Student experience

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From: UK 

Currently working: as a statistician at GSK 

I studied Mathematics with Finance at undergraduate level at Newcastle University. I enjoyed (almost) all parts of my degree, but statistics certainly captured my interest most. Although I’d learnt a lot, I knew long before finishing my undergraduate degree that I wanted to pursue statistics further. Before starting this MSc, I

undertook a Statistics and Operational Research internship at the STOR-I Doctoral Training Centre at Lancaster University. Here, I learnt about Operational Research for the first time and found it really interesting; particularly a project on Resource Optimisation for the NHS excited me about the subject. Studying an MSc in Statistics and Operational Research, in an amazing city like Edinburgh, was the perfect option for me. 

Looking back at the MSc, I can’t believe how much I learnt in one year. There was a wide range of courses on offer, so what I learnt always felt varied and tailored to my interests. The program was very intense, with almost non-stop assessments and coursework, but this massively developed my time-management and teamworking, as well as my technical skills. For me, the most enjoyable part of the year was my dissertation. The dissertation topics on offer were great, and many have the option to collaborate with an industrial partner. My dissertation was on “Factors Affecting Cancer Survival”, in collaboration with Public Health Scotland. This allowed me to apply what I’d learnt in the year to investigate a real problem. The structure of the MSc meant that I only had my dissertation to work on during the final term, this was something I really valued and allowed me to investigate my topic in substantial depth. Furthermore, I am still collaborating with my dissertation supervisors to produce a paper on my dissertation topic. 

Now I am working as a statistician at GSK, a job that I am really enjoying and for which my MSc has been invaluable. I have always been interested in public health and medical statistics, something that was only emphasised during my MSc. I often use the statistics and programming skills, as well as collaborative, problem-solving and communication skills that I learnt and developed at Edinburgh. The MSc was thoroughly enjoyable and so useful for my career, overall, I could not recommend it enough.

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From: UK

Currently working: as a Data Scientist/Researcher for a health economics consultancy

After completing a bachelors in mathematics and then working as a data analyst for two years, I became increasingly interested in statistical modelling and data science. Therefore, I decided to enrol on the MSc Statistics and Operational Research course to expand my knowledge and allow me to pursue a career in data science/statistics.

It has been an extremely challenging year. The course has definitely pushed me but I have learnt a huge amount in a very short space of time. It was great to be taught by professors who are leading researchers in their field and to obtain knowledge in such a broad range of subject areas. I’ve met some amazing people who have been a great support throughout and who have become life-long friends.

There’s no doubt that the course from a uni with such a great reputation has opened many doors and allowed me to pursue the career that I always wanted. Plus, Edinburgh is a beautiful place to live and somewhere that will always feel like home.

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From: Japan

Currently working: on a PhD in Optimization and OR at the University of Edinburgh

After finishing my undergraduate degree in Systems Engineering, I wanted to develop some more analytical skills, and I decided to pursue a MSc in SOR at the University of Edinburgh. Through this intensive one-year program, I found that SOR was an ideal place to study both statistics and mathematics.

First, the atmosphere in this course was very nice and cozy. Students gathered from all over the world (like me), and created a stimulating social environment for each other both inside and outside of the university. All the faculty members were also quite supportive and approachable. Additionally, a carefully designed curriculum met my broad interests and enhanced my knowledge effectively. I wanted to learn theory while also seeking practical skills. I also thought that combining statistics and mathematics would be a great advantage in academic and industrial fields. This program provided a wide variety of optional courses, and thereby provided opportunities to pursue my specific academic goals perfectly.

The MSc in SOR at the University of Edinburgh definitely broadens its students' horizons. If you want to develop analytical skills in statistics with a strong background in theory, this is a suitable program. The master degree at the University of Edinburgh will be a great asset in your life, which will ensure your future success.

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From: Portugal

The MSc in Statistics and Operational Research (SOR), at the University of Edinburgh, proved to be the right investment for me, both with respect to the acquisition of technical concepts and as a life experience. You will get training in theoretical contents, but you will also be able to learn how to transfer this background knowledge into real world applications. The quality and support of the teaching body and the positive attitude amongst students, where the class is like a family, provides an excellent working environment.

The MSc in SOR will always be an excellent option for those who want a high level of theoretical background with objective training on the application of theory to the real world. Moreover, holding an MSc in SOR you will be well placed in the market, with potential opportunities in a wide range of areas.

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From: Poland 

Currently working: as a Machine Learning Research Engineer

During my Bachelor's, I became interested in Machine Learning and the computational aspects of Statistics. I have thus decided to enrol in the Statistics with Data Science programme to learn more about the statistical theory underlying modern Data Science. The course offers a very diverse selection of modules, ra

nging from highly theoretical to more applied ones, allowing students to tailor their degree and gain proficiency both in rigorous mathematics as well as programming and practical data analysis. We were also given the opportunity to enrol in Machine Learning-related modules from the Informatics department, which perfectly complemented the courses offered by the School of Maths. 

I feel the programme has helped me to supplement and greatly expand the knowledge I built during my Bachelor's. I am currently working as a Machine Learning Research Engineer and need to read research papers on a daily basis. The familiarity with Probability and Statistics I acquired during my Master’s degree allows me to quickly grasp new concepts and understand the reasoning behind novel data-driven algorithms. I believe this skill is incredibly vital in such a rapidly developing field as Data Science. 

