[Image: York city centre]
Caption: A view of York city centre.
When I started my degree at the University of York, I never imagined that I would end up applying for and being accepted onto an MSc in Data Science at a Russell Group university. I began my studies with a BA in English Language and Linguistics, before transferring to BSc Linguistics in my second year.
In this blog, I want to share how someone with no formal mathematical background was able to make this transition into Data Science in my experience. I’ll talk about the choices I made during my degree, including the modules I selected and the extra work I did alongside my studies. If you’re currently studying a non-technical subject but are interested in a more technical career, my experience shows that it can be possible to apply for a Master’s in a different field without repeating your undergraduate degree.
Why I transferred to BSc Linguistics
At the end of my first year, I realised that I was more interested in modules that required logical thinking and structured analysis. After speaking with my academic supervisor, I learned that it was possible to transfer from BA English Language and Linguistics to BSc Linguistics.
Luckily, the first-year modules for both courses were very similar, so I didn’t need to take any additional modules or repeat a year. In my experience, the transfer process was much smoother than I expected, and asking for advice early made a big difference.
This decision changed how I thought about my future. Studying BSc Linguistics encouraged me to explore career paths that involved more analysis, problem-solving, and technical thinking.
Discovering Data Science through linguistics
My interest in Data Science began through one particular module, Linguistics as Data Science. This module introduced me to working with real datasets using R.
At first, I found it challenging. I had never coded before, and learning how to clean data, run statistical tests, and visualise results took time and patience. However, as the module progressed, I started to enjoy working through problems and seeing patterns emerge from the data.
This module completely changed how I saw linguistics. Language became something that could be analysed as data rather than just discussed theoretically. I also realised how flexible Data Science is, and how similar analytical skills can be applied across many different fields. This was the moment I began to seriously consider Data Science as a future career.
[Image: Coding at my favourite café]
Alt text: coding in one of my favourite cafes in York.
How I prepared for my Master’s applications
When I decided to apply for a Master’s in Data Science, the first thing I did was check the entry requirements for each university. In the UK, many universities offer Data Science conversion programmes, which are often designed to welcome students from non-technical backgrounds, if you’re eligible.
I also noticed that Data Science programmes are offered by a wide range of departments, including Information or Computing-related departments, not only Engineering or Mathematics. In my experience, some of these programmes have slightly more flexible mathematical entry requirements. Keeping this in mind helped me decide which universities to apply to.
In my third year, I was very intentional about my module choices. I tried to select modules that involved analytical thinking, statistics, and programming wherever possible. At the University of York, the Linguistics programme offers modules such as Linguistic Computation and Corpus Linguistics, which focus on researching language using real datasets. Having access to these data-driven modules felt like a real strength of studying Linguistics at York.
Alongside my degree, I also worked on strengthening my skills independently. I completed online mathematics courses on Coursera, including linear algebra, calculus, and probability. I also worked on small data analysis projects and took part in online modules on artificial intelligence concepts from the University of Oxford. In my experience, showing motivation and evidence of self-directed learning was just as important as meeting the formal entry requirements.
Conclusion
Data Science is a broad and flexible field, and it can connect with many different subjects. If you’re worried that you’re “too late” because you didn’t start in a technical degree, I don’t think that has to be the case. Coming from a different academic background can give you a unique and valuable perspective.
For me, studying BSc Linguistics at the University of York helped me build analytical thinking, research skills, and the confidence to change direction. York also gave me the space and support to explore my interests, ask questions, and seek advice when I needed to. Looking back, I’m very grateful for the opportunities my degree offered me.
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