What is data science?

Data science and analytics are two of the hottest new terms around. It’s also a huge, burgeoning career option – and a growing designation too.

But many of us don’t really know what data science is.

First, let’s get into what big data is.

Big Data

The name isn’t enough to exactly cover the meaning of the term itself, but here’s a little primer. Big data is data that is so huge in quantity – and meaning – that you cannot use a simple data processing application, or program, to understand it. There needs to be an analysis of multiple parts, and aspects, that is modeled, understood, taken apart and systematically analysed in multiple dimensions.

There is not really much linearity to big data. Think of a chunk of data as an atom; you analyse it in multiple dimensions, trying to understand not only the intricacy of what it is made up of, but how that functions, and what that means. Analysing that big data then can give you some idea into how that atom, in this case, might behave – whether on its own or interacting with others.

But that’s simplifying it.

Over the centuries, scientists, analysts and more have attempted to analyse the world around us with various scientific methods. In the modern day, with the kind of access we now have to data in its many forms, we can use it to predict things from musical tastes to literal food taste, from something as complex as the intricacies of individual packets of information in network usage to something as ‘simple’ seeming as the mood of a population.

But it isn’t just as simple (or however that may seem) as just collating that information into one place and making a ‘list’. It’s what you construct out of that data – and it is not just mathematics or technical know-how, or the ability to understand linear trends in data.

In fact, true data science is anything but linear. Trends need to be woven together – and in that way, data science is so much like art. It isn’t just up and down, greater than, equal to, or simply explained away in a bar chart, or a line graph. The levels of detail one can get to in data science is far, far more complex – and more intricately explained than just regular analysis.

Regular analysis might simply involve, say, taking numbers themselves and establishing numerical trends. With data science and data analysis, analysts will have to balance both telescopic and microscopic vision, understanding the macro and the micro, with a way to then use that data to extrapolate real-life consequences, results, and discuss any potential changes, if necessary, which need to be implemented.

For example, you could use data science for something like understanding the most minute bits of how, perhaps, an excessive cow population is causing a specific geographical area to experience rapidly the effects of global warming, or something like the growth of food blogging in an 18-25 year old demographic on social media, factoring in the advent of Instagram and monetary models for advertising on that same social media.

The applications are multidisciplinary, diverse, and fascinating! And they’re not restricted to humans anymore.

What is Machine Learning?

You must have heard of, or read the term “machine learning” at some point whilst reading about data.

And you could get some quick idea of what machine learning is from its name – it is a machine learning how to do computational tasks humans do manually – and by manually, I mean program manually into a computer, at least in the modern-day context.

Machine Learning is a significant application of Artificial Intelligence, where a computer learns how to improve upon a specific task without being explicitly programmed to do so. It is essentially that ‘machine’ having access to big data that it can use then to extrapolate information.

Now that too can be either through a supervised algorithm – where, essentially, the machine knows what parameters, or information, it is supposed to be looking for, and then predicts values for itself. Under this, that machine can also adjust those targets.

Then there is unsupervised machine learning, where there is access to data, but there is no specific, explicit trend that machine is to look for. In this case, the machine doesn’t reach a specific ‘answer’ – but figures out itself how it is to get there.

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Some real-life examples

Ever think of the food you order after a tough night at college, or work? For you, it’s as simple as tapping a few buttons on a screen, spending some money, and hey presto, you quickly (traffic notwithstanding!) have a hot meal in front of you.

For the company, it involves many factors. It’s not just about the restaurants as it is about their user base. What is the average age group of users of that app in a particular area, and what would best suit their needs? That aside, there is also continuous – and large-scale – analysis on user preferences, and from restaurant choices to delivery density – every single factor changes. So for example, if an area is IT-heavy, it may likely see a much younger demographic that works later timings.

Data mining – and data science – help factor that data into real-time profits for the company and restaurant, while best trying to figure out how to optimise the users’ experience and tailoring it for the better.

So if you are in that tech window, it’s no coincidence that that app you use suddenly has orders open until 3 a.m. It’s about understanding the need for that that involves data science, economics, and so much more. Fascinating, isn’t it?

Or something even simpler – which isn’t even necessarily that simple. Have you ever tried opening your navigating application on a long, traffic riddled ride home, only to see it glaring red saying “traffic is heavier than usual on your route” and suggesting alternate routes? It computes that say, even if route X from point A to B is 3km, and taking that detour to Route Y will lengthen the distance to, say, 3.5km, you will still get home faster on Route Y because there’s no traffic.

Now imagine all those calculations and computations happening in real-time, while you’re in the drivers’ seat, at the wheel, in the back-seat of your rickshaw, Uber or just walking home.

That’s just a small example of how far and wide data science can go. And it’s just the tip of the iceberg.

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