Warehouses. CP8. ETL Systems Design. CP9. Data Warehousing Systems Administration. CP10. Data Warehousing Systems Exploration.
The total of 64h are divided into 31h of theoretical lectures (T), and 31h of theoretical-practical classes (TP). T lectures are expositional. TP classes are used to consolidate concepts given in T classes, with exercises of dimensional modeling and the required tutoring to each group regarding the practical assignment, as well to practice with the tools for the development of data warehouses required by the practical assignment. In order to succeed in this course it is mandatory that students take a written exam and a practical assignment. The final score is calculated as the average of the scores of the written exam and practical assignment. The minimum score in both components is 10/20. If students have a score in the written exam lower than 10 but higher or equal to 8, then they will be submitted to an oral evaluation. The practical assignment will be performed in groups
Golfarelli, M., Rizzi, S., Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Osborne Media; 1st Edition, May 26, 2009. Kimball, R., Reeves, L., Ross, M., Thornthwait, W., The Data Warehouse Lifecycle Toolkit – Pratical Techiniques for Building Data Warehouse and Business Intelligence Systems, John Wiley & Sons, 2ª Edição, 2008 Kimball, R., Caserta, J., The Data Warehouse ETL Toolkit - Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, John Wiley & Sons, 2004. Kimball, R., Roos, M., The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, John Wiley & Sons, 2ª Edição, 2002. Larissa Moss, Shaku Atre. Business Intelligence Roadmap. The Complete Lifecycle for Decision-Support Applications. Addison-Wesley Information Technology Series. 2003. Inmon, W.H., Building the Data Warehouse , John Wiley & Sons,1996
The successful taking of this course will enable students to be able to: OA1> Understand the mission and objectives of a OLAP system inside an organization and characterize in a clear way its implementation process, and justifying all the required investments. OA2> Design a OLAP system since its inception (conceptual modeling) until its effective implementation and exploration. OA3> Acquire knowledge and expertise to know how to manage and apply security, recovering and privacy models for multidimensional database systems. OA4> Know and use MDX querying multidimensional databases.
CP1. On-Line Analytical Processing. CP2. Multidimensional Databases. CP3. Multidimensional data structures - Hypercubes. CP4. Processing, Storage and Exploration of Data Cubes. CP5. Multidimensional Querying Languages. CP6. Multidimensiona Data Structures Optimization. CP7. OLAP Tools and Applications.
The total of 45h are divided into 20h of theoretical lectures (T), and 25h of theoretical-practical classes (TP). T lectures are expositional. TP classes are used to consolidate concepts given in T classes, with exercises of dimensional modeling and the required tutoring to each group regarding the practical assignment, as well to practice with the tools required by the practical assignment. In order to succeed in this course it is mandatory that students take a written exam and a practical assignment. The final score is calculated as the average of the scores of the written exam and practical assignment. The minimum score in both components is 10/20. If students have a score in the written exam lower than 10 but higher or equal to 8, then they will be submitted to an oral evaluation. The practical assignment will be performed in groups.
Golfarelli, M., Rizzi, S., Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill Osborne Media; 1st Edition, May 26, 2009. Thomsen, E., OLAP Solutions, Building Multidimensional Information Systems, Jonh Wiley & Sons, Inc., 2nd Ed, 2002. Whitehorn, M., Meehan, P., Zare, R., Pasumansky, M., Whitehorn, M., Fast Track to MDX, Springer-Verlag New York, LLC, 2002. Celko, J., Joe Celko's Analytics and OLAP in SQL, Morgan Kaufmann, 1 edition, 2006.
The successful taking of this course will enable students to be able to: OA1> Understand the mission and objectives of a database system inside an organization and characterize in a clear way its implementation process, and justifying all the required investments. OA2> Design a database system since its inception (conceptual modeling) until its effective implementation and exploration. OA3> Apply requisites gathering and analysis techniques in the development of data schemas for database systems. OA4> Acquire knowledge and expertise to know how to manage and apply security, recovering and privacy models for database systems. OA5> Know and use SQL in data description, manipulation and controlo. OA6> Know the new NoSQL approaches and differentiate them from the relational approaches.
CP1. Introduction to Database Systems. CP2. The Relational Model. CP3. Database Systems Design and Implementation. CP4. SQL. CP5. Database Systems Administration. CP6. Distributed Database Systems. CP7. NoSQL Databases.
The total of 56h are divided into 24h of theoretical lectures (T), and 32h of theoretical-practical classes (TP). T lectures are expositional. TP classes are used to consolidate concepts given in T classes, with exercises of dimensional modeling and the required tutoring to each group regarding the practical assignment, as well to practice with the tools required by the practical assignment. In order to succeed in this course it is mandatory that students take a written exam and a practical assignment. The final score is calculated as the average of the scores of the written exam and practical assignment. The minimum score in both components is 10/20. If students have a score in the written exam lower than 10 but higher or equal to 8, then they will be submitted to an oral evaluation. The practical assignment will be performed in groups.
Connolly, T., Begg, C., Database Systems, A Practical Approach to Design, Implementation, and Management , Addison-Wesley, 4ª Edição, 2004. Garcia-Molina, H., Ullman, J., Widom, J., Database Systems: The Complete Book, Prentice Hall, 2ª Edição, 2008. Date C., An Introduction to Database Systems , Pearson Education; 8ª Edição, 2003. Sadalage , P., Fowler, M., NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Addison-Wesley Professional, 2012.