Life as a Data Science MSc Student
Written by Alex Otway, an MSc Data Science student.
When I came to Manchester three and a half years ago to study Politics and International Relations, I didn’t imagine I’d end up studying for a Master’s in Data Science, learning to code in multiple languages and taking modules in Machine Learning. This kind of stuff seemed like something ‘STEM’ people did, whereas I was more interested in understanding and engaging in debates about the social world.
But when I had my compulsory modules in research methods, I realised I actually quite enjoyed exploring data. I could understand the basic stats concepts – in fact, anyone can. And I really enjoyed making graphs and maps that told a story. I wasn’t just a ‘humanities’ person, and there is no need for anyone to put themselves in that sort of a box. And after some encouragement from some great tutors, and the brilliant opportunity of a Q-Step internship, I had heard of this MSc and decided I could make the jump.
But this was still a big step away from the kind of things I’m learning in Data Science, like building predictive models in Python for business problems. And on this course, I’m often working in groups with people who did all sorts of undergraduate degrees, from Maths or Computer Science to Geography or various Social Sciences. Many have had previous careers too. That’s one of the great and unique things about this course: it’s one of the most diverse, multidisciplinary courses out there, so everyone brings different skills and perspectives.
The course is designed across six different pathways currently: Business and Management; Computer Science Data Informatics; Environmental Analytics; Applied Urban Analytics; Mathematics; and Social Analytics, which is my pathway. Across these, you have compulsory and elective modules both for the whole course and for each pathway. And, of course, everyone writes a 60-credit dissertation at the end, which is pathway-specific and likely to be related your own discipline. If you haven’t coded in Python before, there’s a compulsory module at the start of Semester 1. It’s tough but incredibly rewarding – you’ll be amazed by how quickly you can learn something like this.
What the course ultimately allows you to do, is learn increasingly valuable skills, and apply them to what you’re interested in. So, for my dissertation, I’ll be using techniques I’d never heard of a few weeks ago, in a sub-field of Politics or Political Economy that I’m interested in.
There’s a fair amount of group work involved in Semester 1. A lot of people are hesitant about group coursework, but it’s one of the things I’ve enjoyed most. You learn a lot from each other, not least because everyone’s done different degrees before. It’s also good practice for the professional world, and a typical group coursework project is that you’re faced with a business problem that you’d be in a team to work on. And ultimately, there’s no other way to learn these sorts of skills and get these sorts of projects done. The majority of your assessment is still individual though, and assessment methods are much more diverse than my undergraduate degree: you’ll have a mix of reports, essays, presentations, tests, exams and more.
We’re lucky this year, with the course growing in popularity, to have better resources including our own co-lab space which is the home of all the Data Science practical classes. The co-lab is open for just us to work in, so we never have to fight for a room in the library to do our group work. It also means you get to know your course mates quicker, which is important for a Master’s.
If you come from a STEM background and have any maths or coding background, certainly think about going for this course, because you will end up fully employable in one of the most sought-after, growing, and financially rewarded professions. If, like me, you come from a qualitative or humanities background, then the same applies – you just have to be prepared for a steeper learning curve at the start. But with Manchester being one of the most well-resourced universities and research-intensive in the world, there’s always help available, and the challenging nature of the course will be worth it.