dc.contributor.advisor | Ruiz Parrales, Iván Rubén | |
dc.contributor.author | Montes Buenaire, Gianella Andreina | |
dc.date.accessioned | 2023-11-06T04:28:51Z | |
dc.date.available | 2023-11-06T04:28:51Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://dspace.utb.edu.ec/handle/49000/15002 | |
dc.description | This case study focuses on carrying out an exhaustive comparative analysis between the two leading programming languages in the field of data analysis: R and Python. The main objective is to evaluate and compare the ease of use, efficiency and visualization capabilities of both languages in the context of data analysis. Ease of use refers to the accessibility and friendliness of each language for users with various levels of programming experience. Efficiency relates to speed and performance in data processing, especially on large and complex data sets. Visualization capabilities focus on the ability to generate effective graphs and visualizations to explore and communicate results. Various data analysis scenarios will be carried out that will address aspects such as data manipulation, statistical calculations and generation of visualizations in both languages. These scenarios will provide insight into how R and Python perform in real-world situations. The result of this study will allow data analysis professionals and students to make informed decisions about which programming language is best suited for their specific needs, considering ease of use, efficiency, and visualization capabilities. | es_ES |
dc.description | This case study focuses on carrying out an exhaustive comparative analysis between the two leading programming languages in the field of data analysis: R and Python. The main objective is to evaluate and compare the ease of use, efficiency and visualization capabilities of both languages in the context of data analysis. Ease of use refers to the accessibility and friendliness of each language for users with various levels of programming experience. Efficiency relates to speed and performance in data processing, especially on large and complex data sets. Visualization capabilities focus on the ability to generate effective graphs and visualizations to explore and communicate results. Various data analysis scenarios will be carried out that will address aspects such as data manipulation, statistical calculations and generation of visualizations in both languages. These scenarios will provide insight into how R and Python perform in real-world situations. The result of this study will allow data analysis professionals and students to make informed decisions about which programming language is best suited for their specific needs, considering ease of use, efficiency, and visualization capabilities. | es_ES |
dc.description.abstract | Este caso de estudio se enfoca en realizar un análisis comparativo exhaustivo entre los dos lenguajes de programación líderes en el campo del análisis de datos: R y Python. El objetivo principal es evaluar y comparar la facilidad de uso, la eficiencia y las capacidades de visualización de ambos lenguajes en el contexto del análisis de datos. La facilidad de uso se refiere a la accesibilidad y amigabilidad de cada lenguaje para usuarios con diversos niveles de experiencia en programación. La eficiencia se relaciona con la velocidad y el rendimiento en el procesamiento de datos, especialmente en conjuntos de datos grandes y complejos. Las capacidades de visualización se centran en la capacidad de generar gráficos y visualizaciones efectivas para explorar y comunicar resultados. Se realizarán diversos escenarios de análisis de datos que abordarán aspectos como la manipulación de datos, cálculos estadísticos y generación de visualizaciones en lenguaje amboss. Estos escenarios proporcionarán información detallada sobre cómo R y Python se desempeñan en situaciones del mundo real. El resultado de este estudio permitirá a los profesionales y estudiantes de análisis de datos tomar decisiones informadas sobre qué lenguaje de programación es más adecuado para sus necesidades específicas, considerando la facilidad de uso, la eficiencia y las capacidades de visualización. | es_ES |
dc.format.extent | 45 p. | es_ES |
dc.language.iso | es | es_ES |
dc.publisher | Babahoyo: UTB-FAFI. 2023 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Ecuador | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ec/ | * |
dc.subject | Análisis de Datos | es_ES |
dc.subject | Python | es_ES |
dc.subject | Facilidad de Uso | es_ES |
dc.subject | Eficiencia | es_ES |
dc.subject | Visualización | es_ES |
dc.subject | Comparación de Lenguajes | es_ES |
dc.subject | Ciencia de Datos | es_ES |
dc.title | Análisis comparativo entre los lenguajes de programación R y Python en cuanto a facilidad de uso, eficiencia y visualización en el análisis de datos. | es_ES |
dc.type | bachelorThesis | es_ES |