Decision Supporting Systems 2017/2018
- 5 ECTS
- Taught in Portuguese
- Both continuous and final Assessment
- relevant skillset
i. To apply and identify the main tests of parametric and non-parametric hypotheses for some population parameters (using SPSS statistical software)
ii. To understand the importance and potential of Decision Support Systems in the acquisition of competitive advantages and improvement of planning and decision making
iii. To develop research skills and a critical perspective within the use of Decision Support Systems
iv. To systematize the decision-support capabilities enhanced by the use of Business Intelligence Systems
v. To acknowledge the structure and functioning of the technologies and methodologies for the development of Business Intelligence solutions
vi. To evaluate and apply the data mining methods appropriate to a specific situation or problem within a business context
vii. To have an overview and assess the main BI commercially available tools
viii. To know how to use a Business Intelligence tool
In TP classes the exposition-active method is used for the topics of the programme, complemented with the analysis and discussion of papers and case studies in the context of collaborative work. The demonstration method and guided practice are also used to show the use of SPSS software and other data mining and BI tools.
Seminar classes will be reserved for companies’ presentations of BI and data analysis solutions used in the market.
Body of Work
1. Tests of parametric hypotheses: Normal populations - tests of means and variances; Non-standard populations (large samples) - tests for proportion; Analysis of variance (ANOVA tables); Levene test for the homogeneity of variances (Normal populations / Non-standard populations) - SPSS
2. Tests of non-parametric hypotheses: adjustment test (Kolmogorov-Smnnov test); Wilcoxon test; Mann-Whitney test; Krüskal-Wallis test (SPSS)
3. The concept and basic architecture of a Decision Support System (SSD). Types of SSD. The DSS-BI connection.
4. Introduction to Business Intelligence (BI)
5. Data Warehousing
6. Business Analytics and Data Visualization
6.2 Data Mining
7. Overview of Business Performance Management (BPM)
• Gujarati, D. and D. Porter (2009) Basic econometrics, Fifth edition, Boston: McGraw Hill.
• IRMA (ed.) (2016) Business Intelligence: Concepts, Methodologies, Tools and Applications (4 Volumes), Information Resources Management Association, IGI Global, USA.
• Newbold, P., Carlson, W. and Thorne B. (2013) Statistics for Business and Economics. Prentice-Hall – Pearson Education (International Edition).
• Provost, F., Fawcett, T. (2013) Data Science for Business: What you need to know about data mining and data-analytic thinking, O´Reilly Media, Inc.
• Sharda, R., Delen, D., Turban, E. (2017) Business Intelligence, Analytics and Data Science, A Managerial Perspective, Pearson.
• Turban, E., Sharda, R., Delen, D. (2013) Decision Support and Business Intelligence Systems, 9th Edition, Pearson.
Demonstration of the syllabus coherence with the curricular unit's objectives
The learning of parametric and non-parametric hypothesis tests, using when appropriate the SPSS software (topics 1 and 2 of the contents), contributes to the students' achievement of a solid knowledge in this area and to the objective of using statistical inference in decision making (objective i). Objectives ii and iii of the curricular unit are achieved through the contribution of topic 3 of the contents. Topics 4 to 7 of the contents contribute to objectives iv, v, vii and viii. Objective vi is achieved by topic 6.2 of the contents.
Demonstration of the teaching methodologies coherence with the curricular unit's objectives
In TP classes, the exposition-active method will be used to present the main concepts. The use of questions-answers in these presentations and discussion in the classroom will be used for frequent interaction with students in order to promote their critical thinking, the ability to issue insight opinions and internalize the key concepts. The analysis and discussion of papers and case studies will aim to stimulate critical discussion and develop students' ability to apply concepts to concrete and real situations.
Practical exercises solving will be used to check the students' ability to apply the knowledge obtained in classes to real situations of application of statistical inference and development of BI projects, targeting, in particular, KPIs identification and the data warehouse model definition for the system.
Guided practice and seminars will allow students to gain insights into various BI and data mining tools so as to enable them to use the adequate tools for the development of practical projects in a proficient way.
|relevant generic skill||improved?||assessed?|
|Achieving practical application of theoretical knowledge||Yes||Yes|
|Adapting to new situations||Yes||Yes|
|Analytical and synthetic skills||Yes||Yes|
|Balanced decision making||Yes||Yes|
|Commitment to effectiveness||Yes||Yes|
|Commitment to quality||Yes||Yes|
|Ethical and responsible behaviour||Yes|
|Event organization, planning and management||Yes|
|Foreign language proficiency||Yes|
|Information and learning management||Yes||Yes|
|IT and technology proficiency||Yes||Yes|
|Problem Analysis and Assessment||Yes||Yes|
|Relating to others||Yes||Yes|
|Written and verbal communications skills||Yes||Yes|