Student stories - MSc Knowledge, Information and Data Science (KIDS)
Testimonials from our MSc Knowledge, Information and Data Science (KIDS) alumni
Xingyuan Feng
Why I chose KIDS
When I first learned about the MSc Knowledge, Information and Data Science (KIDS) programme, I was immediately drawn to its well-designed and highly structured curriculum. The course covers a wide range of topics, including machine learning, natural language processing (NLP), graph databases, and AI reasoning. This diversity made it particularly appealing to me, as it allowed space to explore different areas before deciding on a specific direction for deeper study and research.
A well-structured and progressive course design
The overall structure of the KIDS programme is very well balanced and thoughtfully organised across three terms. The first term focuses on building strong foundations, especially for students who may not have extensive prior experience in AI. It introduces the historical development of traditional machine learning models, alongside the underlying mathematical principles that support them. We were also taught how to use MATLAB for data visualisation, which helped make complex data and models more intuitive and interpretable.
In the second term, students are given the flexibility to choose optional modules based on their interests, such as NLP, machine learning, graph databases, or generative AI. This flexibility allowed me to focus more deeply on the areas most relevant to my academic goals.
A challenging but rewarding NLP module
Among all the modules, I particularly enjoyed Natural Language Processing (NLP). The course covered both the theoretical foundations and practical coding skills that were directly relevant to my research interests. There is no doubt that the content was challenging, but the teaching support was exceptional. The lecturers were patient and approachable, and the lab sessions were extremely well designed. Each piece of code was clearly commented, explaining not only how something worked, but why it was implemented in that way and what advantages it offered. This experience allowed me to gradually understand how professional researchers structure their code and design complete research pipelines, which has had a lasting impact on my own academic work.
A supportive and welcoming academic environment
Beyond the teaching itself, what impressed me most was the friendly and supportive atmosphere within the Department of Information Studies. Every member of staff was approachable and genuinely willing to help. When problems arose, lecturers would often come over and work through them with students step by step.
Support also extended well beyond academic matters. Our tutors were always willing to offer thoughtful advice on career planning, PhD applications, and potential pathways after graduation. My tutor, Antonis, played a particularly important role in encouraging me to pursue doctoral research within the department. More broadly, many other lecturers also offered guidance and support, making the department an incredibly positive and motivating place to study.
Studying at UCL and in London
Studying at UCL, and in London more broadly, provided many valuable opportunities beyond the classroom. I was able to take part in activities such as an exchange project focused on building online learning platforms for Ukrainian children. Through these experiences, I met students from different programmes within the faculty and built strong interdisciplinary connections.
I also had the chance to speak with Karen, a KIDS lecturer, during these activities. He shared insightful advice on applying for PhD programmes and later followed up by emailing me with recommendations for relevant application platforms. These conversations were extremely helpful and encouraging.
Preparing me for a PhD journey
For me, KIDS has been an excellent preparatory programme for doctoral study. My PhD research focuses on logical reasoning frameworks in large language models for fake news detection. The knowledge and skills gained from modules in generative AI, NLP, and AI reasoning have been directly applicable to my research. The programme equipped me with both the theoretical background and practical tools needed to transition confidently into PhD-level work.
Final advice
For anyone looking to pursue deeper study in artificial intelligence, KIDS is an outstanding choice. Beyond AI itself, many data analysis and information-related roles today require skills in AI, data visualisation, and database querying. The KIDS programme integrates all of these elements in a coherent and highly practical way.
Each module feels like an essential piece of a larger puzzle, and every course offered me skills and insights that I could immediately apply in real research and practice.
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Cexu Fu
Why I Chose to Apply to MSc Knowledge, Information and Data Science (KIDS) at UCL
It was 2022 Feb, when I got the interview with Prof. Antonis Bikakis. As a matter of fact, it was a surprise for a student like me, who has no systematic academic background in computer science or experience in engineering. My Bachelor’s was about media relations. When I got the interview email, I was so excited, and the talk with the professor was extraordinary for their expertise, kindness and professionalism.
How the KIDS Programme Aligned with My Multidisciplinary Background
What really matters for people who have worked for years when deciding to chase a new program, I bet, is a promising future and how the technologies and their philosophies can fit into their careers and lives. KIDS was the programme that shed light on technologies with a perfect match with my multidisciplinary background.
