Data Analysis and Knowledge Discovery

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Credits
6
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
This exciting course broaches the hot topic of Data Analysis and Knowledge Discovery (DAKD) from the viewpoint of Data Mining.
Most areas in science, engineering and business are becoming increasingly data dependent. Clear examples of this are, to name a few, bioinformatics, medicine, or electronic commerce.
Data analysis techniques are needed to deal with these data and generate usable knowledge out of them. Amongst them, DAKD techniques are one of the most promising approaches. This theme is at the core of the contents of this course.

Teachers

Person in charge

  • Alfredo Vellido Alcacena ( )

Others

  • Luis Antonio Belanche Muñoz ( )
  • Mario Martín Muñoz ( )

Weekly hours

Theory
3
Problems
0
Laboratory
0
Guided learning
0.2
Autonomous learning
5

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

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 knowledge 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.
  • CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

Generic Technical Competences

Generic

  • CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

Technical Competences

Especifics

  • CE2 - Apply the fundamentals of data management and processing to a data science problem
  • CE5 - Model, design, and implement complex data systems, including data visualization
  • CE8 - Extract information from structured and unstructured data by considering their multivariate nature.
  • CE10 - Identify machine learning and statistical modeling methods to use and apply them rigorously in order to solve a specific data science problem
  • CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats

Objectives

  1. Presenting DM as a process that should involve a methodology applied at its best.
    Related competences: CT4, CT5, CG2, CE2, CE8, CE10, CB10,
    Subcompetences:
    • Técnicas de búsqueda y tratamiento de la información en entornos heterogéneos
    • Limpieza de datos
    • Derivación de datos
  2. Introducing the students to the new concept of DM for processes, called Process Mining.
    Related competences: CT4, CT5, CG2, CE2, CE5, CE8, CE10, CE12, CB6,
    Subcompetences:
    • Algoritmos de análisis de flujos continuos de datos
  3. Delving into some detail in one of the stages of DM: data exploration.
    Related competences: CT4, CT5, CG2, CE2, CE5, CE8, CE10, CB10,
    Subcompetences:
    • Exploración y visualización de datos en minería de datos
  4. Dealing in detail with the problem of data visualization for exploration as a key issue in DM.
    Related competences: CT4, CT5, CE5,
    Subcompetences:
    • Exploración y visualización de datos en minería de datos
  5. Introducing the students to the basics of probability theory as applied in Data Analysis and Knowledge Discovery (DAKD)
    Related competences: CT5, CE8, CE10, CB7, CB10,
    Subcompetences:
    • Estadística bayesiana
  6. Introducing the students to the probabilistic variant of DAKD in the form of Statistical Machine Learning, both for supervised and unsupervised learning models.
    Related competences: CT5, CE8, CE10, CB7, CB10,
    Subcompetences:
    • Estadística bayesiana
    • Modelización a partir de factores latentes
  7. Dealing in detail with different unsupervised models for data visualization, including case studies.
    Related competences: CT5, CG2, CE2, CE5, CE8, CE10, CE12, CB6, CB10,
    Subcompetences:
    • Algoritmos avanzados para minería de datos
    • Exploración y visualización de datos en minería de datos
    • Diseño e implementación de sistemas de visualización
  8. Approaching the multi-faceted concept of data mining (DM) from different perspectives.
    Related competences: CT4, CT5, CE12, CE13, CB6, CB7,
    Subcompetences:
    • Técnicas de búsqueda y tratamiento de la información en entornos heterogéneos

Contents

  1. Introduction to the concept of data mining (DM).
    DM is a multi-faceted concept that requires discussion and clarification. We will do this at the beginning of the course.
  2. DM as a methodology.
    We argue that DM should not be focused on the concept of data analysis/modeling, but, instead, should be treated as a methodology with diverse inter-related stages.
  3. DM for processes: Process Mining.
    A new development in DM methodologies is that which deals with one specifically suited for processes. It is called Process Mining and will be described and discussed in this course.
  4. Data exploration in DM.
    One of the main stages of well-structures DM methodologies is Data exploration. It will be discussed as a preamble to data visualization.
  5. Data visualization for exploration.
    One of the aspects of the problem of data exploration is data visualization. It has a research 'life' of its own as it involves not only computer-based mathematical models, but also natural perception and processing.
  6. Basics of probability theory in Data Analysis and Knowledge Discovery (DAKD)
    For a long time in the last half-century, multivariate statistics and artificial intelligence (mostly in the field of machine learning) have developed in parallel without fully meeting. Statistical machine learning has bridged that field over the last two decades. We introduce it by first providing some basic principles of probability theory (Bayesian inference).
  7. Statistical Machine Learning for DAKD: supervised models.
    Once the basics of Bayesian inference are set, we will delve into the field of Statistical Machine Learning for IDA, starting with supervised learning models, with an emphasis on feed-forward artificial neural networks.
  8. Statistical Machine Learning for DAKD: unsupervised models.
    Once the basics of Bayesian inference and of Statistical Machine Learning for IDA in supervised models are set, we will continue with unsupervised models, focusing on self-organizing maps and related models.
  9. Unsupervised models for data visualization, with case studies.
    In the final item of the contents of the course, we will bring statistical machine learning and data visualization together by discussing some probabilistic unsupervised learning models for data visualization, including some case studies as an example.

Activities

Activity Evaluation act


Essay on DAKD for DM

Students will have to write a research essay on the topic of DAKD for DM, with different options: 1. State of the art on an specific DAKD-DM topic 2. Evaluation of an DAKD-DM software tool with original experiments 3. Pure research essay, with original experimental content
Objectives: 1 3 5 7 2 4 6 8
Week: 18
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
3h
Autonomous learning
0h

Introduction to Data Mining and its Methodologies

Introduction to Data Mining as a general concept and to its methodologies for practical implementation
Objectives: 1
Contents:
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
13h

Process Mining

Introduction to the novel concept of Process Mining and its application within the DM framework.
Objectives: 2
Contents:
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
5h

Data Visualization

As part of the DM stage of Data Exploration, we focus in the problem of Data Visualization.
Objectives: 3 4
Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
9h

Basics of probability theory for intelligent data analysis

Introduction to probability theory for intelligent data analysis, with a focus on Bayesian statistics
Objectives: 5
Contents:
Theory
6h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
9h

Statistical Machine Learning methods

The meeting of statistics and machine learning: Statistical Machine Learning methods, from the point of view of both supervised and supervised learning
Objectives: 5 6
Contents:
Theory
12h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
18h

SML in data visualization, with case studies

We merge the topics of SML and data visualization, illustrating its use with some real case studies
Objectives: 7 4 8
Contents:
Theory
9h
Problems
0h
Laboratory
0h
Guided learning
1h
Autonomous learning
15h

Teaching methodology

This course will build on different teaching methodology (TM) aspects, including:
TM1: Expositive seminars
TM2: Expositive-participative seminars
TM3: Orientation for individual assignments (essays)
TM4: Individual tutorization

Evaluation methodology

The course will include two evaluation tasks:
The first one will be a data science purely analytical task performed according to data mining principles.
The second one will involve writing an essay according to one of these three modalities:
1. State of the art on an specific IDA-DM topic
2. Evaluation of an IDA-DM software tool with original experiments
3. Pure research essay, with original experimental content

Bibliography

Basic:

Complementary:

Previous capacities

Students are expected to have at least some basic background in the area of artificial intelligence and, more specifically, with the areas of Machine Leaning and Computational Intelligence.
Some basic knowledge of probability theory and statistics would be beneficial.
Other than this, the course is open to students and researchers of all types of background.