What is data science?
The common practice with buzzwords is that they become misrepresented and misused. We hope to combat this with one definition at a time. This time, we are taking on data science in the hopes that next time you hear the phrase, you will know exactly what it entails. So, what is data science?
Data science is a combination of computer science, maths & statistics, business knowledge
To start off, let’s dive into our self-constructed definition:
“Data science is an interdisciplinary field (i.e. a combination of computer science, maths and statistics, and business knowledge) that aims to provide intelligent business insights by studying, understanding, and learning from both structured and unstructured data.”
Ok, so it’s a bit of a mouthful, but it’s definitely not as complicated as it sounds. Data science is basically a multifaceted science that is a combination of “traditional” sciences.
For a fully fledged definition, with a furious amount of information about the history of data science, head over to Wikipedia.
The inevitable Venn diagram
If you’ve ever done a Google search of data science before, you’ve probably already seen this. Or at least a version of it. And while it’s painfully textbook, it does offer a visual representation of what data science consists of. Furthermore, it shows us what data science is not. It is not machine learning. It is not software development. It is not traditional research. Rather, it is all of the above.
We could go on and bend the words back and forth a little in an attempt to fill out this piece. But we won’t. Because that pretty much covers it. If we’ve left you with any burning questions, please drop us a comment below!
Are you interested in learning more about Big Data?
Good. Because we have an entire section of our blog that is dedicated to teaching Big Data terms and processes. Head over to our Resources page to explore titles including “What is Data Lineage?“, “What is the GDPR, really?“, and “What is data literacy and why do we need it?“.
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