Data Science Automation For Big Data and IoT Environments

The purpose of data science is not only to do machine learning or statistical analysis, but also to derive insights out of the data that a user with no statistics knowledge can understand.

The half of data science that requires manual intervention is still to be automated. However, those are areas that involve the experience and wisdom of a people: a data scientist, a business expert, a software developer, a data integrator, everyone who currently contributes to making a data-science project operational. This makes it difficult to automate every aspect of data science. However, we can think of data science automation as a two level architecture, wherein:

– Different data science disciplines/components are automated

– All the individual automated components are interconnected to form a coherent data-science system

Figure 1. The required elements of an automated data science system.

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