Statistics has its own significance in data science, but it’s not the only thing which data scientists have to deal with. Statistics are of two kinds – Bayesian and Classical.
When people start talking about statistics, they are most often talking about classical statistics; but understanding both is beneficial. When you focus more on machine learning algorithms and inferential techniques, you will need to use linear algebra more than usual.
The commonly used way to address hidden characteristics within a data set is known as SCD. The method SCD has its grounding in matrix math and hardly need classical statistics. A data scientist should have a proper understanding of mathematics in all areas.
Traditional Methods for Statistics
Processing of big data can’t be achieved through conventional methods. Instead, when it comes to unstructured data, you will need specialized data modelling systems, techniques, and tools to remove information and insights as required by businesses.
Data science is applied as a scientific approach that uses statistical and mathematical ideas along with computer tools to easily process big data. It leverages different areas to align and prepare big data in order to drive information and insights. The key areas are as under-
Data programming
Data mining
Data cleansing
Intelligent data capture techniques
Mathematics ...
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