Edinei Santos

About

Data professional with an Economics degree and over 7 years of experience in the energy market. I have 6 years of experience as Data Analyst, 1 year as Business Analyst and 6 months as a Data Product Manager. My specialization includes data analysis, process automation, and API product management, enhanced by certifications in Data Science, Data Engineering, MLOps, and Data Products Management.

Technologies and Tools

I have hands-on experience with the following technologies and tools:

SQL
Python
FastAPI
Jupyter Notebook
Machine Learning
AWS Athena
Cloud Computing
Power BI
Docker
Google App Engine
GitHub Actions
TensorFlow
PyTorch
R

I have basic knowledge of the following technologies and tools:

C++
JavaScript
Java
Spark
Sagemaker
H20
Weights & Biases
MLFlow
DVC
DBT

I am committed to continuously exploring and learning new tools and technologies in the rapidly evolving field of Data Science, Machine Learning and Artificial Intelligence.

Edinei Santos's Projects

basic_algorithms_problems

This project is part of the Data Structures and Algorithms Nanodegree program. It covers the implementation and analysis of various algorithmic problems using Python 3.

build_ml_pipeline_for_short_term_rental_prices

An end-to-end solution for property management firms. This pipeline integrates weekly rental data to provide price estimations based on current market trends, aiding in competitive pricing decisions.

data_structures_problems

This project is part of the Data Structures and Algorithms Nanodegree program by Udacity. It encompasses a comprehensive exploration of various data structure concepts, demonstrating proficiency through practical Python solutions.

deploying_ml_model_with_fastapi

This project demonstrates the deployment of a machine learning model as a RESTful API using FastAPI. The model, trained on census income data, is containerized with Docker and deployed on Google Cloud's App Engine. The project includes CI/CD practices via GitHub Actions, providing a streamlined approach for ML model deployment and serving.

disaster_response_pipeline

This project is part of the Data Scientist Nanodegree Program from Udacity. It aims to develop a machine learning pipeline to classify disaster response messages. The goal is to build a model that can categorize messages into 36 different categories, helping disaster response organizations prioritize and direct resources effectively.

dynamic_risk_assessment_system

Dynamic Risk Assessment System: A simplified portfolio project showcasing data ingestion, model deployment, monitoring, and reporting in a machine learning pipeline.

image_classifier_with_pytorch

This project is part of the Introduction to Machine Learning with PyTorch course from Udacity. It aims to develop an in-house object detection algorithm for a hypothetical self-driving car startup. The goal is to build a neural network that can classify arbitrary objects using the CIFAR-10 dataset.

image_classifier_with_tensorflow

Image Classifier project implemented using TensorFlow, running with GPU access in a Docker container.

near_earth_objects

This portfolio project, developed for educational purposes as part of the 'Advanced Python Techniques' nanodegree from Udacity, showcases the application of Python in analyzing and exploring the close approaches of near-Earth objects (NEOs), utilizing data from NASA/JPL's Center for Near Earth Object Studies.

predict_customer_churn_with_clean_code

This portfolio project contains a Python package designed to predict customer churn in the credit card industry. As a showcase of both coding and data science expertise, the package is built following PEP8 coding standards and incorporates best practices in software engineering.

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