The growing scope of Data Science applications and how to keep up to date

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It has been so encouraging over recent years to see the growth of the Data Science sector. From the increased range of roles for whom we find candidates, to the increased priority organisations put on these skills. Now I’m noticing a different increasing breadth. A growth in new application areas and new technology opportunities.

Here at MBN we’ve spent over 12 years helping businesses find the analytics and data science talent they need. But, if we thought technology was changing fast a decade ago, the pace has only increased. So, to complement what we’ve shared before about the skills Data Scientists need, let’s consider new applications.

In his ‘Origin of Species’, Darwin wrote:

“It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able to adapt to and to adjust best to the changing environment in which it finds itself.”

I hope being aware of such new applications helps you (and us at MBN) adapt and thrive in a fast-changing world.

Wider applications for Data Scientists with different data

There are far too many examples for my list to paint a complete picture and I’m no expert in this field. But, talking with the hiring managers we support and those we place who go on to success, some themes emerge.

Here are 4 of the newer applications we’ve heard about.

1)    Computer Vision

Beyond using Data Science to model and predict based on existing data, some businesses have branched out into vision processing.

Computer Vision at its simplest is using AI algorithms to teach a machine to see. To extract meaning from pixels. I’ve been told that surprisingly this is analogous to how we humans ‘see’.  Although we may feel like our eyes are just windows on the world, we are experiencing our brain’s interpretation of much simpler visual data. Apparently, we receive data on three channels (red-green, blue-yellow and luminance). It’s the combination of these which our brains translate into what we ‘see’ as colours.

Anyway, enough of me trying to be a scientist. As you can imagine the growing applications of Computer Vision are vast. From autonomous vehicles to medical imaging, smart phone apps to facial recognition in crowds. The latter having had plenty of press coverage lately.

Experts working in this field tell me there is much still to do. As well as the technical challenges, there are ethical and privacy concerns. For instance the risk of ‘Deep Fakes’, manipulated videos that look real, is so great that the US government is investing heavily in being able to spot them.

For more on Computer Vision, see this useful overview from Towards Data Science:

2)    Natural Language Processing (NLP)

If vision is something that most humans are fortunate enough to take for granted, another is surely the ability to communicate. Understanding language can feel like a primary school challenge for humans, but once again has been a tough challenge for machines.

But, here again significant progress has been made. This includes producing more intelligent chat bots and interpreting audio commands from users. Whether you are a fan of Alexa or her competitors, users’ expectations have been raised.

Data Scientists appear to have taken this work a lot further since the early days of Text Mining. I recall that took a number of years to be adopted by businesses. Now it seems that the growth of audio assistants has really raised its importance.

So, many data scientists are now working with audio or transcribed speech data. Another exciting advance beyond the days of just using data from existing databases.

For more on NLP, see this useful overview from Analytics Vidhya:

3) Robotic Process Automation (RPA)

In the mainstream media, one of the common messages about AI and Data Science is how they might threaten our jobs. A picture is painted of how most people’s jobs could be automated. Whereas, in reality much Data Science work has been about just avoiding stupid decisions or interactions with customers.

But the promise of automation remains and has opened up another new application area. Robotic Process Automation (or RPA) is a broad field. I have heard Data Scientists talk about everything from scripts that do little more than record keystrokes, to actual use of robots.

It sounds like an exciting field though. The diversity of solutions needed appears to vary as widely as different business processes. So far it also appears to be focussed on mundane and repetitive work that frees up human operators to take judgement calls when they are needed.

A key advantage of this new application has been financial return on investment. A few leaders have told me that it enabled them to demonstrate cost savings. These ‘quick wins’ then bought them time to invest in more strategic work (like innovative products).

For more on RPA, see this useful overview from Data Science Centrak:

4) Emotion Analytics and Humanising AI

After considering machines as seeing, hearing and moving thanks to advances in Data Science, it might be easy to feel fearful. Memories of too many dystopian movies can leave the impression that this is all heading towards a heartless future.

However, another interesting branch of Data Science, that others have highlighted to me, is working with emotions. Many organisations recognise the power of improved interactions with their customers. Recognising that emotions are a key part of relationships.

Data Scientists are working on moving beyond simple sentiment analysis towards both extracting emotional meaning and generating an appropriate response. There is much work to do in this area, but interestingly it brings together the 3 other applications above. There is some evidence that AI applications can recognise facial emotions quicker than people.

If greater emotional intelligence algorithms can collaborate with both computer vision and NLP, it could offer improved communication. The opportunity for machines to recognise what humans are communicating non-verbally and through emotion. If that can be employed to guide more human-like RPA, we could experience much improved customer experience.

For more on Emotions and Data Science, listen to this interesting interview on MyCustomer:

Four foundations to help you adapt to new opportunities

I am sometimes awed by those who thrive in such a fast-changing world of technology. How do Data Scientists and their leaders do it? What could we learn from them?

Well a few traits appear common amongst those leading the charge in such innovation. Perhaps these are the equivalent of Darwin’s evolutionary adaptations. The changes that allow some Data Scientists to adapt and thrive more quickly.

Four of those tips that made sense to me are:

  1. Read widely
  2. Experiment
  3. Collaborate
  4. Talk with others

Read widely

For each of the above applications, I have shared a different website that I find useful when keeping up to date with such developments. Fortunately, these sites also share a growing number of books, hubs, podcasts and other resources that can help us keep up to date with innovations. Do you protect time each week to read or listen to these?


Like me, you have probably lost count of how many times you have heard a speaker at a Data Science conference talk about “failing fast”. It has been over-hyped. However, the most successful Data Science leaders I know embrace that ethos. Their teams discover and master new application areas through the freedom to test and learn.


We’ve shared before on the importance of building and maintaining a network as a Data Scientist. I’ve also heard this point reiterated by Data Science leaders. When spotting a potential new application area, there is often too much unknown to tackle it alone. Different options may include collaborating with academia, technology providers, existing suppliers or even just other parts of your own business. Worth investigating what others already know.

Talk with Others

As the oft quoted old BT advert said, “it’s good to talk”. Have you discovered the benefit of chatting about your application areas or even just ideas with others working in Data Science? If not, then read on, I recommend searching out a local event or “meet up”.

Data Science meetups as an enabler

I hope those thoughts have helped you. At MBN we want to encourage the wider Data Science community to keep developing.

As part of that commitment, we regularly host a range of both themed events and meet-ups. For instance, the Scotland Data Science & Technology Meetup. There is plenty of chat about new application areas and challenge there, with shared knowledge and collaboration as a result.

We’d also love to hear your experience. Are you discovering new application areas for Data Science? If so, please do share the resources that have helped you, or that you offer to help others.

What will be the new Data Science application areas in 2020 and beyond?