I'm a Data Scientist (looking forward to a fully Machine Learning Engineering approach) whose experience goes from automating data extraction pipelines to deploying machine learning models and pipelines on cloud services.
Want to know how I got from Music to Data Science? Click here
You'll probably find many unfinished work here. Unfortunately, time is being short for dealing and with end-to-end side projects, but I'm doing my best to improve it and turn this profile into a portifolio.
- Languages: Python, SQL
- Reproducible ML: Airflow, Kedro, MLFlow
- Containers: Docker/Docker Compose
- Unix
- Business intelligence: Metabase, Google Data Studio, Power BI
- Versioning: Git, Github
- CI/CD: Github Actions and Azure Devops
- Big Data: PySpark
- Clouds: AWS, GCP
- IaC: Terraform
- Product Management: Kanban, Scrum
- Within Pythonic World:
- WebScrapping: Selenium, BeatifulSoup
- Data Processing: Pandas, Numpy, Scipy, NLTK
- Modelling/Machine Learning: Scikit-learn, XGBoost, Statsmodels
- Deep learning: Keras, Tensorflow
- NLP: NLTK, BERT, Word and Doc2Vec
- Data Visualization: Matplotlib, Seaborn, Plotly
- Deployment: FastAPI, Streamlit, Gradio
- Kuberenetes
- Causal Inference
- Grhaps - networks and databases