Data Analysis and Information Exploitation

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Credits
6
Types
Specialization compulsory (Information Systems)
Requirements
  • Prerequisite: PE
Department
EIO
L'objectiu de ADEI es la de dotar als estudiants dels coneixements i habilitats per poder fer front a les necessitats d'informació de les organitzacions, això és, saber aprofitar les dades emmagatzemades pels SI de les organitzacions per tal d'integrar sistemes automà tics d'ajuda a la presa de decisions. La idea subjacent és que les dades són un tresor per a les organitzacions i que mitjançant la seva explotació es fa palesa la informació que contenen.
L'assignatura es desenvolupa a partir de la resolució dels problemes d'un cas pràctic real. Es divideix en quatre blocs: Qualitat de les dades i descripció sumaria. Eines de predicció en les organitzacions, anàlisi multivaraint de les dades i establiments de tipologies.
El curs inclou la presentació de resultats obtinguts en el cas d'estudi.

Teachers

Person in charge

  • Lidia Montero Mercadé ( )

Others

  • Mari Paz Linares Herreros ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0.4
Autonomous learning
5.6

Competences

Technical Competences of each Specialization

Information systems specialization

  • CSI2 - To integrate solutions of Information and Communication Technologies, and business processes to satisfy the information needs of the organizations, allowing them to achieve their objectives effectively.
    • CSI2.1 - To demonstrate comprehension and apply the management principles and techniques about quality and technological innovation in the organizations.
    • CSI2.3 - To demonstrate knowledge and application capacity of extraction and knowledge management systems .

Transversal Competences

Reasoning

  • G9 [Avaluable] - Capacity of critical, logical and mathematical reasoning. Capacity to solve problems in her study area. Abstraction capacity: capacity to create and use models that reflect real situations. Capacity to design and perform simple experiments and analyse and interpret its results. Analysis, synthesis and evaluation capacity.
    • G9.3 - Critical capacity, evaluation capacity.

Third language

  • G3 [Avaluable] - To know the English language in a correct oral and written level, and accordingly to the needs of the graduates in Informatics Engineering. Capacity to work in a multidisciplinary group and in a multi-language environment and to communicate, orally and in a written way, knowledge, procedures, results and ideas related to the technical informatics engineer profession.
    • G3.2 - To study using resources written in English. To write a report or a technical document in English. To participate in a technical meeting in English.

Objectives

  1. Learn how to identify the three levels of decision making in a company
    Related competences: CSI2.1,
  2. Learn how to make a report on data quality
    Related competences: G9.3, CSI2.1,
  3. Control Quality
    Related competences: G9.3, CSI2.3, CSI2.1,
  4. Continuous process control
    Related competences: G9.3, CSI2.3, CSI2.1,
  5. Control of discrete indicators
    Related competences: G9.3, CSI2.3, CSI2.1,
  6. Determining the drivers of continuous response.
    Related competences: G9.3, CSI2.3, CSI2.1,
  7. Diagnosis of a statistical model
    Related competences: G9.3, CSI2.3, CSI2.1,
  8. Modelling of discrete choices
    Related competences: G9.3, CSI2.3, CSI2.1,
  9. Modelling the propensity
    Related competences: G9.3, CSI2.3, CSI2.1,
  10. Analysis of databases. Determination of the significant characteristics of groups of individuals.
    Related competences: G9.3, CSI2.3, CSI2.1,
  11. Concept and measurement of intangibles in a company
    Related competences: G9.3, CSI2.3, CSI2.1,
  12. Multivariate information visualization
    Related competences: G9.3, CSI2.3, CSI2.1,
  13. Clustering
    Related competences: G9.3, CSI2.3, CSI2.1,
  14. Modelling intangibles. Models for consumer satisfaction
    Related competences: G9.3, CSI2.3, CSI2.1,
  15. Statistical tools for support decision making
    Related competences: G9.3, CSI2.3, G3.2, CSI2.1,

Contents

  1. Bloc1: Levels of corporate decision
  2. Block 2: Summary description and data quality
  3. Block 3: Statistical Modeling
  4. Block 4: Multivariate Data Analysis and intangible measurement
  5. Block 5: Clustering and profiling

Activities

Activity Evaluation act


Quiz blocks 2 and 3


Objectives: 1 2 3 6 7 8
Week: 8
Type: lab exam
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
8h

Quiz Blocks 4 and 5


Objectives: 1 10 11 12 13 14
Week: 14
Type: lab exam
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
8h

Handing in of practical work 1


Objectives: 1 2 3 6 7 8
Week: 8
Type: lab exam
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
8h

Handing in of practical work 2


Objectives: 10 11 12 13 14
Week: 14
Type: lab exam
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
8h

Presentation of the Case of Study


Objectives: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Week: 15 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
8h

Block 1. Levels of corporate decision

It presented the three levels of decision making in companies. What are the main business processes and how is stored the generated data.
Objectives: 1
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
1h

