I want to track the progress of my studies in this broad area. I do not intend to list a huge number of resources or courses, just the ones that I have completed so far and the following ones that are on my mind for a next step (in a short/medium or even long term). Anyway, valuable resources that may be part of my plan in the future.

	Name	School	End Date	Certified
✔️	Complete Python Bootcamp: Go from zero to hero in Python 3	Udemy	Aug 2017
✔️	Python	Kaggle	Dec 2018	N/A
◻️	Machine Learning	Kaggle
◻️	Pandas	Kaggle
◻️	Deep Learning	Kaggle
◻️	Data Visualisation	Kaggle
◻️	SQL	Kaggle
◻️	Time Series with Siraj	Kaggle
◻️	R	Kaggle
◻️	Embeddings	Kaggle
◻️	Machine Learning Explainability	Kaggle
◻️	Probabilidade e Estatística	Veduca
◻️	Machine Learning e Data Science com Python	Udemy
◻️	The Top 5 Machine Learning Libraries in Python	Udemy
◻️	Deep Learning Prerequisites: The Numpy Stack in Python	Udemy
◻️	Python Fundamentos para Análise de Dados	Data Science Academy
◻️	Introdução à Ciência de Dados 2.0	Data Science Academy
◻️	Scraping and Data Mining for Beginners and Pros	Udemy
◻️	Learn Educational Data Mining (EDM)	Udemy
◻️	Python Hand-on Solve 200 Problems	Udemy
◻️	Understanding Data Quality and Reference Data Management	Udemy
◻️	Data Warehouse Steps	Udemy
◻️	Data Manipulation and PCA (Principal Component Analysis)	Udemy
◻️	Introduction to Data Analysis for Government	Udemy
◻️	Data Integration with the MarkLogic NoSQL Database	Udemy
◻️	Mobile ML and Data Science with nitroproc	Udemy
◻️	Learn Data Science With R Part 1 of 10	Udemy

• Python
• IPython
• SciPy
• NumPy
• Matplotlib
• Pandas
• PyDataset
• db.py

• LinkedIn
• Twitter

This document's structure was inspired by the @joaopaulolndev's Data Science Roadmap.