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About me

Hello there! My name is Julio Pimentel. I am Data Scientist with experience in data analytics, data visualisation, machine learning algorithms, and lean six sigma projects. I am currently cursing the Master of Information Technology at Griffith University, located in Queensland, Australia.
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I have five years of experience in Consumer Goods companies, where I have supported decisions based on data analytics tools and constant engagement with stakeholders to meet our goals. My expertise is in helping organisations to design innovative supply chain, procurement, and manufacturing solutions.

I am also passionate about environmental projects. I have been working as Science Tutor with Safe Passage, a non-profit organisation that provides education for students living around the Guatemala City garbage dump. This volunteer experience inspired my dissertation of the Master in Environmental Economics. I proposed several alternatives to make the Guatemala City garbage dump a safe, clean and sustainable organisation.
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I am open to helping your organisation with other types of enquiries. Just contact me!

portfolio

Data@ANZ Virtual Internship‍

The project was based on a dataset containing 3 months worth of transactions for 100 hypothetical customers. It includes purchases, recurring transactions, and salary transactions. ANZ expected a presentation with many insights as possible.

SEEK Job Market analysis

This project was the final assignment of the course 7030ICT Introduction to Big Data Analytics at Griffith University. In this project, we analysed the Seek Job Market dataset.

Amazon Reviews Insights

This project was the final assignment of the course 7130ICT Data Analytics at Griffith University. In this assignment, we studied the Amazon review dataset. The analysis was focused on the subset "Tablets" to make a more comprehensive analysis.

Data Visualisation Workshops

The Data Visualsation Workshop was developed by Dr Mark Griffin. This repository contains the R Scripts used to present different types of plots.

Machine Learning & Deep Learning Applications

This repository contains applications of the following algorithms: Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, Image Processing, and Deep Learning.