Lastly, I would like to mention that Edinburgh is the most beautiful city I have ever lived in. The Maths campus itself is located just five minutes away from the magnificent Blackford hill towering above the city, surrounded by stunning nature and wildlife. The atmosphere on the campus is very cosy and welcoming. During my one year here, I was able to build strong, life-lasting friendships with amazing people from all around the world. I cherish the time spent as a student at the University of Edinburgh and I do deeply believe I will always consider it one of the best experiences of my life. 

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From: Sweden 

Currently working: Quantitative Risk Analyst at ABN AMRO Bank in Amsterdam 

Before coming to Edinburgh, I studied mathematics at an undergraduate level and became interested in probability and statistics as well as machine learning. The MSc Statistics with Data Science at the University of Edinburgh therefore seemed to suit me perfectly. 

As the name suggests, the programme lets you delve deep into mathematical statistics but also explore trendy topics in data science. While preparing for an exam with challenging problems and results to be proved on e.g. Markov chains or ARMA models, I would in the same weeks look at transformer net

works as part of some coursework in the School of Informatics. In between all that, I also learned to appreciate the differences between frequentist and Bayesian statistics. 

Compared to my undergraduate studies which were almost entirely assessed by exams at the end of each module, this MSc put more weight on coursework. While it kept me very busy throughout the semesters, it taught me to manage my time better and get things done before deadlines. Moreover, projects and assignments offered plenty of experience with both R and Python in modules which were not necessarily focused on programming. 

Now, a few months into my first job after graduation, I already find that all parts of the curriculum has offered me something which I apply today. I would also emphasise the importance of the way I was taught to communicate my analyses throughout the MSc, particularly during the dissertation. It is not enough to simply fit a good model and print some diagnostics. What I do in my job today has no real meaning in practice unless it can be explained in a simple and clear manner so that others can understand. 

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From: Portugal

After completing a first master’s in economics and working briefly in that field, I became interested in statistics and its applications to the life sciences. I enrolled in the MSc in Statistics with Data Science with the goal of receiving solid training in modern statistics and machine learning to prepare me for this change in career path.

From the first day, I found the atmosphere at the School of Mathematics to be friendly and welcoming. Induction events allowed us to meet other students as well as the staff in an informal setting, and throughout the year the faculty were always very approachable.The programme provided a variety of core courses in statistics, with an emphasis on practical applications and programming. Through the different assignments and projects I gained substantial experience in data analysis, and I feel that this hands-on side of the master’s was one of its most valuable components.

A good selection of optional courses was also made available, including relevant courses from other departments. I particularly enjoyed taking machine learning courses at the School of Informatics and I believe that the opportunity of studying in these two departments (Mathematics and Informatics) simultaneously sets this programme apart from other postgraduate degrees in statistics.

To conclude, the extensive training in statistics and data science that I received as part of this master’s, combined with the great reputation of the university, has opened doors to many exciting and varied opportunities, both professional and for further study, and all this while living in the beautiful, vibrant city of Edinburgh!

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From: Germany

After finishing my undergraduate degree in economics and an internship as a data scientist I realized the MSc in Statistics with Data Science would be the right choice for me. It gave me the opportunityto get a more solid and formal background in statistics while only lasting one year.

I particularly enjoyed that we were able to select courses from multiple schools as my two favourite courses turned out to be Machine Learning and Pattern Recognition offered by the School of Informatics as well as Genetic Epidemiology offered by the Medical School. I also enjoyed the good mix between theoretical content and more practical skills (e.g. the Python course) and multiple courses were based on Assignments during the semester which were often more open ended and enjoyable than exams at the end of the semester.

The support from the university and professors is very good, almost all lectures were recorded so that we could watch them again in our own pace and there were lots of opportunities to ask questions during tutorials. Moreover, the career service is great giving lots of support by organising events and individual meetings.

Finally, Edinburgh is a great city with amazing pubs which offer plenty of opportunities to meet other students from all over the world.

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From: Jamaica

One of the things I enjoyed most about studying Statistics with Data Science at the University of Edinburgh was the mix of statistical theory and applications which gave me knowledge useful both in a professional setting and in an academic environment for even higher studies. I appreciated that the course options allowed us to study aspects of data science, at a level that matched our computer science experience. Also, the dissertation projects were consultancy-style projects which put a very practical spin on the studies I'd done in the prior semester.

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From: Indonesia

Currently working: as a Data Scientist at Google EMEA

I believe achieving MSc in Statistic with Data Science at University of Edinburgh is the best experience of my academic career. 

First, it has strengthen my core in theoretical statistics and enhance my ability in advanced data science problems. I acquired advanced proficiency in applying state-of-the-art data mining, modeling, and forecasting in era of big data. I also developed various software skills to support a variety of analytics applications.

Second, it has effectively built my leadership and communication skills which includes developing prominent practical solutions and understanding the relationship between research and analytics implementation.

Third, it has been an incredibly rewarding and enjoyable experience. The staff provides a great deal of academic support. The professors were very responsive and informative. I met so many people from different countries, which was an unbelievable experience. I found friends for a lifetime from all over the world and it definitely broadens my horizon.

Therefore, I view that SwDS program is the perfect combination of research and innovative technologies but includes the fundamental of mathematics to make the degree unique! It will align with prospective students who have a deep passion in quantitative analytics and interest to compete in the market of big data era.