Data Science: A Load of Hype?

Inspired by our recently-posted interview with a brilliant data scientist, we’re opening up the manual on data science to discuss: is it more than just hype? …

Whether you’re a techno-nerd or a techno-phobe, there’s no denying that data science is the technological tidal wave of our times. Many of us are familiar with the term and have a vague sense of what it entails. Sort of. So, let’s take a data science tour to understand why it’s considered today’s tomorrow.

Firstly, let’s break it down. Data science is a dynamic field of study, which merges the following disciplines:

  • Data mining
  • Statistics
  • Machine learning
  • Analytics
  • Programming

What is it and why do we need it?

A simple definition can be found at investopedia.com, but in a nutshell, data science provides meaningful information based on complex data that’s collated, analysed and interpreted for decision-making purposes. One of its integral and vitally important aims is to gain insight into human social and economic behaviour, and design new solutions for the future.

Data is drawn from a range of sectors and channels, including mobile phones, Internet surveys and searches, social media sites, etc. Gone are the days where we use websites as a one-dimensional book – nowadays they are hosts of information where knowledge grows via its users. If we take a look at websites such as Facebook and YouTube, they are so-called platforms for good reason: they’re a dynamic and interactive means of communicating through sharing. Online browsing is no longer anonymous; we are encouraged to share by uploading, liking, commenting, friending, favourit-ing…thereby leaving our footprint on the digital landscape we call Internet.

“We are encouraged to share…thereby leaving our footprint on the digital landscape we call Internet.”

This ‘footprint’ forms the data that’s collated and analysed, so our online choices (also known as trends and behaviours) can be interpreted to maximise the user experience. And considering how many websites are out there, and the multiple millions of people using them – that’s a lot of data. In 2010, the traditional method of processing was shifted up a gear, which is where ‘big data’ came into the equation, opening up a Pandora’s Box of possibilities in finding data insights.

Perhaps the more well-known areas of data science refer to machine learning, deep learning, and artificial intelligence. You may be thinking that these are the same, but it’s a common misconception. Allow these guys at SAS to enlighten you, whose mission statement is “To transform a world of data into a world of intelligence”. There are endless ‘machines vs humans’ debates on a growing number of traditionally manual roles, but machine learning was initially inspired by the notion to improve the way we live and how we experience the world around us (although this in itself is debated). Love it or loathe it, this technology is constantly progressing with time.

The brains behind data science

Many of us have this idea of a computer genius glued to their monitor, crunching numbers and munging the heck out of data all day and night. Or, we imagine that their data-diet consists purely of data analytics with a side of statistics, washed down with a glass of algorithms. But there’s more to a data scientist than simply numbers: they interpret these numbers in order to problem-solve and strategise. They apply their analytical-based knowledge to advise and guide a business to perform to its full potential. It puts a whole new spin on ‘do the math’.

“There’s more to a data scientist than simply numbers…they problem-solve and strategise…they guide a business to perform to its full potential.”

As an example, the resume of a data scientist may look a little like this:

  • Undertaking data collection, pre-processing and analysis
  • Identifying valuable data sources and automate collection processes
  • Creating models to address business problems
  • Presenting information using data visualisation techniques
  • Analyse large amounts of information to discover trends and patterns
  • Build predictive models and machine-learning algorithms
  • Propose solutions and strategies to business challenges

So, with creating, building, analysing, proposing, they’re sort of artists and scientists. Nerdy, smart, cool – however you label it, their contribution to big data is undoubtedly a big part of our lives (despite most humans not understanding ‘munging’). That’s why the career of a data scientist has been ranked as the third best job in America for 2020, and apparently the “sexiest job of the 21st century” according to a Harvard Business Review article. And also why more and more companies have a data scientist on board, to help improve their business model and drive it forward. Configuring tomorrow’s world is no easy feat, but for these guys, it’s all in a day’s work…

Looking for a career in data science or similar? Click on iET’s webpage to discover Data Science related jobs.

Written by Nadia Danaos

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