Guías-E

1. General information

Course:

DATA MINING

Code:

42348

Type:

ELECTIVE

ECTS credits:

6

Degree:

407 - DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING

Academic year:

2022-23

Center:

108 - SCHOOL OF COMPUTER SCIENCE OF C. REAL

Group(s):

20

Year:

4

Duration:

First quarter

Main language:

Spanish

Second language:

Use of additional languages:

English Friendly:

Y

Web site:

Bilingual:

N

Lecturer: FERNANDO GUALO CEJUDO - Group(s): 20

Building/Office

Department

Phone number

Email

Office hours

TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN

Fernando.Gualo@uclm.es

Lecturer: MACARIO POLO USAOLA - Group(s): 20

Building/Office

Department

Phone number

Email

Office hours

Fermín Caballero/3.21

TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN

3730

macario.polo@uclm.es

2. Pre-Requisites

We recommend students are familiar with Computer Science concepts, like those in the previous courses of the Degree Program. In addition, this subject is based on the skills and knowledge acquired in the following ones:

- Logic

- Statistics

- Algorithm Design

- Intelligent systems

- Knowledge Based Systems

3. Justification in the curriculum, relation to other subjects and to the profession

Data mining and machine learning are linked to the field of statistics and computer algorithms. They are based on techniques for the extraction of knowledge from data sets. In recent years, these disciplines are gaining importance due to the increase in data production -propitiated by phenomena such as the rise of the Internet or social networks- or the development of new techniques for obtaining genetic information. From a professional point of view, there is a rising demand for data scientists in fields as diverse as marketing, market analysis, security, or biology.

4. Degree competences achieved in this course

Course competences
Code	Description
CM05	Ability to acquire, formalise, and represent human knowledge in a computable form for the solution of problems throughout a digital system in any application context, especially the one linked to computational aspects, perception, and behaviour in intelligent frames.
CM07	Ability to know and develop computational learning techniques, and design and implement applications and systems which could use them, including the ones for the automatic extraction of information and knowledge from great batches of information.
INS01	Analysis, synthesis, and assessment skills.
INS04	Problem solving skills by the application of engineering techniques.
INS05	Argumentative skills to logically justify and explain decisions and opinions.
PER02	Ability to work in multidisciplinary teams.
PER04	Interpersonal relationship skills.
PER05	Acknowledgement of human diversity, equal rights, and cultural variety.
SIS01	Critical thinking.
SIS03	Autonomous learning.
SIS09	Care for quality.
UCLM03	Accurate speaking and writing skills.

5. Objectives or Learning Outcomes

Course learning outcomes
Description
Description and application of different phases of the discovery process of knowledge extraction from large volumes of data.
Development and implementation of a small to medium-sized information retrieval system.
Knowledge and development of computational learning techniques, both supervised and unsupervised, and design and implement applications and systems that use them.
Additional outcomes
Not established.

6. Units / Contents

Unit 1: Introduction
- Unit 1.1: Artificial Intelligence, KDD and Data Mining
- Unit 1.2: Intelligent Data Analysis in Big Data Environments
Unit 2: Exploratory Data Analysis
Unit 3: Data Mining Tasks
- Unit 3.1: Clustering
- Unit 3.2: Dimensionality Reduction
- Unit 3.3: Association Rules Extraction
- Unit 3.4: Anomaly Detection
- Unit 3.5: Classification
- Unit 3.6: Regression
Unit 4: Data Mining Applications
Unit 5: Study CAses

ADDITIONAL COMMENTS, REMARKS

LABORATORY

a complete KDD process will be developed throughout the course. The student will propose the domain linked to their interests of work and/or research or some topic proposed by the professors.

