Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!
I am a recruiter specialised in the field of data science. The idea for this project arose because one of the most common questions I am asked is: “how do I obtain a position as a data scientist?” It is not just the regularity of this question that got my attention, but also the diverse backgrounds from where it was coming from. To name a few, I have had this conversation with: software engineers, database developers, data architects, actuaries, mathematicians, academics (of various disciplines), biologists, astronomers, theoretical physicists – I could go on. And through these conversations, it has become apparent that there is a huge amount of misinformation out there, which has left people confused about what they need to do, in order to break into this field.
I decided, therefore, that I would investigate this subject to cut through the BS and provide a useful starting point for anyone looking to move into commercial data science – whether you are just starting out, or already possess all the necessary skills but have no industry experience. And so I set out with the aim of answering two very broad questions:
Why am I qualified to write this? Well, I speak with data scientists every day and to be an effective recruiter, I need to understand career paths, what makes a good data scientist, and what employers look for when hiring. So I already possess some knowledge on the matter. But I also wanted to find out directly from those who have trodden this path, so I began speaking with data scientists of different backgrounds to see what I could unearth. And this took me on a journey through ex-software engineers, an ex-astrophysicist and even an ex-particle physicist, who – to my excitement – had worked on the discovery of the Higgs boson.
CHAPTER ONE: WHAT IS DATA SCIENCE? Different Types of Data Science
So you have made the decision to become a data scientist. Great, you are on your way. But now you have another choice, which is: what kind of data scientist do you want to become? Because – it is important to acknowledge – while data science as a profession has been recognised for a number of years now, there still isn’t a commonly accepted definition of what it actually is.
In reality, the term ‘data scientist’ is regarded as a broad job title and so it comes in many forms, with the specific demands dependent on the industry, the business, and the purpose/output of the role in question. As a result, certain skillsets suit certain positions better than others, and this is why the path to data science is not uniform and can be via a diverse range of fields such as statistics, computer science and other scientific disciplines.
The purpose is the biggest factor that dictates what form data science takes, and this is related to the Type A-Type B classification that has emerged (see here: What is Data Science?). Broadly speaking, the categorisation can be summarised as:
We may see further evolution of these definitions as the field matures, but for now, we will continue this exploration with a look at the ‘science’ in data science.
Owning Up To The Title
All scientists work with data, so in a sense all scientists are data scientists.