How Close the Course Is to Real Industry Practice
I think, KIDS is really closed to the real industry, teaching us something that can be directly taken back into the office, and change our desks with new tools.
What Makes KIDS Different from Other Programmes
In my opinion, KIDS is completely different from any other program out there. KIDS stands for pragmatic romance. If anyone happens to know, this is the motto of ByteDance. Professors, researches and students in this program are romantic professors, who are chasing perfection in technology to solve human-centric issues.
Unexpected Intellectual Depth and Human-Centred Perspective
Before joining KIDS at UCL, I have never imagined that I could discuss the philosophy of anti-colonization, privacy protection, and user rights in an engineering course. If anyone is chasing a field in technical game-change, I would highly recommend you this program. You will not only be trained to be a tech-savvy and technically advanced expert, but also supported to open the eyes to see the big world, where technologies are answers and consequences rather than tools only. For example, in the information governance module, I remembered the debate about how to use technologies to fight against the misuse of technology, rather than simply banning these due to potential risks. I believe, when a group of talents are working on building human-centric technologies, they care about the human rather than the tech, which could perhaps explain their energetic, pragmatic, and inspiring attitudes.
Teaching, Mentorship, and Academic Inspiration
In this program, I sincerely wish to say thanks to Antonis Bikakis, Rob Miller, Luke Dickens and many other professors who have taught me, inspired me, bore with my imagination and unstructured ideas. This program introduced me to the existence of knowledge graph, argumentation, and how new technologies are practically used in the industry.
How the Course Shaped My Career and Professional Growth
After graduation, I got a wonderful job at Tencent, and later at ByteDance. Notably, with this degree, my salary increased, compared to my salary before 2023, over 70%, and helped me become a middle-level manager at a big company like ByteDance. Although it sounds opportunistic, my career stands as a testimony for how useful this program could be. Today, I am an AIGC policy maker as well as an expert that uses AI to boost efficiency in tech giants. What I look for in the future is embracing technologies for next-generation governance and society. Luckily, KIDS has helped me build a solid basis for this.
Advice for Prospective Students and the Long-Term Value of KIDS
I know KIDS is currently offering a unique pathway in tech, not as mainstream as traditional CS courses, but certainly a more promising one in terms of a long-term value. I believe, the great popularity of large language models will fade off some day, and after that, people will need to stay chill and think about what technologies can offer to our human nature, beyond the excitement and fun of a grand, new, exotic machine. The reasoning module, the argumentation studies, the knowledge about databases, and how to use knowledge graphs to along with logic teach how to get technical answers, which means that the KIDS programme will remain an important area in the next era, the era that AI becomes routine and people live with AI.
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Hafsa Khan
Why did you want to study the MSc in Knowledge, Information and Data Science (KIDS) at UCL?
I chose the MSc KIDS at UCL because it struck the perfect balance between technical depth and real‑world relevance. I wanted a programme that looked beyond coding and analytics — one that explored how data becomes knowledge and drives intelligent decision‑making. KIDS stood out for its unique mix of data science, information management, and language technologies, all taught within an environment known for innovation and academic excellence.
What impressed me most was how the course went beyond traditional data science to examine how information is structured, interpreted, and applied to create meaningful insights. The modules on language processing, knowledge representation, and data intelligence aligned perfectly with the direction I wanted to take my career.
How did you find the KIDS course?
I discovered the KIDS programme while completing my BSc in Information Management for Business (IMB) at UCL. The course director notified the students this was the direction students steer towards if they want to pursue further academia in the domain. Upon, speaking with the teachers in Department of Information Science I felt like the bridge between information management and the expanding world of data science was a connected distinction.
I’d always been fascinated by how data captures meaning and context, and the KIDS programme transformed that curiosity into real, applied understanding. In a world where data drives transformation across every industry, the ability to interpret and apply it effectively has never been more vital - it’s the key to unlocking innovation and staying ahead in an age of disruption.
The course structure was well designed - each module built naturally on the previous modules, combining theory with practical, hands-on work. I realised the variety of lectures, labs, and collaborative projects, which made the learning experience immersive and relevant to this disruptive age we are currently in where technology is ever evolving.
What modules did you particularly enjoy and why?
