Incorporating The Know-How Into The Decision Process

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Credits
1.5
Types
Elective
Requirements
This subject has not requirements
Department
EIO
Web
https://www-eio.upc.edu/~karina/datamining/IKPD/
Mail
The course provides tools to take as much advantage as possible of the data registered in information systems of any organization, with regards to decision making processes. The course works over central elements in decision-making-oriented information explotation; elements with high impact, too often dismissed. Mainly, the impact of a priori knowledge on the data-driven analysis of the organization will be addressed, as well as the dangers associated with the impact of the implicit knowledge managed by the experts in decisional processes. The course is organized under a real case analysis approach

Teachers

Person in charge

  • Karina Gibert Oliveras ( )

Others

  • Xavier Angerri Torredeflot ( )

Competences

Technical Competences of each Specialization

Direcció i gestió

  • CDG1 - Capability to integrate technologies, applications, services and systems of Informatics Engineering, in general and in broader and multicisciplinary contexts.

Especifics

  • CTE9 - Capability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.

Generic Technical Competences

Generic

  • CG8 - Capability to apply the acquired knowledge and to solve problems in new or unfamiliar environments inside broad and multidisciplinary contexts, being able to integrate this knowledge.

Transversal Competences

Teamwork

  • CTR3 - Capacity of being able to work as a team member, either as a regular member or performing directive activities, in order to help the development of projects in a pragmatic manner and with sense of responsibility; capability to take into account the available resources.

Information literacy

  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.

Reasoning

  • CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.

Objectives

  1. Identify decisional variables in a real problem
    Related competences: CTR4,
  2. Identify the relevant a priori knowledge for decision-making
    Related competences: CTR4, CTR6,
  3. Elicit the relevant implicit knowledge for the target problem
    Related competences: CTR3, CTR4, CB7, CB8,
  4. Design information extraction procedures from data, which take into account the elicited a priori knowledge
    Related competences: CDG1, CTR3, CTR4, CTR6, CB6, CG8,
  5. Apply the information extraction processes to a real case and present the final results
    Related competences: CTE9, CB6, CB8, CG8,

Contents

  1. Introduction
  2. A priori knowledge
    Concept
    Representation formalisms
    How to introduce it in data analysis?
    Case: Analysis of mental health systems from the WHO
    Case: Identification of typical situations in wastewater treatment plants
  3. Implicit knowledge
    Concept
    Impact of implicit knowledge in Knowledge Discovery
    Tools to elicit implicit knowledge
    Case: Efects of elecktroconvulsive therapy over reaction times in schyzophrenia
    Case: Dependency Law in Catalunya

Activities

Activity Evaluation act


Reading, comprehension, analysis of a text about decision-making. Fill-in a brief form


Objectives: 1 2
Contents:
Theory
0h
Problems
0h
Laboratory
0.5h
Guided learning
0h
Autonomous learning
3h

Development of a case


Objectives: 1 2 3 4 5
Contents:
Theory
0h
Problems
0h
Laboratory
5h
Guided learning
0h
Autonomous learning
15h

Final presentation of developed cases and common discussion of all the works developped


Objectives: 5
Theory
0h
Problems
0h
Laboratory
1.5h
Guided learning
0h
Autonomous learning
3h

Presentation, analysis and discussion of illustrative cases


Objectives: 1 2 3 4
Contents:
Theory
1.5h
Problems
0h
Laboratory
3.5h
Guided learning
0h
Autonomous learning
3h

Introduction



Theory
0.5h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

Very specific course, contentrated on time. Totally given in the laboratory room. Case-based learning approach, with small conceptual introductions. Small practical works to consolidate the obtained knowledge. During the first week the conceptual bases of the course will be presented and the activities to be developed by every student will be distributed.
There is an exercise based on the reading of a text about decision-making, where the student must identify the use of the a priori knowledge made, the assumptions, and must analyze drawbacks and advantages of the decision process described on the paper.
In a second exercice, in groups, a new application will be developed. The case can be proposed by both the students or the conductor of the course.
Besides the acquisition of specific technical skills, some transversal competences will be also developed during the course, like knowledge integration, capacities of analysis, sinthesis, oral, visual and written comunication. The works will be presented to the whole group and a global discussion will perform as an oral evaluation.

Evaluation methodology

The final qualification is composed by 4 components:
*) Q: questionaire about the reading of a text about decision-making
*) P: practical work over a real case (includes public presentation)
*) C: participation in classes

The final score is obtained as:

N= 0.3 Q + 0.6 P+ 0.10 C

Bibliography

Basic: