I am a specialist in software engineering with long experience in development, analysis, planning and management of IT projects.

For over 15 years I have participated in numerous challenges, as a developer and analyst; but also taking on leadership responsibilities as a Scrum Master or Project Manager. I have an excellent degree of communication in Portuguese and Spanish (native) and I can also read and write without difficulties in English.

I am currently working at a leading telecommunications company in Brazil, performing monitoring and functional analysis activities in a large data ingestion project for a Hadoop environment.

Always looking for new challenges, I'm transitioning into data science; improving my knowledge in maths, statistics, advanced python language and developing projects with Pandas, Numpy, Matplotlib, Seaborn and Scikit-Learn libraries, among others.

Sobre o conhecido dataset de sobreviventes do Titanic, disponibilizado em Kaggle, implementei um fluxo de MLOps usando MLflow.

Projetos e atividades da formação em Data Science oferecida pela Minsait e ministrada por Thaís Ratis

• Numpy
• Pandas
• SQL + Pandas
• Pré-processamento
• Alguns questionamentos
• BMW Pricing Challenge - Regressão Linear work in process...

O objetivo deste projeto foi desenvolver um pipeline para movimentar um grande conjunto de dados (big data) a partir de uma fonte inicial (flatfile) até a apresentação de dashboard para tomada de decisão sobre plataforma Power BI.

(*) Atividades realizadas na formação Engenheiro de Big Data na Minsait ministrado por Caiuá França

• Notebook
• Data Analysis
• Machine Learning

144 is a telephone number implemented throughout the Argentine Republic to report domestic violence. The datasets studied in this notebook contain the records of the calls to this service.

• Pre-process
• Explore data
• k-NN
• Linear Regression

Python script to read and process Excel datasource and build a Gantt Chart

Exploring a dataset about data jobs salaries from 2020 to 2022

Argentinian economy has two exchange rates to USD. An official and an unofficial (called blue).

The goals of this notebook are:

1. Read a dataset with the official exchange rates.
2. Read a dataset with the unofficial (blue) exchange rates.
3. Merge those datasets.
4. Calculate the mean between buy and sell for both exchange rates.
5. Analyze the correlation along time for these exchange rates.

25 mistakes a Pandas developer shouldn't make