EAGLE-Covering the training gap in digital skills for european SMEs manpower

Free training

Partner: Burgos University
Faculty/Department: Digitization Department
Course code:Course title: Enterprise Machine Learning based on R. Learn how to get the most out of your data.
Format: online
Workload: 30 hours (20 sychronous lecture hours and 10 hours of autonomous work)
Number of ECTS credits awarded / Type of certification: 2 ECTS
Course completion requirements:
Continuous assessment: (20%): Monitoring of students’ work through evaluation questionnaires.
Final evaluation (80%): Performance evaluations of the supervised learning and unsupervised learning algorithm studied through the course.
Target audience:
Primary: People working in ICT and SMEs technicians.
Other: Students of technical careers, biotechnology or research staff.
Learning outcomes:
Upon the completion of the course, the participant will:
Understanding the basic criteria for using R language for data analysis.
Learning to create, extract, preprocess and visualize information. Learn how to create linear and nonlinear predictive models using supervised learning for classification and regression tasks. Practice using unsupervised learning to extract information from clusters.
Course structure and syllabus:
hours below are shown in brackets as follows (synchronous / autonomous work)  

Transversal concepts (2h / 1h) – Data Science introduction in R. Main data types and programming structures. Vectors and matrixes. Dataframes. R Markdown.
Core content (2h / 1h) – Data manipulation and visualization. Types of variables. Normalization. One-hot encoding. Plotting with ggplot.
Expert content (12h / 6h) – Supervised learning and unsupervised learning. Classification, regression and clustering tasks. Linear, non-linear and projection models. Evaluation strategies. Performance metrics.
PL module (3.5h / 2h) – Students will work on a use case of a real-life problem where they will apply the knowledge acquired during the course.
Guided reflection and feedback (0.5h / 0h) – Depending on the results of the continuous assessment, feedback will be given on the basic concepts, feedback will be given on the proposed project.  
Recommended readings:   Norman Matloff. 2011. The Art of R Carmen Ximénez y Javier Revuelta. 2022. Análisis de datos en Lenguaje R. 2022.  https://doi.org/10.15366/9788483448304.dt.107%20 Cesar Pérez López. 2015. R. Lenguaje de programación y análisis estadístico de datos  
Language competence required: Spanish
Evaluation grid:
ABCDEFX
90-100%80-89.9%70-79.9%60-69.9%50-59.9%0-49.9%
Lecturer(s):  Nuño Basurto Hornillos, Daniel Urda Muñoz  

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.

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