1. Problem Selection

2. Data Selection.

3. Pre-processing.

4. Transformation.

5. Data Mining.

6. Use of patterns discovered in an application.

7. Activities, Units/Modules and Methodology

Training Activity	Methodology	Related Competences	ECTS	Hours	As	Com	Description
Class Attendance (theory) [ON-SITE]	Lectures	CM05 CM07	0.6	15	N	N	Teaching of the subject matter by lecturer (MAG)
Individual tutoring sessions [ON-SITE]		CM05 CM07 INS05 SIS01 SIS09 UCLM03	0.18	4.5	N	N	Individual or small group tutoring in lecturer¿s office, classroom or laboratory (TUT)
Study and Exam Preparation [OFF-SITE]	Self-study	CM05 CM07 INS01 SIS01 SIS03 SIS09	1.8	45	N	N	Self-study (EST)
Other off-site activity [OFF-SITE]	Practical or hands-on activities	CM05 CM07 INS01 INS04 PER02 PER04 PER05 SIS03 SIS09 UCLM03	0.9	22.5	N	N	Lab practical preparation (PLAB)
Problem solving and/or case studies [ON-SITE]	Problem solving and exercises	CM05 CM07 INS01 INS04 PER02 PER04 PER05 SIS01 SIS09	0.6	15	Y	N	Worked example problems and cases resolution by the lecturer and the students (PRO)
Writing of reports or projects [OFF-SITE]	Self-study	CM05 CM07 INS01 INS04 INS05 PER02 PER04 PER05 SIS01 SIS03 SIS09 UCLM03	0.9	22.5	Y	N	Preparation of essays on topics proposed by lecturer (RES)
Laboratory practice or sessions [ON-SITE]	Practical or hands-on activities	CM05 CM07 INS04 PER02 PER04 PER05 SIS03 SIS09	0.72	18	Y	Y	Realization of practicals in laboratory /computing room (LAB)
Progress test [ON-SITE]	Assessment tests	CM05 CM07 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03	0.1	2.5	Y	N	Progress test 1 of the first third of the syllabus of the subject (EVA)
Progress test [ON-SITE]	Assessment tests	CM05 CM07 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03	0.1	2.5	Y	N	Progress test 2 of the two first thirds of the syllabus of the subject (EVA)
Progress test [ON-SITE]	Assessment tests	CM05 CM07 INS01 INS04 INS05 PER02 SIS01 SIS09 UCLM03	0.1	2.5	Y	N	Progress test 3 of the complete syllabus of the subject (EVA)
Total:			6	150
Total credits of in-class work: 2.4			Total class time hours: 60
Total credits of out of class work: 3.6			Total hours of out of class work: 90

As: Assessable training activity
Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).

8. Evaluation criteria and Grading System

Evaluation System	Continuous assessment	Non-continuous evaluation *	Description
Progress Tests	7.50%	0.00%	Progress test 1. Non-compulsory activity that can be retaken (rescheduling). To be carried out at the end of the first third of the teaching period.
Progress Tests	15.00%	0.00%	Progress test 2 Non-compulsory activity that can be retaken. To be carried out at the end of the second third of the teaching period.
Progress Tests	27.50%	0.00%	Progress test 3. Non-compulsory activity that can be retaken. To be carried out during the non-teaching period
Theoretical papers assessment	15.00%	15.00%	Non-compulsory activity that can be retaken. To be carried out before end of teaching period.
Laboratory sessions	25.00%	25.00%	Compulsory activity that can be retaken. To be carried out during lab sessions
Oral presentations assessment	10.00%	10.00%	Non-compulsory activity that can be retaken. The students in the continuous mode will be evaluated in theory/laboratory sessions The students of non-continuous mode will be evaluated from this activity through of an alternative system.
Final test	0.00%	50.00%	Compulsory and can be retaken activity to to be carried out on the date scheduled for the final ordinary exam.
Total:	100.00%	100.00%

According to art. 4 of the UCLM Student Evaluation Regulations, it must be provided to students who cannot regularly attend face-to-face training activities the passing of the subject, having the right (art. 12.2) to be globally graded, in 2 annual calls per subject , an ordinary and an extraordinary one (evaluating 100% of the competences).

