I have found multiple online courses and I have decided to base my learning on the path given in the Algorithmic Trading course on Udacity.
Step 1 is identifying which stock statistics are actually important to visualize and what they mean. It is also important to get accustomed with the very powerful Pandas Library and other complementary libraries such as Matplotlib and NumPy and SciPy and Seaborn.
Step 2 is understanding the widely used Financial Strategies and the basic financial concepts that govern the everyday trading process. It also important to understand what the key indicators are while making a decision and also crucial to know that no-one should rely on a single indicator to make any decision. I have resorted mainly to the Bloomberg Marketing Concepts and other few online educatoinal courses to get a good hang and understanding of some of the basic concepts.
Step 3 is learning and understanding the underlying machine-learning techniques and trying to recognize which one is more effective and what kind of data is to be fed in which kind of model.
As of June 2020, I am still on steps 1 and 2 and am trying my best everyday to gain as much knowledge as possible. I will continue to push files that summarize my learning of the key concepts and the underlying code with commented explaination. Many of these files contain functions that have been used in further files so the code duplication is to bare minimum. This is a great resource mfor aosmone starting off in financial data visaulisation and does not want code all the bsics from scratch. Anyone is welcome to download these files and import and use them in their own endeavours. Anyone interested can learn from this or contribute to it to make this repository a valuable resource.