Data Analysis and Information Exploitation

You are here

Home » Studies » Bachelor's degrees » Bachelor Degree in Informatics Engineering » Curriculum » Syllabus » Data Analysis and Information Exploitation
Credits
6
Types
Specialization compulsory (Information Systems)
Requirements
  • Prerequisite: PE
Department
EIO
The aim of ADEI is to provide students with the knowledge and skills to be able to deal with the information needs of organizations, that is, to know how to take advantage of the data stored by the IS of the organizations in order to integrate automatic systems help in decision-making. The underlying idea is that data is a treasure for organizations and that through their exploitation the information they contain is revealed. The subject is developed from the resolution of the problems of a real practical case. It is divided into four blocks: Data quality and summary description. Prediction tools in organizations, multivariate analysis of data and establishment of typologies. The course includes the presentation of results obtained in the case study.

Teachers

Person in charge

  • Lidia Montero Mercadé ( )
  • Xavier Angerri Torredeflot ( )

Others

  • Bhumika Ashvinbhai Patel ( )
  • Josep Franquet Fàbregas ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
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. Control Quality
    Related competences: G9.3, CSI2.3, CSI2.1,
  3. Control of discrete indicators
    Related competences: G9.3, CSI2.3, CSI2.1,
  4. Determining the drivers of continuous response.
    Related competences: G9.3, CSI2.3, CSI2.1,
  5. Diagnosis of a statistical model
    Related competences: G9.3, CSI2.3, CSI2.1,
  6. Modelling of discrete choices
    Related competences: G9.3, CSI2.3, CSI2.1,
  7. Modelling the propensity
    Related competences: G9.3, CSI2.3, CSI2.1,
  8. Analysis of databases. Determination of the significant characteristics of groups of individuals.
    Related competences: G9.3, CSI2.3, CSI2.1,
  9. Concept and measurement of intangibles in a company
    Related competences: G9.3, CSI2.3, CSI2.1,
  10. Multivariate information visualization
    Related competences: G9.3, CSI2.3, CSI2.1,
  11. Clustering
    Related competences: G9.3, CSI2.3, CSI2.1,
  12. Modelling intangibles. Models for consumer satisfaction
    Related competences: G9.3, CSI2.3, CSI2.1,
  13. Statistical tools for support decision making
    Related competences: G9.3, CSI2.3, G3.2, CSI2.1,
  14. Continuous process control
    Related competences: G9.3, CSI2.3, CSI2.1,
  15. Learn how to make a report on data quality
    Related competences: G9.3, 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 15 2 4 5 6
Week: 8
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
12h

Presentation of the Case of Study


Objectives: 1 15 2 14 3 4 5 6 7 8 9 10 11 12 13
Week: 15
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
12h

Quiz Blocks 4 and 5


Objectives: 1 8 9 10 11 12
Week: 14
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
12h

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

Block 2. Data visualisation

Type of Data Collection and applicability to operational control. Indicators common in continuous process control
Objectives: 15 2 13
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: 4 13
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
8h

Block 3. Training the model

Estimació per mínims quadrats
Objectives: 4
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: 5
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Bloc 3. Statistical Modeling of binary variables


Objectives: 5 6
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
8h

Block 4. Multivariate Data Analysis

Problemes multivariants en l'empresa
Objectives: 9 10
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
3h

Block 4. Principal Component Analysis


Objectives: 9 10
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
5h

Blok 4. Measurement of intangibles


Objectives: 9 10
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: 9 10 13
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
3h

Blok 5. Clustering


Objectives: 11
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: 11 13
Contents:
Theory
2h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
3h

Block 5. profiling


Objectives: 8 13
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
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:

Web links

Previous capacities

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