Francesco ORCIUOLI | DECISION MODELS
Francesco ORCIUOLI DECISION MODELS
cod. 0222600031
DECISION MODELS
| 0222600031 | |
| DIPARTIMENTO DI SCIENZE AZIENDALI - MANAGEMENT & INNOVATION SYSTEMS | |
| EQF7 | |
| BUSINESS INNOVATION AND INFORMATICS - BUSINESS, INNOVAZIONE ED INFORMATICA | |
| 2019/2020 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 10 | 60 | LESSONS |
| Objectives | |
|---|---|
| THIS COURSE REFERS TO THEORIES, MODELS, METHODS AND TOOLS FOR DECISION MAKING AND TO DECISION SUPPORT SYSTEMS (DSS). KNOWLEDGE AND UNDERSTANDING: THE COURSE AIMS AT PROVIDING STUDENTS WITH AN ENDOWMENT OF KNOWLEDGE RELATED TO DECISION THEORY AND DECISION SUPPORT SYSTEMS. STUDENTS WILL STUDY COMPUTATIONAL MODELS FOR DECISION MAKING: MULTI-CRITERIA DECISION MAKING (E.G., ANALYTIC HIERARCHY PROCESS), GROUP DECISION MAKING (E.G., FUZZY CONSENSUS MODEL) AND DATA-DRIVEN DECISION MAKING (E.G., ROUGH SET THEORY E THREE-WAY DECISIONS). MOREOVER, A PART OF THE COURSE WILL FOCUS ON STUDYING AND ANALYSING THE KNOWLEDGE-DRIVEN DSS WITH PARTICULAR REFERENCE TO KNOWLEDGE GRAPH AND DATA SEMANTICS. LASTLY, STUDENTS WILL ACQUIRE KNOWLEDGE FOR THE COMPREHENSION OF SPECIFIC PYTHON LIBRARIES FOR DATA MANIPULATION (E.G., NUMPY, PANDAS, MATPLOTLIB, SCIPY), TOOLS SUCH AS JUPYTER AND SPYDER AND ADDITIONAL TOOLS FOR SEMANTIC MODELLING AND BUSINESS INTELLIGENCE. THE BIG DATA CONTEXT WILL BE ALSO CONSIDERED. APPLYING KNOWLEDGE AND UNDERSTANDING: AT THE END OF THE COURSE, STUDENTS WILL DEVELOP PRACTICAL SKILLS FOR APPLYING COMPUTATIONAL MODELS FOR DECISION MAKING. STUDENTS WILL ALSO DEVELOP SKILLS ON THE USE AND DEVELOPMENT (IN PYTHON) OF TOOLS AND TECHNIQUES TO SUPPORT DECISION-MAKING WITH PARTICULAR REFERENCE TO DATA-DRIVEN APPROACHES AND THE SKILLS NEEDED TO DESIGN DSS ALSO BY USING SEMANTIC TECHNIQUES. MAKING JUDGEMENTS: THE COURSE AIMS TO ENCOURAGE THE DEVELOPMENT OF DECISION-MAKING SKILLS (MOSTLY DATA-DRIVEN) TO BE APPLIED TO SEVERAL AND HETEROGENEOUS DOMAINS (E.G., SECURITY, INTELLIGENCE, LOGISTICS, PROJECT MANAGEMENT). COMMUNICATION: THE STUDENT WILL BE STIMULATED, THROUGH PROJECT WORKS AND PRESENTATIONS AND DISCUSSIONS OF THEM, TO DEVELOP INTERPERSONAL AND COMMUNICATION SKILLS NEEDED TO WORK WITH THE CUSTOMER FOR DEFINING SOLUTIONS BASED ON DECISION SUPPORT SYSTEMS. LEARNING SKILLS: AT THE END OF THE COURSE, STUDENTS WILL HAVE DEVELOPED THOSE LEARNING SKILLS THAT WILL ENABLE THEM TO CONTINUE TO STUDY AUTONOMOUSLY NEW METHODOLOGIES, TECHNIQUES AND TOOLS RELATED TO DECISION MAKING AND DECISION SUPPORT SYSTEMS. |
| Prerequisites | |
|---|---|
| COMPUTER PROGRAMMING BASICS |
| Contents | |
|---|---|
| WITH RESPECT TO THE CONTENT, THE COURSE IS DIVIDED INTO THREE PARTS: I - DECISION-MAKING I.A ELEMENTS OF DECISION THEORY (6 HOURS) I.B INTRODUCTION TO DECISION SUPPORT SYSTEMS (12 HOURS) II – COMPUTATIONAL MODELS FOR DECISION MAKING II.A DECISION-MAKING MODELS (6 HOURS) II.B DATA-DRIVEN DSS (6 HOURS) II.C KNOWLEDGE-DRIVEN DSS (9 HOURS) II.D BIG DATA AND DECISION MAKING (3 HOURS) III – DEVELOPMENT TOOLS FOR DECISION MAKING (18 HOURS) |
| Teaching Methods | |
|---|---|
| THE TEACHING ACTIVITIES (60 HOURS AND 10 ECTS) WILL BE DIVIDED INTO LECTURES AIDED BY MULTIMEDIA MATERIAL (40 HOURS), DISCUSSIONS RELATED TO THE DEFINITION OF PROJECT WORK PROPOSALS AND REALIZATION APPROACHES (5 HOURS) AND LABORATORY LESSONS (15 HOURS). ACTIVITY-BASED LEARNING WILL BE THE MAIN EMPLOYED METHOD. |
| Verification of learning | |
|---|---|
| THE EVALUATION WILL BE CARRIED OUT BY MEANS OF A 40 MINUTES DISCUSSION (FOR EACH STUDENT) ABOUT BOTH ALL THE TOPICS OF THE COURSE AND THE ASSIGNED PROJECT WORK (PRESENTATION AND Q/A SESSION). STUDENTS WILL BE EVALUATED BY USING A SCALE OF 30. |
| Texts | |
|---|---|
| MAIN TEXTBOOKS: - M. PETERSON – AN INTRODUCTION TO DECISION THEORY – 2ND EDITION, CAMBRIDGE UNIVERSITY PRESS, 2017 - G-H TZENG, J-J HUANG - MULTIPLE ATTRIBUTE DECISION MAKING: METHODS AND APPLICATIONS - 1ST EDITION, CRC PRESS, 2011 - R. SHARDA, D. DELEN, E. TURBAN – BUSINESS INTELLIGENCE AND ANALYTICS: SYSTEMS FOR DECISION SUPPORT – 10TH EDITION, PEARSON, 2013 - V.L. SAUTER – DECISION SUPPORT SYSTEMS FOR BUSINESS INTELLIGENCE – 2ND EDITION, WILEY, 2011 - G. ANTONIOU, P. GROTH, F. VAN HARMELEN – A SEMANTIC WEB PRIMER – 3RD EDITION, MIT PRESS, 2012 - M. LUTZ – LEARNING PYTHON – 5TH EDITION, O’REILLY, 2013 - C.R. SEVERANCE - PYTHON FOR EVERYBODY: EXPLORING DATA USING PYTHON 3 (FREE WEB RESOURCE) |
| More Information | |
|---|---|
| ADDITIONAL LEARNING RESOURCES WILL BE PROVIDED BY THE TEACHER. |
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