AI News, 87 Best 21 century images in 2020

Data science is a broad and loosely defined field.

What may have begun as an application of statistics to solve business problems, is now a name that encompasses areas of big data engineering, visualisation, machine learning, deep learning and artificial intelligence.

The rapid evolution was in part due to the breadth of areas in which data science can be applied to, but also because the technologies have also developed rapidly.

imagine a one-person Data Scientist in a small organisation would have a different set of tasks to do compared to a Data Scientist in a team within a large organisation.

Compounded with the rapid decrease in job tenure (or an increase in job mobility), this variance in the job description requires the practising Data Scientist to keep up with a large number of skills and technologies.

Or perhaps you are looking to switch your career track, say from a data analyst to a data engineer or from a web developer to a machine learning engineer, in which case, you will benefit by knowing something about everything.

There are other technologies that I have learned that I will continue to keep up with (Tidyverse, Spark, and Airflow), additional technologies I will learn a little bit about this year (Vue perhaps) knowing that I probably won't use them.

Nevertheless, I think doing this kind of exercise once in a while is helpful in reassessing the pros and cons of each technology and getting the feel for the overall landscape.

I have probably read over a hundred blog posts and a hundred videos about the latest developments and commentaries about the technologies listed in the first diagram and many more.

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