Subject
Advance Topics in Economics
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
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.
Teaching staff
| Name | Institution | Category | Doctor | Teaching profile | Area | |
|---|---|---|---|---|---|---|
| VEGA BAYO, AINHOA | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Fundamentals of Economic Analysis | ainhoa.vega@ehu.eus |
Competencies
| Name | Weight |
|---|---|
| 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 % |
Study types
| Type | Face-to-face hours | Non face-to-face hours | Total hours |
|---|---|---|---|
| Lecture-based | 24 | 36 | 60 |
| Applied computer-based groups | 16 | 24 | 40 |
Training activities
| Name | Hours | Percentage of classroom teaching |
|---|---|---|
| Exercises | 8.0 | 100 % |
| Expositive classes | 16.0 | 100 % |
| Reading and practical analysis | 60.0 | 0 % |
| Tutorials | 16.0 | 100 % |
Assessment systems
| Name | Minimum weighting | Maximum weighting |
|---|---|---|
| Practical tasks | 20.0 % | 40.0 % |
| Written examination | 60.0 % | 80.0 % |
Learning outcomes of the subject
- 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
Temary
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.
Bibliography
Basic bibliography
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