I am an Data Scientist, Full-stack WebDev and Oceanographer, with eleven years of experience as a Navy Officer.

www.oceano.live. This open source app is a combination of three packages:

• oceanoobsbrasil: PYTHON. A pip python package for retrieving weather and ocean data for the Brazilian Coast. These data came from more than 1100 sources, like buoys, tide gauges, weather stations, satelites and ships. The data are merged in a single postgre DB. Use of BS4, selenium, xarray, requests, packegenlite, pandas, sqlalchemy. Codes are running in a AWS EC2 using Apache Airflow;
• oceanoobsbrasil_web: Ruby on Rails, HTML, CSS, JS. A app to visualize all the data. Use of lealflet, mapbox, ajax, plotly, devise, heroku, cloudinary, s3 bucket; and
• remobs_api: Ruby on Rails. API created to connect to postgre DB and the web app.

www.dharmaproject.one. Deep learning NLP techniques and pre-trained sentiment analysis models applied in hundreds of thousands OF newspaper articles. This app is a combination of three packages:

• dharma_analysis: PYTHON. A pip package to get, preprocess and process newspaper articles. Use of API, NLP preprocessing techniques (stopwords, punctiation, lemmatize, etc), sentence transforme, clustering, cosine similarity, pre-treined sentiment analysis model, BERT transformers, mathematical analysis (hilbert curve), viz (holoviews, bokeh);
• dharmaproject: Ruby on rails, HTML, CSS, JS. App to visualize the result of the models; and
• bucketapi: PYTHON. API created to connect to postgre DB and GCP bucket to the web app. Use of fastapi, docker, GCP, Google Cloud Run.

remobs_qc: PYTHON. Code for managing the buoys of the National Buoys Program and the REMO Observacional project. This repository presents codes for obtaining data from buoys from different manufacturers that transmit data through different satellites (IRIDIUM SBD, INMARSAT, CLS ARGOS), as well as quality control routines used in the PNBOIA and data encoding in BUFR for the GTS. This repository also collect Brazilian Navy WWIII model outputs. Together with the API (pnboia_api), we develop some web app to disclose our data:

• buoy_web_antartica: Ruby on rails, HTML, CSS, JS. App for a environmental monitoring system in Antarctica. Web app: www.operantar.live;
• buoy_web_noronha: Ruby on rails, HTML, CSS, JS. App for a environmental monitoring system in Noronha. Web app: www.noronha.live;
• buoy_web_imbituba: Ruby on rails, HTML, CSS, JS. App for a environmental monitoring system for the coastal area of Santa Catarina-Brazil. Web app: www.santa-catarina.live;
• buoy_web_alcatrazes: Ruby on rails, HTML, CSS, JS. App for a environmental monitoring system for the coastal area of São Paulo-Brazil. Web app: http://alcatrazes.herokuapp.com;
• bmocampos. A site to view the data of a buoy on the Brazilian coast. Python folium, plotly, streamlit, Heroku. Use daat technique for wave plot (PARENTE, 1999). Python folium, plotly, streamlit, Heroku. App: http://bmocampos.herokuapṕ.com; and
• pnboia_api. PYTHON. API created to connect to postgre DB to our web apps. Use of fastapi, docker and AWS.

inmetpy: PYTHON. An PIP python package to access data from the BRAZIL National Institute of Meteorology (Inmet). Try: pip install inmetpy.

pnboia_qc: PYTHON. An PIP python package to quality control time series data collected in the ocean.

equip-chm112: Ruby on Rails, HTML, CSS. Website to control the equipment of the operational oceanography division of the Brazilian Navy Hydrography Center. Web app: http://equip-pnboia.herokuapp.com/.

machine-learning-examples: This repository represents a list of notebooks for analyzing data using machine learning.

deep-learning-examples: This repository represents a list of notebooks for analyzing data using deep learning.