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
Complementary Bibliography:
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.
Project-based learning: Practical projects are given to the students whose scope requires that an important part of the total dedication of the student is dedicated to the course. In addition, due to the scope of the work to be carried out, it is required not only that the students apply management skills but also technical skills.
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".
Autonomous problem solving: The teaching staff proposes to the students a problem whose scope and objectives require from them autonomous work. The process of design and implementation of the solution to the problem will be supervised by the teaching staff. In general, this methodology is applied to pieces of work with a time scope and greater effort than those of laboratory work.
The conditions below apply to both opportunities of June and July.
Project-based learning (30%): Defense of the solution provided by the student in front of the teaching staff, possibly including an oral presentation of the solution.
Laboratory work (30%): Various evaluation tests will be carried out, especially aimed at evaluating the understanding of the knowledge presented in the theory and/or practical classes.
Autonomous problem solving (40%): The evaluation of supervised autonomous work will be carried out by submitting a report and a defense in which the students explain their proposal and conclusions to the teaching staff.
Those students who either make one of the laboratory work tests or submit the project are considered as “presented”.
To pass the course in any call, the final grade must be equal to or greater than 5, and a minimum of 5 (out of 10) must be obtained in each of the parts.
For the evaluation in extraordinary calls, the students will be asked to submit a piece of work that will be used to evaluate the course as a whole.
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
Jose Ramon Rios Viqueira
Coordinador/a- Department
- Electronics and Computing
- Area
- Languages and Computer Systems
- Phone
- 881816463
- jrr.viqueira [at] usc.es
- Category
- Professor: University Lecturer
| Monday | |||
|---|---|---|---|
| 16:00-17:30 | CLE_01 | English | IA.02 |
| Wednesday | |||
| 16:00-17:30 | CLIL_01 | English | IA.02 |
| 01.11.2023 17:00-20:45 | CLIL_01 | IA.02 |
| 01.11.2023 17:00-20:45 | CLE_01 | IA.02 |
| 06.20.2023 17:00-20:45 | CLE_01 | Classroom A7 |
| 06.20.2023 17:00-20:45 | CLIL_01 | Classroom A7 |