Visualizations and Interpretabilty - Working with 3D - tSNE -R and Python-Part 3

Visualizations and Interpretabilty - Working with 3D - tSNE -R and Python-Part 3

Data Science foundations have roots in Statistical, Mathematical, and Computational Topics. Often you have to think differently as data science is mostly about unstructured data, a fair statement and data that are coming not in a designed way!

It is a joy to spend time with you all who aspire to commit or are committed to the nucleus of data science...Statistical-Mathematical-Computational foundations...

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In this weekly series, I will bring out how to think differently and hence expose how simplicity in the vast topics in the congruence of these topics can be the power that will encourage to learn a vast amount of topics in data science

I may select examples and properties of various statistical, mathematical, and computational (SMC) constructs from books or published articles but interpretations and simplicity are the purposes, I will adhere to ...

We in data science are biased towards in prioritizing the uncertainty in observations as the lead to bring together other tools and constructs such as mathematics and computational methods.

We will not forget the importance of automation of algorithms...