Evaluation criteria for the final exam:

Continuous assessment:

In compulsory activities, a minimum mark of 40% is required in order to pass that activity and
have the possibility to therefore pass the entire subject. The evaluation of the activities will be
global and therefore must be quantified by means of a single mark. In the case of the activities
that may be retaken (i.e., rescheduling), an alternative activity or test will be offered in the
resit/retake exam call (convocatoria extraordinaria).

The progress tests will be common for all the theory/laboratory groups of the subject and will
be evaluated by the lecturers of the subject in a serial way, i.e., each part of the progress tests
will be evaluated by the same lecturer for all the students. A student is considered to pass the subject
if she/he obtains a minimum of 50 points out of 100, taking into account the points obtained in
all the evaluable activities, and also has passed all the compulsory activities.

For students who do not pass the subject in the final exam call (convocatoria ordinaria), the
marks of activities already passed will be conserved for the resit/retake exam call (convocatoria
extraordinaria). If an activity is not recoverable, its assessment will be preserved for the
resit/retake exam call (convocatoria extraordinaria) even if it has not been passed. In the case
of the passed recoverable activities, the student will have the opportunity to receive an
alternative evaluation of those activities in the resit/retake exam call and, in that case, the final
grade of the activity will correspond to the latter grade obtained.

The mark of the passed activities in any call, except for the progress tests, will be conserved for
the subsequent academic year at the request of the student, provided that mark is equal or
greater than 50% and that the activities and evaluation criteria of the subject remain unchanged
prior to the beginning of that academic year.

The failure of a student to attend the progress test 3 will automatically result in her/him
receiving a "Failure to attend" (no presentado). If the student has not passed any compulsory
evaluation activity, the maximum final grade will be 40%.
Non-continuous evaluation:

Students may apply at the beginning of the semester for the non-continuous assessment mode.
In the same way, the student may change to the non-continuous evaluation mode as long as
she/he has not participated during the teaching period in evaluable activities that together
account for at least 50% of the total mark of the subject. If a student has reached this 50% of
the total obtainable mark or the teaching period is over, she/he will be considered in continuous
assessment without the possibility of changing to non-continuous evaluation mode.

Students who take the non-continuous evaluation mode will be globally graded, in 2 annual calls
per subject, an ordinary and an extraordinary one (evaluating 100% of the competences),
through the assessment systems indicated in the column "Non-continuous evaluation".
In the "non-continuous evaluation" mode, it is not compulsory to keep the mark obtained by the
student in the activities or tests (progress test or partial test) taken in the continuous assessment
mode.

Specifications for the resit/retake exam:

Evaluation tests will be conducted for all recoverable activities. Due to the nature of the progress
tests, in the resit/retake exam (convocatoria extraordinaria) there will be a single progress test
that includes the three progress tests.

Specifications for the second resit / retake exam:

Same characteristics as the resit/retake exam call.

9. Assignments, course calendar and important dates

Not related to the syllabus/contents
Hours	hours

General comments about the planning:

The course is taught in three weekly sessions of 1.5 hours.

10. Bibliography and Sources

Author(s)	Title	Citv	Publishing house	ISBN	Year	Description	Link

Adriaans, P. W.; Zantinge, D.	Data Mining.		Addison-Wesley		1996
Berry, M. J. A.; Linoff, G.	Data Mining Techniques.	New York	Wiley Computer Publishing.		1996
Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.	The KDD Process for Extracting Useful Knowledge from Volumes of Data.				1996
Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R. (Eds)	Advances in Knowledge Discovery and Data Mining.	Cambridge MA	AAAI/MIT Press		1996
Igual, Laura, Seguí, Santi	Introduction to Data Science		Springer	9783319500171	2017	This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning and the practical application of data science.	https://link.springer.com/book/10.1007%2F978-3-319-50017-1
Jan Van der Plass	Python Data Science Handbook		O'Reilly	9781491912058	2016		https://learning.oreilly.com/library/view/python-data-science/9781491912126/
Jefrey Leek	The Elements of Data Analytic Style		LeanPub		2014		http://worldpece.org/sites/default/files/datastyle.pdf