Guías Docentes Electrónicas
1. General information
Course:
DATA MINING
Code:
42348
Type:
CORE COURSE
ECTS credits:
6
Degree:
347 - DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING (CR)
Academic year:
2021-22
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:
Technical documentation in English
English Friendly:
Y
Web site:
Virtual space of the course at https://campusvirtual.uclm.es
Bilingual:
N
Lecturer: JOSE ANGEL OLIVAS VARELA - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Fermín Caballero / 3.10
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
6476
joseangel.olivas@uclm.es
Available at https://esi.uclm.es/categories/profesorado-y-tutorias

Lecturer: FRANCISCO PASCUAL ROMERO CHICHARRO - Group(s): 20 
Building/Office
Department
Phone number
Email
Office hours
Fermin Caballero / 3.17
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
5748
franciscop.romero@uclm.es
Available at https://esi.uclm.es/categories/profesorado-y-tutorias

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.
CM7 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
Knowledge and development of computational learning techniques, both supervised and unsupervised, and design and implement applications and systems that use them.
Development and implementation of a small to medium-sized information retrieval system.
Description and application of different phases of the discovery process of knowledge extraction from large volumes of data.
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 CM7 0.6 15 N N Teaching of the subject matter by lecturer (MAG)
Individual tutoring sessions [ON-SITE] CM05 CM7 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 CM7 INS01 SIS01 SIS03 SIS09 1.8 45 N N Self-study (EST)
Other off-site activity [OFF-SITE] Practical or hands-on activities CM05 CM7 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 CM7 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 CM7 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 CM7 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 CM7 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 CM7 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 CM7 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 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
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.
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 and 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 recoverable activity to to be carried out on the date scheduled for the final ordinary examination .
Total: 100.00% 100.00%  
According to art. 6 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. 13.2) 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.T
10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
 
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  



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