What is Data Science?

Chapter 1 is a detailed introduction to data science.

To be honest, the chapter was longer than I expected considering how lazy I am. So, I’m going to do a brief introduction of my own and then blog about the rest in subsequent posts.

What is Data Science?

Data science is a computational science of extracting meaningful insights from raw data and then effectively communicating those insights to generate value.

(Lillian Pierson. 2017. Data Science for Dummies [2nd Edition])

Okay, if you happened to take Computer Science as a subject in primary school or secondary school, you’ve most likely been taught, made to memorize or cram (like me) the six stages of data processing. Remember?

Data collection, preparation, input, processing, output/interpretation and data storage.

Well, let’s just say that you’re putting those stages you crammed to actual use now. Even though you’ve been doing that the whole time without giving it much thought. Probably.

For example, considering the best place to get a decent meal at a reasonable price. Based on your past experiences and the experiences of a few friends, you collect data, arrange your restaurant options, make sense of it and then choose the one that’s both beneficial to your palate and your wallet. That’s low-key data science work right there. Take it or leave it😂. We work with data everyday.

Data science is all about gathering data, processing it, putting it out there in such a way that someone who isn’t a data scientist can understand and then using that data to make better decisions, with the help of mathematics and statistics, coding and expertise in the field you’re trying to help.

It’s about making sense of big data (data that’s so plentiful that it’s wreaking havoc in traditional database structures).