Although data analytics and data science are closely related, they are ultimately different. Both involve working with data to gain insights from it, but data analytics involves and focuses on the analysis of past data to inform decisions to be made in the future. Data analysts would employ the use of tools so they can prepare, combine and analyze existing datasets, identify patterns in them and develop actionable steps. On the other hand, data science involves the use of data to build models that can predict future outcomes. Data scientists employ the use of tools to examine and then prepare large datasets. Then they use programing, machine learning and algorithms to develop new data models. Fascinating, yes? I agree but it all sounds very complex.
That is an excerpt from a previous blog entry, on the meaning of data analytics and pulling up a comparison against data science. The conclusion is also in that post but that’s not today’s discussion so you can look up entry 5 in the series if you’re interested. Now, to the topic of today.
I was taught statistics in secondary school, but for the life of me, I don’t recall understanding it as well as I did in my data analytics class. We finally moved from the heavier stuff, back to the lighter, more beginner analyst friendly topics. So now, back to statistics. I’ve never been big on numbers, which is why I thought statistics would be impossible for my brain to wrap around. But alas, it wasn’t complex. Or maybe I’m just older now and can understand certain things better. Ah yes, that’s probably the reason. Anyway, back to statistics.
We were taught that there are many policies in the world of statistics such as “conditional probability and “post theorem probability”
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