Career Paths to Data Science
What are the different entry routes for a career in data science?
The role of a data scientist requires a broad set of skills. Ideally you need a sound knowledge of maths and statistics. Good programming skills in at least one language. A solid understanding of machine and deep learning, and ideally some domain knowledge, and/or business acumen.
In the following article I am going to describe a number of different routes to move into a data science career. This post will focus on an applied data science role within a business or other large scale enterprise, as opposed to something like a research data science role.
Data analyst to data scientist
I am going to be biased and list out my own personal route first. I transitioned over the period of about three years from a data analyst role to data scientist. Having spent time in a data analyst role you will already have a really good foundation of understanding around data. How to extract it, clean it, some of the gotchas that you may experience when pulling data together for reporting such as missing or erroneous values. You will also gather an understanding of how to extract insights from data and develop a high level of data curiosity.
To transition from data analyst to data scientist you will need to develop good programming skills in at least one language. My personal opinion here is that if you are just starting out in programming then python would be the best one to learn. In a poll by KDNuggets Analytics in 2018, 65.6% of data scientists surveyed selected python as a tool that they used, up 11% on the same survey carried out in 2017. So python is currently the most popular language for data scientists and this popularity is continuing to grow. Python is highly accessible and fairly easy to learn for beginners in programming. It also has a very active community around it in the data science space, examples include NumFocus and Pydata.
Chances are that, unless you have already have a highly numerate degree, you will also need to brush up on your knowledge of maths and statistics. You will need to have an enough of an understanding to perform complex data analyses, and have a reasonable understanding of what is going on behind machine learning algorithms for example.
Graduate degree in maths/engineering/computer science
Chances are that if you have an advanced degree in one of these subjects you will already have a lot of the required skills for data science. You might need to learn programming if you don’t already have this, or more maths and statistics if your degree was computer science based for example.
The learning curve here is likely to be adopting typical business practices such as writing reproducible code, version control and corporate working methodologies e.g. Agile. Developing domain knowledge of the industry you are working within and interpreting, sometimes vague, business questions into useful projects.
Software engineering
I personally think this a great role to transition from. From my experience, data science teams often lack the knowledge of software development best practices, and being able to write the most efficient, production ready code. If you have already spent time working as a software engineer, then the chances are you will have a solid foundation in the sort of programming that data science teams often lack.
For a transition here you may need to learn one of the popular data science programming languages — as mentioned previously probably python, as well as SQL. You would need to develop a solid understanding of data, extracting, cleansing, processing and analysing. A foundational knowledge of maths and statistics, especially as applied to data analysis and machine/deep learning, is also needed.
Other routes
I am hugely passionate about diversity in tech and I believe very much that this also includes diversity of backgrounds. Not coming from a “traditional” route can have its advantages, your experience or outlook on life may give a fresh perspective on processes or problems within a team. I have listed three of the most common routes for entering a career in data science in this article, however there are of course many more. If you don’t have a relevant degree, or experience in data, or programming, I don’t believe that this is a barrier, it just means that it may take you a little longer to get there.