Block 2. Description and quality control data

Problems in data quality: This is seen in the Case Study or problems that may present data: inconsistency, redundancy. Missing data. Outliers. How do I report data quality. What is the standardization of data.
Objectives: 2
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
1h
Autonomous learning
3h

Block 2. Treatment of random variability

Principles of continuous improvement in quality. Definition of indicators and statistical variability. Methodology Operational Control: historical variability
Objectives: 2 3 15
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Block 2. Data visualisation

Type of Data Collection and applicability to operational control. Indicators common in continuous process control
Objectives: 2 3 15
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Block 3. Statistical Modeling

Perspectiva del modelatge per tècniques de regressió lineal : components estadístiques implicades. Rols: variables de resposta/explicatives
Objectives: 6 15
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
1h
Autonomous learning
3h

Block 3. Training the model

Estimació per mínims quadrats
Objectives: 6
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Blok 3. Validation of statistical modeling

Elements involved in the validation of regression modeling. Values ​​influential and / or outliers
Objectives: 7
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Bloc 3. Statistical Modeling of binary variables


Objectives: 7 8
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Block 4. Multivariate Data Analysis

Problemes multivariants en l'empresa
Objectives: 11 12
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
1h
Autonomous learning
3h

Block 4. Principal Component Analysis


Objectives: 11 12
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Blok 4. Measurement of intangibles


Objectives: 11 12
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Blok 4. Practice of Principal Component Analysis

Practice Principal Component Analysis, interpretation of the representations obtained. Positioning of the supplementary information.
Objectives: 11 12 15
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Blok 5. Clustering


Objectives: 13
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Block 4. Practice of Clustering

Presentation of the k-means and hierarchical methods.
Objectives: 13 15
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
1h
Autonomous learning
3h

Block 5. profiling


Objectives: 10 15
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
1h
Autonomous learning
4h

Teaching methodology

Learning the course consists of three distinct phases:
1. Acquisition of specific knowledge through the study of literature and material provided by teachers. 2. The acquisition of skills in specific techniques of data analysis and exploitation of information and
3. Integration of knowledge, skills and competencies (specific and generic) by solving a real Case Study.
In theory classes serve to expose the foundations of methodologies and techniques of the subject.
The laboratory classes are used to learn the use of specific techniques for solving problems, using appropriate informatics tools, in this sense, students first must repeat the problem solved previously by the teachers and then solve a similar one.
While the case study, are setttled in groups in selflearning hours, and serves to put into practice the knowledge, skills and competences in solving a real case of ADEI.

Evaluation methodology

The evaluation of the course integrates the three phases of learning process: knowledge, skills and competencies.
The knowledge is assessed by two quizes, in the middle and last week of the course. If you fail this exam, students may have a final resit. (score T).
The skills assessed from the delivery from 2 to 5 practices relating to the course case study. Each of the blocks 2 and 3 involve a practice that students will perform either individually or in groups of 2, the same for blocks 4 and 5. The average of the scores comes out the L score.
The case study as a whole exercise will be evaluated based on the oral presentation (score P).
In the presentation of case study that generic skills will be assessed. In any case, the presentation of the case study is compulsory.

The final grade will obtained weighing the three scores: Final Mark = 0.4P + 0.3L + 0.3T.

Generic skills will be assessed on the scale: Fail, Pass, Good and Very good (D,C,B and A).
To assess the competence on English, it will be required to have written in English the report on the Case Study, moreover at the beginning of the presentation, the student must do an outline of the work in English as well. Regarding the reasoning competence, it will be assessed from the answers given to the presentation of the Case Study.

Bibliography

Basic:

Previous capacities

Students must have completed a course in probability and statistics and a course on business and economic environment

Addendum

Contents

NO HI HA CANVIS RESPECTE LA INFORMACIÓ PUBLICADA A LA GUIA DOCENT

Teaching methodology

NO HI HA CANVIS RESPECTE LA INFORMACIÓ PUBLICADA A LA GUIA DOCENT

Evaluation methodology

NO HI HA CANVIS RESPECTE LA INFORMACIÓ PUBLICADA A LA GUIA DOCENT. Si les condicions COVID impliquen la suspensió de la presencialitat en les proves avaluatòries de l'assignatura aleshores es passarà al format Meet/Hangouts com activitat síncrona en la mateixa franja horària, si procedeix. Es farà ús d'ATENEA per accedir i penjar en format .pdf els exàmens de l'assignatura.

Contingency plan

Si les condicions COVID impliquen la suspensió de les sessions presencials de laboratori de l'assignatura aleshores es passarà al format Meet/Hangouts com activitat síncrona en la mateixa franja horària. Es farà ús d'ATENEA per accedir als continguts i pel lliurament de pràctiques tal com es fa habitualment en condicions no COVID.