EAGLE-Covering the training gap in digital skills for european SMEs manpower
Free training
| Partner: Burgos University | ||||||
| Faculty/Department: Escuela Politécnica Superior/Digitalization Department | ||||||
| Course code: | Course title: Introduction to Big Data and Business Intelligence | |||||
| Format: online | ||||||
| Workload: 16 hours (8 synchronous lecture hours and 8 hours of autonomous work) | ||||||
| Number of ECTS credits awarded / Type of certification: | ||||||
| Course completion requirements: Continuous assessment: (20%): Monitoring of students’ work through evaluation questionnaires. Final evaluation (80%): Students will be given final assignments on data processing and reporting, requiring to put into practice theoretical and applied concepts studied during the course. | ||||||
| Target audience: Primary: SMEs technicians. SMEs middle management. Other: students of technical careers, industry technicians, research institutes, etc. | ||||||
| Learning outcomes: Upon the completion of the course, the participant will: Recognise the phases involved in a business intelligence project. Analyse the different technical options available to implement business intelligence projects. Present data insights and analysis results in a way that allows the best possible use of them by the end user. Examine and use widespread machine learning techniques to prototype processes for exploiting unstructured information. | ||||||
| Course structure and syllabus: Note: hours below are shown in brackets as follows (synchronous / autonomous work) Transversal concepts (1h / 0.5h): The Big Picture: Introduction to bussiness intelligence and big data. Big data concepts, data literacy, data analytics. Use cases. Core content (2h / 1h): Big data Infrastructures. Big Data Storage Models. Big Data Programming Models. Storage and Data Management Security. Expert content (3h / 2h): Data exploration and interpretation: Data Visualization. Data Analysis: Machine Learning (supervised leraning). Information Discovery (unsupervised learning). Data streams. CBL module (1.5h / 4.5h): Students will work on a use case of their interest to complete (a) an assignment related to data exploration and presentation and (b) an assignment related to a basic data analytics process to obtain actionable results. Guided reflection and feedback (0.5h / 0h): Feedback on quizzes and day-by-day work will be given. Moreover, the learning process will be monitored, including office hours with the students on demand and following their progress in the resolution of the CBL assignment. | ||||||
| Recommended readings: Curto Díaz, Josep., and Jordi Conesa Caralt. Introducción al business intelligence. Barcelona: Editorial UOC, 2010. Print.Ian H. Witten, Eibe Frank y Mark A. Hall. Data Mining: practical machine learning tools and techniques (third Edition). Morgan Kaufmann, 2011.Cole Nussbaumer Knaflic. Storytelling With Data: A Data Visualization Guide for Business Professionals. Wiley (2015) Betancourt Uscátegui, Jorge Fernando, and Iryopogu. Análisis de datos con Power BI, R-RStudio y Knime : curso práctico. Paracuellos de Jarama, Madrid: Ra-Ma, 2021. Print. | ||||||
| Language competence required: Spanish | ||||||
| Evaluation grid: | ||||||
| A | B | C | D | E | FX | |
| 90-100% | 80-89.9% | 70-79.9% | 60-69.9% | 50-59.9% | 0-49.9% | |
| Lecturer(s): Bruno Baruque | ||||||
More information about the EAGLE Project
This Project has received funding from the European Union’s Digital Europe Programme (DIGITAL) under the identifier No 101100660.
The views and opinions expressed in the project are solely those of the author(s) and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Interested in this course?
Send us a message and we will notify you when the course is available.
