Guías Docentes Electrónicas
1. General information
Course:
DATA MANAGEMENT
Code:
42402
Type:
ELECTIVE
ECTS credits:
6
Degree:
405 - DEGREE IN COMPUTER SCIENCE ENGINEERING (TA)
Academic year:
2022-23
Center:
15 - FACULTY OF SOCIAL SCIENCES AND INFORMATION TECHNOLOGIES
Group(s):
60 
Year:
3
Duration:
C2
Main language:
Spanish
Second language:
Use of additional languages:
English Friendly:
Y
Web site:
Espacio virtual de la asignatura en https://campusvirtual.uclm.es
Bilingual:
N
Lecturer: RICARDO PÉREZ DEL CASTILLO - Group(s): 60 
Building/Office
Department
Phone number
Email
Office hours
2.11
TECNOLOGÍAS Y SISTEMAS DE INFORMACIÓN
+34926051816
Ricardo.PdelCastillo@uclm.es
Available at https://www.uclm.es/toledo/fcsociales/grado-informatica/profesorado-y-tutorias

2. Pre-Requisites
Not established
3. Justification in the curriculum, relation to other subjects and to the profession
Not established
4. Degree competences achieved in this course
Course competences
Code Description
INS03 Ability to manage information and data.
INS04 Problem solving skills by the application of engineering techniques.
SI01 Ability to integrate information and communiction technology solutions and entrepeneurial process so as to fulfil the needs for information in organisation, allowing them to meet their goals in an effective and efficient manner, providing them with competitive benefits.
SI02 Ability to determine the needs of information and communication systems in an organisation, following security aspects and complying with current laws and regulations.
SI06 Ability to understand and apply principles and management techniques for quality and technological innovation in organisations.
SIS01 Critical thinking.
SIS09 Care for quality.
5. Objectives or Learning Outcomes
Course learning outcomes
Description
Understanding and managing data quality in a business environment.
Knowledge and use of key technologies for business intelligence, in order to provide the organisation with solutions for advanced decision making.
Knowledge to elicit, analyse and represent information needs and requirements.
Be familiar with data warehouse technology and be able to facilitate the integration of data into data warehouses.
Knowledge and application of the main analysis techniques and methods for big data, including unstructured data.
Knowledge and application of fundamentals and techniques of analytics and data science.
Additional outcomes
Not established.
6. Units / Contents
  • Unit 1: Introduction to Big Data
  • Unit 2: Modelling and sotring information in Big Data
  • Unit 3: Data Processing and Integration
  • Unit 4: Databases in Big Data environments
  • Unit 5: Other technologies for data management
  • Unit 6: Data Governance
  • Unit 7: Data Quality
7. Activities, Units/Modules and Methodology
Training Activity Methodology Related Competences (only degrees before RD 822/2021) ECTS Hours As Com Description
Class Attendance (theory) [ON-SITE] Combination of methods INS03 INS04 SI01 SI02 SI06 SIS01 SIS09 0.72 18 N N
Workshops or seminars [ON-SITE] Workshops and Seminars INS03 INS04 SIS01 SIS09 0.24 6 N N
Problem solving and/or case studies [ON-SITE] Problem solving and exercises INS03 INS04 SIS01 SIS09 0.48 12 N N
Laboratory practice or sessions [ON-SITE] Lectures INS03 INS04 SI01 SI02 SI06 SIS01 SIS09 0.24 6 N N
Computer room practice [ON-SITE] Guided or supervised work INS03 INS04 SI01 SI02 SI06 SIS01 SIS09 0.72 18 N N
Individual tutoring sessions [ON-SITE] Guided or supervised work INS03 INS04 SIS01 SIS09 0.2 5 N N
Practicum and practical activities report writing or preparation [OFF-SITE] Guided or supervised work INS03 INS04 SIS01 SIS09 1.08 27 Y Y
Study and Exam Preparation [OFF-SITE] Self-study SI01 SI02 SI06 SIS01 2.08 52 N N
Other on-site activities [ON-SITE] Assessment tests INS03 INS04 SIS01 SIS09 0.24 6 Y Y
Total: 6 150
Total credits of in-class work: 2.84 Total class time hours: 71
Total credits of out of class work: 3.16 Total hours of out of class work: 79

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
Final test 50.00% 50.00%
Assessment of active participation 10.00% 0.00%
Laboratory sessions 25.00% 25.00%
Theoretical papers assessment 15.00% 15.00%
Total: 100.00% 90.00%  
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. If the activity consists of several sections, each section may be evaluated separately provided students are informed in writing of this evaluation criterion at the beginning of the academic year. 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 partial 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 final exam 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). 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 qualification of the passed activities in any call, except for the final test, will be conserved for the next academic year at the request of the student, provided that it is equal or superior to 5 and the training activities and the evaluation criteria of the subject are not modified in the next academic year.
    The failure of a student to attend the final test 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 who are unable to attend training activities on a regular basis may apply at the beginning of the semester for the non-continuous assessment mode. Similarly, if a student who is undergoing continuous assessment incurs any circumstance that prevents her/him from regularly attending the classroom-based training activities, she/he may renounce the accumulated mark in continuous assessment and apply for the non-continuous assessment mode. In this case, a notification by the student must be given before the date scheduled for the tests in the ordinary call, in accordance with a deadline that will be informed at the beginning of the semester.

    Students who take the non-continuous assessment 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 assessment".

    In the "non-continuous assessment" 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 criteria not defined
Specifications for the second resit / retake exam:
Evaluation criteria not defined
9. Assignments, course calendar and important dates
Not related to the syllabus/contents
Hours hours

10. Bibliography and Sources
Author(s) Title Book/Journal Citv Publishing house ISBN Year Description Link Catálogo biblioteca
 
Baldominos Gomez Resolviendo problemas de Big Data Marcombo 8426732135 2020  
Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy, Jessi Ashdown Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness O'Reilly 1492063495 2021  
Herbert Jones Analítica de datos: Una guía esencial para principiantes en minería de datos, recolección de datos, análisis de big data para negocios y conceptos de inteligencia empresarial Bravex Publications 1647482798 2018  
Luis Joyanes Aguilar BIG DATA: ANALISIS DE GRANDES VOLUMENES DE DATOS EN ORGANIZACIONES Marcombo 8426720811 2013  
Martin Kleppmann Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems O'Reilly 1449373321 2016  
Piethein Strengholt Data Management at Scale: Best Practices for Enterprise Architecture O'Reilly 149205478X 2020  



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