GUÍA DOCENTE
STORYTELLING WITH DATA
Coordinación: | VILAPRIÑO TERRE, ESTER |
Información general de la asignatura
| Denominación | STORYTELLING WITH DATA |
| Código | 14709 |
| Semestre de impartición | 1R Q(SEMESTRE) EVALUACIÓN CONTINUADA |
| Carácter | | Grado/Máster | Curso | Carácter | Modalidad | | Máster Universitario en Investigación Biomédica | 1 | OPTATIVA | Presencial |
|
| Número de créditos de la asignatura (ECTS) | 4 |
| Tipo de actividad, créditos y grupos | | Tipo de actividad | PRAULA | TEORIA |
|---|
| Número de créditos | 2.5 | 1.5 |
|---|
| Número de grupos | 1 | 1 |
|---|
|
| Coordinación | VILAPRIÑO TERRE, ESTER |
| Departamento/s | CIENCIAS MÉDICAS BÁSICAS |
| Información importante sobre tratamiento de datos | Consulte este enlace para obtener más información. |
| Profesor/a (es/as) | Dirección electrónica\nprofesor/a (es/as) | Créditos impartidos por el profesorado | Horario de tutoría/lugar |
| SORRIBAS TELLO, ALBERT | albert.sorribas@udl.cat | 1 | |
| VAQUEIRO DE CASTRO ALVES, RUI CARLOS | rui.alves@udl.cat | 1 | |
| VILAPRIÑO TERRE, ESTER | ester.vilaprinyo@udl.cat | 2 | |
Objetivos académicos de la asignatura
Learning results:
-
You will learn about basic data analysis and prediction techniques: dimensionality reduction, linear and logistic regression model, clusters, dendograms, neural networks and SVM.
-
You will know the main data analysis and representation packages in R (ggplot2, lattice, leaflet and shiny) and Python (seaborn, pandas and numpy)
-
You will know how to use them in a way adapted to your data and problem.
-
You will be able to generate the right visualizations to communicate your results clearly and accurately.
-
You will understand the importance of group work and cooperation among researchers.
-
You will be able to formulate work objectives, plan the work, carry out the experiments, present the results obtained and draw conclusions.
Contenidos fundamentales de la asignatura
- Story trends from data: Linear Regression and Correlations.
- From complex data to a simpler story: Principal Component Analysis and Factor Analysis.
- Group building from data: Clusters and Dendograms.
- Group assignment from data: Logistic Regression, Neural Networks, and Support Vector Machine.
- Story telling tools I. A sip of R: Tydiverse, Ggplot2, Plotly, Shiny, and Leaflet.
- Story telling tools II. A sip of Phyton: Seaborn, Pandas, and Numpy.
Ejes metodológicos de la asignatura
- Active class sessions based on data analysis and student participation.
- Introduction of statistical and technical tools by lecturers.
- Intensive use of computer programs (R and Phyton).
- Students should bring their own computer for class activities (this facilitates further work at home)
- Autonomous work by students
Plan de desarrollo de la asignatura
- The course will be developed in 20 sessions of 2 hours each. No distinction is made among practical and theoretical work.
- Depending of the situation with respect the SARS-COV-2 pandemic, some of the activites would be developed virtually.
- A specific schedule will be added in the contens section of the virtual campus as soon as we know the conditions for the next lecturing period.
Sistema de evaluación
- 20% Oral presentation of work
- 15% Class participation in discussions and data analysis
- 65% Assignments (article analysis, projects, data analysis)
Bibliografía y recursos de información
Web links, articles, data collections, etc. will be provided during the course and made available at the virtual campus.
PDF