This pathway is the hardest to provide a clear answer, as it can be so varied depending on the context and situation in which people are transitioning. Many people that are in Data Science today started their careers in other places, some related and some totally different. If you are already in a technical career, it may be as simple as reskilling and finding an opportunity, and if you are in a completely different line of work you may need to work on the fundamentals before securing a role. Whatever the case, here are a few key things to consider if you want to transfer your career to data science:

Many people that are in Data Science today started their careers in other places, some related and some totally different.

Showing aptitude

When transitioning either internally or externally with other companies, you have to illustrate that you are capable of the job you are transitioning to. As transitions don’t often take you to the lowest rung in the new role (sometimes they can), you need to be able to clearly articulate and demonstrate your abilities in this space. This comes from having a firm grasp on algorithms (a good one to be able to explain is a gradient boosted decision tree), as well as how to apply them in a business context. Keep in mind your current career is your advantage over people entering the workforce for the first time, and being able to articulate how it helps you perform in your role as a data scientist is a great advantage. 

Use your current career as an advantage

Using your current role to illustrate how that helps you have a deeper understanding, or wider view of data science and AI applications can help. Some examples of how this can be done:

  • If you have been a Software Engineer, you deeply understand writing good production-level code, and how to apply those principles and standards to the code you write as a Data Scientist
  • If you have been a User Experience Designer, your ability to understand the pains of the user and clearly articulate problem statements from varied data is an advantage in creating models that solve real business problems with an effect on users 

Show learning and progress toward AI

Have a look at the courses and study that is listed in the second pathway, when transitioning it is critical to show learning toward expanding your skillset in AI. Let’s say you have an engineering degree, so you might take a course to brush up on your statistics in R, and then a course on implementing machine learning models. All these courses show that you are quantifying and solidifying your knowledge of AI, explaining the study you did as well as how it helped your understanding can show the progress in learning. 

Showing projects

Having an active Github or Kaggle account that you can link a recruiter to is a massive advantage, and will be one of the first things that are assessed by those evaluating your future role. Consider using tutorials and other projects as a source for this (but clearly label them as tutorials and not your original work, as to not be disingenuous). Have a look at the ‘Training’ section on this blog, which covers the best ways to get started in these types of projects. 


As you can see from these pathways, there are many paths and methods to get into data science, and that is part of what makes the field fantastic. While you are developing yourself to enter the field of data science it is worth considering what you would like to specialise in, and how to market yourself for that (check out this post for more). I hope this series of articles helps you to understand the ways you can get into Data Science, and potentially offer alternatives to the standard traditional pathways. 

For the other parts of this article where we dive into the other pathways, have a look at the links below!

Pathway 1 – University & Formal Study 

Pathway 2 – Courses, Bootcamps, and Self Study 

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