Objectives of Programme's Learning Outcomes

• Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
• Manage large-scale software development projects.
• Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
• Demonstrate independence and their ability to work alone or in teams.
• Manage research, development and innovation.
• Understand unprecedented problems in computer science and its applications.
• Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
• Master communication techniques.
• Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
• Develop and integrate a high degree of autonomy.
• Pursue further training and develop new skills independently.
• Develop and integrate a high degree of autonomy to evolve in new contexts.
• Apply scientific methodology.
• Critically reflect on the impact of IT in general, and on the contribution to projects.
• Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To be able to analyze correctly biological data with time-dependencies, to fit a nonlinear model (kinetic curve, growth model, dose-response curve, etc.) and to find useful information in a large dataset using data mining and machine learning tools. To be able to present results in a reproducible way (reports) and to use professional software in data science: R, RStudio, R Markdown, git.

Content of UE

Space-time series; machine learning; random forest; discriminant analysis; nonlinear regression; growth model; dose-response curve; Von Bertalanffy; Richards; Weibull; Gompertz; R and RStudio software including R Markdown and Notebook, git.

Prior Experience

Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. Uni- and bivariate statistics, including ANOVA, variance, covariance and correlation.

Type of Assessment for UE in Q1

• Oral examination

Q1 UE Assessment Comments

Preparation of a theoretical subject, or based on a partly solved dataset during 1/2h. Discussion around this question (explanation of the method, what to do next, others methods appliable on such data, etc.)

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Preparation of a theoretical subject, or based on a partly solved dataset during 1/2h. Discussion around this question (explanation of the method, what to do next, others methods appliable on such data, etc.)

Type of Resit Assessment for UE in Q1 (BAB1)

• Oral examination
• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.