{"id":4267,"date":"2023-01-04T11:15:39","date_gmt":"2023-01-04T11:15:39","guid":{"rendered":"https:\/\/sites.manchester.ac.uk\/humanities-blog\/?p=4267"},"modified":"2023-01-04T11:15:39","modified_gmt":"2023-01-04T11:15:39","slug":"life-as-a-data-science-msc-student","status":"publish","type":"post","link":"https:\/\/sites.manchester.ac.uk\/humanities-blog\/2023\/01\/04\/life-as-a-data-science-msc-student\/","title":{"rendered":"Life as a Data Science MSc Student"},"content":{"rendered":"

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Written by Alex Otway, an MSc Data Science student.<\/strong><\/p>\n

When I came to Manchester three and a half years ago to study Politics and International Relations, I didn\u2019t imagine I\u2019d end up studying for a Master\u2019s in Data Science, learning to code in multiple languages and taking modules in Machine Learning. This kind of stuff seemed like something \u2018STEM\u2019 people did, whereas I was more interested in understanding and engaging in debates about the social world.<\/p>\n

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 \u2013 in fact, anyone can. And I really enjoyed making graphs and maps that told a story. I wasn\u2019t just a \u2018humanities\u2019 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.<\/p>\n

But this was still a big step away from the kind of things I\u2019m learning in Data Science, like building predictive models in Python for business problems. And on this course, I\u2019m 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\u2019s one of the great and unique things about this course: it\u2019s one of the most diverse, multidisciplinary courses out there, so everyone brings different skills and perspectives.<\/p>\n

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\u2019t coded in Python before, there\u2019s a compulsory module at the start of Semester 1. It\u2019s tough but incredibly rewarding \u2013 you\u2019ll be amazed by how quickly you can learn something like this.<\/p>\n

What the course ultimately allows you to do, is learn increasingly valuable skills, and apply them to what you\u2019re interested in. So, for my dissertation, I\u2019ll be using techniques I\u2019d never heard of a few weeks ago, in a sub-field of Politics or Political Economy that I\u2019m interested in.<\/p>\n

There\u2019s a fair amount of group work involved in Semester 1. A lot of people are hesitant about group coursework, but it\u2019s one of the things I\u2019ve enjoyed most. You learn a lot from each other, not least because everyone\u2019s done different degrees before. It\u2019s also good practice for the professional world, and a typical group coursework project is that you\u2019re faced with a business problem that you\u2019d be in a team to work on. And ultimately, there\u2019s 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\u2019ll have a mix of reports, essays, presentations, tests, exams and more.<\/p>\n

We\u2019re 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\u2019s.<\/p>\n

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 \u2013 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\u2019s always help available, and the challenging nature of the course will be worth it. <\/p>\n

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