No doubt over the last few years, you’ve seen dozens of infographics telling you what data scientists do on a day to day basis – writing and implementing models (and half the time SQL!), talking to stakeholders etc. but how do you pick one of these aspects of data science to specialise in?

Firstly, why even specialise? It’s not required, you don’t have to, but it adds an element to your story that can act as an important layer of your capability. As more people start to come into data science, a specialisation becomes a way to clearly define your abilities and what areas you work in. 

For this post, I don’t want to spell out the merits of each type of specialisation, but get you thinking about the story within your own career and what you want to tell moving forward. A key thing to mention is the specialisation you choose doesn’t need to be specifically in data science, there are no rules with picking one! As an example of this, you could be a data scientist trained in project management, or a data science specialising in NLP. Both are equally as valid but imply totally different career paths. Specialising in project management, for example, leads you down a track of becoming a manager and managing data science teams, as opposed to being technically focused as a subject matter expert in NLP. 

What a specialisation indicates is not a method of approach, but a classification of the outcome.

The five core areas for specialisation are often broken up into; Deep Learning (DL), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), and Non-DS specialisations. Now you might be saying that there is a lot of crossover between those areas, you can use deep learning in NLP, you can use machine learning in machine computer vision, and yes you would be 100% correct. 

What a specialisation indicates is not a method of approach, but a classification of the outcome. Using NLP as an example, you can use a number of different statistical, ML, and DL methods to achieve NLP outcomes, the primary focus of your role is to employ what’s required for those outcomes. Thinking about the intent of the work you want to work on will help to determine where you should follow. Some questions that can be used to spur your thinking:

  • What outcomes do I tend to focus on?
  • What provides the most business value from my experience? 
  • Am I excited about the field and contribution I could make? 
  • Can I articulate the value of the specialisation to someone outside of data science?

Using these questions can help you figure out where you want to specialise, as the wider field of data science ever increases, having an extra dimension to your role will help to progress you further. As data science becomes more integrated with business, using clear and understandable language will be a key part of marketing yourself to the right businesses.

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