# What Do Data Scientists Actually Do?

_The age old question…_

Let’s start by addressing the elephant in the room - there are thousands of blog articles out there that aim to answer this exact question. Why on earth am I adding another opinion to the mix? Well, as I explained in a [recent-ish talk](http://www.youtube.com/watch?v=J6ZRFt97Rhg&t=1787s), _data science is different now_. Also, not everyone wants to read a 20-minute manifesto on the ins and outs of deep learning, not to mention seeing another bloody [Venn diagram](http://www.google.com/search?q=data+science+venn+diagram&tbm=isch&ved=2ahUKEwi9gvnn7aznAhWbD7cAHXZbBrcQ2-cCegQIABAA&oq=data+science+venn+diagram&gs_l=img.3..0i7i30l8j0i7i5i30l2.4138.6272..6384...2.0..1.357.2120.0j12j0j1). I want to share a simple, digestible take on what data scientists do in practice.

OK, on to the article…

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## What do data scientists do?

I get asked this a lot. Like a lot a lot. And I’ve found when I give an answer, it can feel a little unsatisfactory, both to me, and to the person asking. I feel unsatisfied because it’s a general answer - data is data, we analyse it. What data? All data. Any data. Which industry? Lots. Many. All…? Most. Being a generalist is not the most popular choice in today’s hyper-specialised workforce.

For the person asking, the answer is either too high-level, or the opposite. The question is phrased as _what_, but they usually mean _how_. Or if I answer _how_, it becomes a _why_. So let’s try and give a well-rounded answer that still fits within the parameters of an elevator pitch.

### High-level (the WHAT)

Data scientists create value from data. This may be in the form of insights for decision makers, automation / optimisation of important processes, and general problem solving. We translate business problems into quantifiable objectives and build out technical solutions driven by data.

### Low-level (the HOW)

Data scientists use a range of frameworks, algorithms, tools and techniques to analyse data. We write code in languages like Python, R and SQL to manipulate, transform and interrogate data, and run experiments, often for the purposes of surfacing useful insights or predicting future behaviour. Machine learning? You bet - it’s one of our core techniques. Deep learning? Yes, that too, when needed. We wield a mix of statistics and computer science, backing it up with communication and data visualisation skills to deliver results in a way that our stakeholders can understand.

### The game (the WHY)

At least in the context of business - to increase revenue or reduce costs. It couldn’t be any simpler, or more obvious - yet we’ve seen too many projects where these objectives are either disconnected from the process or are off the table completely. As generic as this is - if the work does not have a clear purpose - what is the point?

A slightly different, but related definition, is that data science is there to support and improve decision-making. A business that makes good decisions is a business that excels. This is why stakeholder buy-in is so important to data science success. And this is why data science is so important.