ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 11 Total: 22
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Languages and Computer Systems, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The objective of the course is the introduction of the basic aspects of data engineering, fundamentally in the field of Big Data. The skills acquired will allow the efficient analysis and management of heterogeneous information, both structured and unstructured, within the development of AI applications, where traditional methods show their insufficiency.
- Concepts and foundations of data engineering: Basic concepts and definitions, efficient data loading problems in Big Data scenarios, massive data storage and access.
- Data cleaning and preparation techniques: Most common techniques, definition of streaming flows, data quality metrics.
- Advanced data structures and efficient data warehouses for Big Data: Multidimensional Data Warehouses and Databases, Data Lakes, NoSQL Databases.
Basic Bibliography:
Sadalage, Fowler. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Addison-Wesley, 2012.
Avi Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts, Sixth edition, McGraw-Hill, 2010. ISBN 0-07-352332-1
Ihab F. Ilyas and Xu Chu. 2019. Data Cleaning. Association for Computing Machinery, New York, NY, USA.
Alex Gorelik, The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science, O’ Reilly Media, Inc., 2019. ISBN: 9781491931554.
Matt Casters, Roland Bouman, Jos van Dongen,, Pentaho Kettle Solutions: Building Open Source ETL Solutions with Pentaho Data Integration, 978-0470635179, Wiley, 2013.
The Degree competences considered in this course (see the Degree definition document) are the following:
Basic and general skills: CG2, CG3, CG4, CG5, CB6, CB7, CB8.
Transversal competences: CT3, CT7, CT8, CT9.
Specific skills: CE16.
More specifically, the student will be able to:
- Acquire the skill of analysis and data modeling for data processing in intelligent systems.
- Acquire knowledge and understanding related to the data extraction, cleaning, transformation, loading and processing.
- Acquire knowledge related to the use of multidimensional and NoSQL databases.
- Acquire knowledge related to the fundamentals of data lakes and data warehouses.
The methodology of this course will be based on the combination of three types of face-to-face activities with autonomous work of the students.
Master class: The instructor presents a topic to the students with the aim to provide specific knowledge. This teaching methodology will be applied to the training activity of classes of theory.
Laboratory work: The teaching staff of the course poses to the students one or various problems of practical nature whose resolution requires the understanding and application of the theoretical-practical knowledge included in the contents of the course.
The students may work on the proposed problems individually or in groups. This teaching methodology will be applied to the training activity "Practical laboratory classes" and may be applied to the training activity of "Problem-based learning sessions, seminars, case studies and projects".
Conditions applied to the two opportunities in June and July.
Laboratory practical (60%): Several laboratory practices aimed to evaluate the understanding of the knowledge exposed in theory and/or practical classes. To pass this part of the course the student has to obtain a grade equal or greater than 5 points (out of 10).
Essay questions exam (40%): The exam covers all the topics of the course. Students must develop, relate, organise and present the knowledge they have on each given topic in a reasoned and well-articulated answer. To pass this part of the course the student has to obtain a grade equal or greater than 5 points (out of 10).
To pass the course the student has to obtain a grade equal or greater than 5 points (out of 10).
OTHER CONSIDERATIONS
If plagiarism is detected in any of the works (essays or project), the final grade will be "Suspenso" (0) and the
situation will be notified to the School's Board to take the appropriate disciplinary actions. If translation errors
cause any contradictions between the various versions of this syllabus, the English will be the prevailing
version.
12 face-to-face hours of theory classes. 12 face-to-face hours of laboratory classes and project-based learning. 50 hours of autonomous and personal work of the students
Follow the proposed methodology, attending classes, devoting the necessary time to study and carrying out assignments and solving specific problems with the help of teachers in tutorial sessions
The virtual campus will be used to improve communication between students and teachers, to host the necessary material and to support the evaluation processes
Pedro Celard Perez
Coordinador/a- Department
- Electronics and Computing
- Area
- Languages and Computer Systems
- pedro.celard [at] usc.es
- Category
- Professor: Intern Assistant LOSU
| Monday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLE_01 | English | IA.02 |
| Wednesday | |||
| 15:30-17:00 | Grupo /CLIL_01 | English | IA.02 |
| 12.17.2024 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 12.17.2024 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 06.18.2025 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 06.18.2025 16:00-20:00 | Grupo /CLE_01 | IA.02 |