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Ariel Luján Giamportone photo

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followers: 3.0 following: 8.0 repos: 13.0 gists: 0.0

Name: Ariel Luján Giamportone

Type: User

Company: ALG Data&Engineering Solutions

Bio: 🛠️STEM+📈Datos+🧪Ciencia+💻TIC|🔬Investigación,🏗️ Desarrollo e💡innovación|🗺️Desarrollo Sostenible| Gestión de proyectos📑|

Location: Worlwide, Europe Based

Hi 👋, I'm Ariel Lujan Giamportone

Empowering Innovation: Bridging Engineering Expertise with Data Analytics Proficiency for Oceanic Advancements

  • 🔭 I’m currently working on Research, Development, and Innovation Projects with the Applied Chemistry to Environment, Food, and Sustainable Development Group

  • 🌱 I’m currently learning Data Analysis and Data Science at UpgradeHub

  • 👯 I’m eager to engage in collaborative ventures focusing on Data Analysis or Data Science Projects across a spectrum of dynamic sectors, including Maritime Technology, Supply Chain Management, Port and Logistics Development, Oceanography, Fisheries, and Aquaculture. My passion lies in leveraging data-driven insights to drive innovation, optimize operations, and tackle challenges within these industries. With a strong background in data analytics and a deep understanding of the complexities inherent in maritime and related fields, I am well-positioned to contribute effectively to projects that aim to enhance efficiency, sustainability, and profitability. I am enthusiastic about the opportunity to apply my expertise and collaborate with teams dedicated to advancing these crucial domains.

  • 🤝 I’m experienced in Competitive Intelligence, Technological Surveillance, Research, Development, and Innovation in Businesses and Organizations, Elevating them to the Next Level

  • 💬 Ask me about Data, Science, Technology, Development, Innovation and more.

  • 📫 How to reach me [email protected]

Connect with me:

https://www.linkedin.com/in/agiamportone https://www.researchgate.net/profile/Ariel-Lujan-Giamportone

Languages and Tools:

azure cplusplus css3 html5 java linux mysql pandas python sass seaborn

arielgiamportone

arielgiamportone

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Ariel Luján Giamportone's Projects

analisis_del_mercado_salarios_ciencia_de_datos_2023 icon analisis_del_mercado_salarios_ciencia_de_datos_2023

Este repositorio contiene un proyecto de análisis de datos que se enfoca en la exploración, limpieza, análisis y visualización de un conjunto de datos específico del área de Ciencia de Datos. El objetivo principal es extraer información significativa y valiosa a partir de los datos mediante diversas técnicas de análisis y visualización.

arielgiamportone icon arielgiamportone

"Unlocking data potential with versatile expertise! Classify me into your data analytics projects for innovative solutions."

educational_amazon_web_scraping_for_data_collection icon educational_amazon_web_scraping_for_data_collection

Este proyecto se centra en el uso de web scraping para recopilar datos específicos de Amazon, ofreciendo una plataforma educativa donde los estudiantes y entusiastas del análisis de datos pueden aprender a extraer información valiosa de manera ética y legal.

mujeres-y-ni-os-primeros_el-titanic icon mujeres-y-ni-os-primeros_el-titanic

Análisis de datos sobre la base de un subconjunto del total de pasajeros al momento del hundimiento y bajo la premisa de encontrar una relación con la famosa frase "Mujeres y Niños Primero"

selectividad_artes_de_pesca icon selectividad_artes_de_pesca

Repository dedicated to exploring the efficiency of selectivity devices in fishing gear through the application of Excel, Solver, and Maximum Likelihood Estimation (MLE)

trawl_net_model_in_advanced_excel icon trawl_net_model_in_advanced_excel

The project focuses on the analysis, sizing, and optimization of a trawl network of the FAO series, with potential applications in species from the Beagle Channel.

wandb_gradientboostingclassifier_wine icon wandb_gradientboostingclassifier_wine

This study addresses the optimization of a Gradient Boosting Classifier (GBC) applied to the Wine dataset, focusing on the systematic fine-tuning of hyperparameters. The Wine dataset, renowned for its classification challenges, serves as a robust testing ground for evaluating the efficacy of the GBC methodology.

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