Data science encompasses a wide range of areas, topics, and sub-domains such as Big Data, Machine & Deep learning (ETL, TensorFlow, Keras), Data Mining/Visualization (EDA), BI, Predictive Analytics, Statistical Analytics, etc.

python etl demo by jupyter notebook

Sample notebooks on Azure Databricks for ETL

Jupyter Notebook ETL from AWS S3 bucket

ETL. Creating an automated pipeline that takes in new data, performs the appropriate transformations, and loads the data into existing tables.

Drafting an ETL on jupyter notebooks that cleans and models us immigration data

Extrat, Transform and Load on movie dataset using python, pandas, jupyter notebook and postgreSQL

Executed an ETL process from an excel file using Python, Jupyter Notebook, and SQL.

Extração, transformação e carga de dados com Python utilizando a biblioteca Pandas no Jupyter Notebook

Understanding ETL, a concept in Data Engineering, using simple and easy Python code on Jupyter notebook.

ETL/Flask/ Jupyter Notebook project designed to scrape and load data into mongodb and display the information.

Extract, Transform, and Load (ETL) to create pipeline on movie datasets using PostgreSQL, Python, Pandas, and Jupyter Notebook

This is a quick script/notebook I wrote to move tables from SalesForce Education Cloud to my MSSQL server

Cleaned movie data with Python in Jupyter Notebook. Then utilized the ETL process to add the data to a PostgreSQL database.

Created a data pipeline from movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL. Implemented (ETL) - Extract, Transform, Load - to complete

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on Crowdfunding datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Performed the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

Perform the Extract, Transform and Load (ETL) process to create a data pipeline on movie datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.

AWS ETL project using google colab notebooks and pyspark to extract data sets from S3 files, transform data sets to fit SQL schema, and load into RDS instance.