Data Analytics Certificate
Data Analysis fundamentals and deployment.
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Data Engineering Student • UPY
I focus on end-to-end projects: ingestion, transformation, orchestration, storage, visualization, and deployment. I like clean documentation, reproducible setups, and measurable performance.
This portfolio highlights selected work across data engineering, cloud deployment, and applied machine learning. The goal is clarity: what was built, why, and how to reproduce it.
ETL/ELT workflows, orchestration (Airflow/Kafka), storage layers (SQL/NoSQL), dashboards, and performance profiling for compute-heavy routines.
I prefer simple architectures, readable code, clean commits, and structured documentation. I aim for deployments that run consistently across machines.
Verified learning milestones and training completed.
Data Analysis fundamentals and deployment.
Coursework focused on practical data workflows and python.
GPU and applied ML training, augmenting and deployment for the industry.
Selected projects with emphasis on scope, outcome, and implementation.
Designed and delivered in team collaboration, a neural network focused on cat images.
Prototype system for assisting with exoplanet signal verification using machine learning, interactive visualization, and a simple web interface.
Academic and community activities that complement project work.
Hands-on academic workshop and collaborative learning regarding Neural Networks.
Rapid prototyping, teamwork, and applied problem solving.
For collaboration, opportunities, or project discussion.