Materia
Temas Avanzados en Economía / Advance Topics in Economics
Datos generales de la materia
- Modalidad
- Presencial
- Idioma
- Inglés
Descripción y contextualización de la asignatura
Students will learn to apply different methodologies learned in other subjects of the master using Python or R while working in an integrated development environment (JupyterLab and RStudio).This course will also place special emphasis on interactive output and data visualization techniques such as R Shiny or Python Dash which will allow students to learn how to communicate their results better.
It will use a project-based learning approach that draws from methodologies already taught in other subjects, which will allow students to focus on their applications with modern tools.
Profesorado
| Nombre | Institución | Categoría | Doctor/a | Perfil docente | Área | |
|---|---|---|---|---|---|---|
| VEGA BAYO, AINHOA | Universidad del País Vasco/Euskal Herriko Unibertsitatea | Profesorado Agregado | Doctora | Bilingüe | Fundamentos del Análisis Económico | ainhoa.vega@ehu.eus |
Competencias
| Denominación | Peso |
|---|---|
| Comprender los principios económicos que rigen el funcionamiento del área de estudio | 50.0 % |
| Determinar cuestiones relevantes y modelos de decisión individual en relación con el área de estudio que se plasmen en modelos teóricos o empíricos | 50.0 % |
Tipos de docencia
| Tipo | Horas presenciales | Horas no presenciales | Horas totales |
|---|---|---|---|
| Magistral | 24 | 36 | 60 |
| P. Ordenador | 16 | 24 | 40 |
Actividades formativas
| Denominación | Horas | Porcentaje de presencialidad |
|---|---|---|
| Clases expositivas | 16.0 | 100 % |
| Ejercicios | 8.0 | 100 % |
| Lectura y análisis prácticos | 60.0 | 0 % |
| Tutorías | 16.0 | 100 % |
Sistemas de evaluación
| Denominación | Ponderación mínima | Ponderación máxima |
|---|---|---|
| Examen escrito | 60.0 % | 80.0 % |
| Trabajos Prácticos | 20.0 % | 40.0 % |
Resultados del aprendizaje de la asignatura
- Use the basic libraries for data analysis in Python or R- Work from an IDE such as JupyterLab or RStudio
- Apply modeling techniques such as logistic regression from a machine learning perspective
- Build an interactive dashboard using Shiny or similar
Temario
1. The Data Scientist’s Toolkit.2. Empirical Applications.
Classification models. Logistic regression. Feature selection and splitting data. Model development and prediction. Model evaluation. Causal impact case study. Examples.
3. Interactive Outputs and Data Visualization.
Introduction to dashboards and other interactive outputs. Basic dashboard creation and interactive graphs with Shiny. Data visualization with Plotly. Informative reports and reproducible research with Markdown.
Bibliografía
Bibliografía básica
PythonOfficial Python tutorial: https://docs.python.org/3/tutorial/index.html
A great introductory book: Downey, A. (2012). Think Python. " O'Reilly Media, Inc.". Jupyterlab documentation: https://jupyterlab.readthedocs.io/en/latest/
An introduction to machine learning with scikit-learn: https://scikit- learn.org/stable/tutorial/basic/tutorial.html
R
Official R tutorial: Venables, W. N., & Smith, D. M. (2008). An introduction to R: notes on R, a programming environment for data analysis and graphics. https://cran.r- project.org/doc/manuals/r-release/R-intro.pdf Version 4.1.0 (2021-05-18).
A superb introductory book that uses the tidyverse collection of packages: Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.". It is also available to read online, completely for free, at https://r4ds.had.co.nz
RStudio documentation: https://docs.rstudio.com
Machine learning with the mlr package: https://mlr.mlr-org.com
Introduction to Shiny: https://shiny.rstudio.com/tutorial/
Mastering Shiny: Wickham, H. (2021). Mastering shiny. " O'Reilly Media, Inc.". It is also available to read online completely for free at https://mastering-shiny.org