Jack is a student on the MSc Sport Data Analytics programme.
What attracted you to the MSc Sport Data Analytics programme, and why did you choose this course over other postgraduate options?
During my undergraduate degree in Business Management, I found that the modules I enjoyed most were the ones built around data, statistics and problem-solving. That experience made me realise that I wanted a career where I could apply this analytical work with something I was genuinely passionate about - that being sport. The MSc Sports Data Analytics programme stood out because it offered that exact combination. I considered broader data analytics and business analytics options, but this course felt much more aligned with where I wanted to go. It was not just about learning technical skills in isolation; it was about applying them to real sporting problems.
I chose Strathclyde because the programmed offered the opportunity to study coding, data analysis, recruitment and performance analysis within a sports context, with the opportunity for a practical placement made the course feel much more purposeful for me.
For someone who wanted to move from a business background into sports analytics, it offered the adaptability to move across from my business background into sport.
How has the programme helped you develop your technical, analytical, and professional skills since you started?
Before starting the MSc, I did not have much coding experience. Since beginning the course, I have become much more confident using R for data cleaning, analysis and visualisation. I have also learned how to approach big data more systematically. Analytically, the biggest development has been learning how to think more critically about what data can and cannot tell us. In sport, it is easy to look at a number and treat it as a complete answer, but the course has taught me to question context, sample size, model limitations and practical meaning.
Professionally, the course has helped me become more confident in presenting my work and explaining technical ideas in a clear way. I have had to produce dashboards, reports and analysis that are useful to coaches and analysts. That balance between technical detail and real-world communication has been one of the most valuable parts of the programme.
Sport Data Analytics attracts students from a range of backgrounds. How did your previous studies prepare you for the MSc, and what would you say to someone considering the course who may not have a traditional sports analytics background?
My background was not a traditional sports one. I studied Business Management at undergraduate level, so I came into the MSc with very limited experience in coding or formal sport, beyond it being a passion of mine. However, that background didn’t hinder me in the slightest. The course leaders were very good at ensuring you are well supported and able to understand all of the coding and analysis from scratch
I think that is important for prospective students to understand. You do not need to have followed a sports analytics pathway to succeed on the course. What matters more is curiosity and a willingness to learn. The technical skills can be built over time, especially if you are prepared to practise and make mistakes early on. My advice to someone from a non-traditional background would be not to rule yourself out. If you enjoy sport, data and problem-solving, then your previous experience may be more relevant than you think. Different backgrounds can actually be a strength because sports organisations need people who can look at problems from different angles.
Can you tell us about your placement experience – what was your role, and what were some of the key projects or responsibilities you worked on?
My placement experience was focused on football recruitment analysis with Dundee United. My role was split into 2 parts. Firstly was video recruitment analysis which involved me watching players and writing up reports with recommendations of whether to monitor the player further. I actually watched and wrote up a positive report on Emmanuel Agyei, who Dundee United then went on to sign in January. Having the opportunity to directly be involved with a professional football transfer was a dream come true. The second part of the placement involved developing a data-driven recruitment methodology that could help identify and compare potential transfer targets. The placement gave me the opportunity to apply what I was learning on the MSc to a real football environment, which made the work feel much more meaningful.
What were the most valuable lessons or insights you gained during your placement, and how has that experience influenced your career goals or decision-making about your future?
The most valuable lesson from the placement was that good analysis has to be useful, understandable and relevant to the decision being made. In football recruitment, a technically strong piece of analysis has limited value if it does not fit the club’s context, budget, playing style or practical needs, it has to add practical value. The experience has definitely influenced my career goals. It confirmed that I enjoy recruitment and performance-related analysis. It also helped me understand the standards required in a professional environment and how the football world really works. More broadly, it has made me realise the importance of these skills in every industry. I recognise that the technical and analytical skills from the course can be valuable and transferable across sport, business and wider data roles.
What advice would you give to prospective students who are considering studying MSc Sport Data Analytics?
My main advice would be to come in ready to learn actively. The course is not something where you can just listen passively and expect to improve. You get the most out of it by practising and experimenting with data in your spare time. If you are new to coding, don’t worry! It can feel uncomfortable, at first but you will pick it up in no time. I would also advise prospective students to start to play around with R now to get a mini head start (this is optional of course). Also, they should build a small portfolio of work as they go. Whether it is a dashboard, a piece of analysis, a recruitment project or a visualisation, having examples of your work is extremely useful. It helps you develop your skills, but it also gives you something tangible to show employers.
Finally, I would say that you should keep an open mind about where the course can take you. Sport data analytics is a competitive field, especially in football, but the skills you develop are highly transferable. The programme can help you become better technically, but it also teaches you how to think more critically, communicate evidence and solve real problems. That is valuable whether you want to work in sport specifically or use sports analytics as a route into a wider data career.