How I Got Into My Dream M.Sc Data Science Program

From a Non-Programming Background to making it to LSE.

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The Goal

I always wanted to pursue my Master’s degree from one of the top universities in the UK. Data Science was most definitely my course of choice. However, coming from a non-programming background, it was quite challenging to stand out among the numerous computer science grads applying for the same course. However, I got into my dream program, and pretty much all of my backup options as well! Here’s my journey-

My Background

• Undergraduate Degree: B.Sc. (Hons) Mathematics
• College: St. Stephen’s College, University of Delhi, India
• GPA: I applied with my interim transcript when I had finished my second year, and my CGPA at that time was 9.93/10, that’s predicting a First Class degree when I actually graduate.
• Strong Points: My math degree meant I had completed multiple courses in Linear Algebra, Probability and Statistics, which are important prerequisites for a Data Science degree. I had already done some projects on Data Science, which I could talk about in my SOP, and use those to prove that I was interested in the degree!
• Weak Points: No formal experience in Python programming. Just a few introductory-level courses in programming that I took in college- that too, in C++

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How I Picked Colleges

Obviously, college ranking mattered! But what mattered the most to me was the course content. I wanted to study Data Science with a strong focus on Machine Learning and Data Visualisation, and I wanted to finish my degree with a little bit of practical experience. Colleges that prioritised project work and real-world use cases were definitely more attractive than those that put more emphasis on dissertations and written exams!

I had narrowed it down to a few colleges that I would want to go — LSE, UCL, King’s College London, University of Warwick, University of Bristol, and the University of Birmingham.

The Master’s degree at LSE came with a “Capstone Project”, that allows students to work on real-world data, compared to more theoretical dissertation options at other universities. So this was definitely my top choice! However, any college in London (LSE, UCL, and KCL) had an upper hand due to the vast opportunities that come with being a student in London.

My SOP

I had always been good at academics, so I tried to make my SOP more about my academic achievements and personal projects. Coming from a non-programming background meant that I really had to prove to the colleges that I could sustain in a CS heavy course like Data Science.

I had already started preparing for my SOP months before the applications open. I planned to apply in September-October, but I started working on my SOP in June. I realised how good personal projects and research work would look in the SOP, so I started making small projects and publishing articles relating to data science on Medium!

Since different colleges have varying word limits — I tried the following approach : —

• 500 Word Limit: Short summary about my introduction to data science, a bit about my research work and personal projects, why I chose that particular college, and finally a paragraph about what makes me a good candidate (basically my strengths, achievements and plus points that my CV cant explain)
• 1000 Word Limit: Same points as the 500 word SOP, but here I dived deeper into my interests, projects and achievements. I also explained any volunteer work/ extra curricular activities and explained in detail how my degree in math has prepared me for a successful transition to data science.

My CV

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I was applying for a Master’s degree while still an undergraduate student, so I didn’t have any formal work experience. But that isn’t a prerequisite for M.Sc courses at LSE. Here’s what I included in my CV and made sure it complemented my SOP in all ways possible:

• Educational Achievements: Any awards I’ve won for academic excellence, what modules I was focusing in my Undergraduate degree (mentioning those that were relevant to Data Science, like Linear Algebra and Statistics), any extracurricular achievements in academia, like Olympiads and Exams I had given!
• Internships: I had a few internships (not many, just 1 or 2) and these weren’t exactly Data Science specific, they were more into marketing, copywriting, or content management. But I included them to show my work ethic and that I like to try new things!
• Volunteering: Always include any volunteering work you may have done in your undergrad. This can be working for societies, groups, events or charity work. You want to show the university you can handle course work along with other things and manage your time well!
• Projects: This is where I included any Data Science projects I had done, and made sure to add links to any Data Science blogs I had written, GitHub repositories to my code or even mentioned the names of courses I had taken that were relevant to Data Science. This makes sure your CV is relevant to the course you’re applying to and shows that you have genuine interest.
• Skills: All technical skills, including languages like Python, R, C++. As well as any other skills such as HTML, web design, or if you’ve been studying about ML algorithms, you can mention the algorithms you already know about!

LORs

I made sure to contact my professors from my undergraduate degrees very early, and chose professors that had taught me modules that were more relevant to the course I was applying to. I also sent over my CV and SOP to them so that they have an idea about the skills they should emphasise on when writing a Letter of Recommendation for me.

I made sure to reach out about 3 months in advance, and reminded them regularly to make sure my references were submitted early and all my applications can be completed in time!

Final Thoughts

Since I had submitted all my applications well before the due dates, the decisions came in fast and I could make a more informed choice about where I finally wanted to go! I could also start any preparations like booking accommodations, visa processes, and formalities well before time!

In the end, I was extremely happy with my choice of going ahead with LSE and quite happy I got into my preferred program. It’s been an amazing journey ever since :)