One of the most memorable modules for me was Graph Databases and Semantic Technologies. It offered a modern way of looking at data - not as static records, but as networks of nodes and edges a visual representation of data. Working with the technology Neo4j, was a highlight, and the industry expert talks with Jesús Barrasa facilitated link theory to real-world applications. The module directly inspired my dissertation project for my MSc, where I used graph modelling and Cypher algorithms to create innovative data-driven solutions for my use case.
I also enjoyed the Database Theory and Practice module, which strengthened my ability to design, manage, and optimise data systems. Working on projects within MySQL taught me
how to turn complex and wicked ideas into precise, efficient database designs. The technical insight I gained continues to support my current work at IBM as a Data Engineer, where I maintain data and provide modern enterprise-grade solutions that drive business transformation to retailers, banks, and insurances.
What surprised you about the course?
The level of academic support throughout the programme genuinely exceeded my expectations. Lecturers were approachable, encouraging, and invested in our progress. Feedback was always constructive and refined my critical thinking and strengthen my work.
I was also impressed by the range of elective modules available. Whereby I had chosen to take the module of Intelligence and Risk Analysis. The module broadened my view beyond pure data science, introducing analytical frameworks from disciplines like psychology and strategic decision-making. It reinforced how diverse ways of thinking can complement technical expertise and lead to better, more informed outcomes.
Aside from teaching, what else did you find useful or enjoyable about the course?
Working with peers and fellow classmates from different academic and non-technical backgrounds enabled discussions which brought about diverse ways of approaching problems. This interactive learning environment developed my communication and shaped how I work with others to deliver outstanding results. This clarified my career direction, solidifying my interest in data architecture and analytics.
How did you find studying in London?
Studying at UCL, in the centre of London, was an incredible experience. From data-focused talks and tech events to cafés around Bloomsbury, there was always something inspiring to explore. What I appreciated most was the balance - studying on campus paired with the chance to discover new parts of the city.
How did the course help you in your career journey?
The MSc KIDS program reshaped and taught me to think beyond the technical layer - to comprehending how information creates value and drives advancements. The combination of academic depth and practical projects meant I graduated ready to apply those skills in the workplace.
I was equipped to analyse complex datasets, design intelligent data architectures, and translate insights into business impact. More importantly, the KIDS course developed a way of thinking - curious, analytical, and creative - which continues to shape my career. It gave me both direction and confidence to make a meaningful contribution in the data and AI space.
What advice would you give to anyone considering the course?
Approach the programme with curiosity and drive. The more you engage with the material, the more rewarding the experience becomes. KIDS isn’t just about learning data science - it’s about developing the mindset to use data as a tool for innovation and actionable insights. If you’re passionate about understanding how information can shape the world, this programme gives you the skills, support, and inspiration to make that happen.
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Thomas Li
The Perfect Blend of Theory and Practice
UCL has an incredible reputation for innovation, which was a huge draw for me. What really stood out about the KIDS programme is how welcoming it is-it’s perfectly suited for students who might not have a strictly technical background but are eager to pivot into the data field. It bridges the gap between learning how to code and understanding why we use data, making the transition into tech feel natural rather than overwhelming.
From “Messy Data” to Real Insights
The curriculum is rigorous, but definitely manageable. I particularly loved the Machine Learning Methods module because it wasn’t just abstract theory; we dealt with messy, real-world data to solve actual problems. I also really enjoyed Foundations of Machine Learning. The lecturer, Luke, was fantastic at explaining complex mathematical concepts in a clear, accessible way. It was amazing to see how quickly I gained confidence in building my own models, even starting from scratch.
A Supportive Community
One of the best things about the course was the community atmosphere within the department. It felt incredibly supportive rather than competitive. You could always find someone near Gordon Square to grab a coffee with, whether it was to debug code together or discuss the concepts from a lecture. It really felt like we were all united, helping each other progress through the course.
Ready for the Future
The course has been a massive boost for my career and played a key role in securing my current position as a Risk Data Manager at a multinational bank. The skills I honed at UCL-especially in handling complex datasets-are things I use daily. For anyone considering this course, especially if you are worried about not having a tech background: don’t be. Be curious, ask for help, and trust the process. It prepares you perfectly for the industry.
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Wenhui Li
An interdisciplinary programme that matched what I wanted
I chose the MSc Knowledge, Information and Data Science (KIDS) because it genuinely sits at the intersection of data science, information science and knowledge engineering. What attracted me most was that the programme doesn’t treat data work as “just modelling”. It also takes seriously how information is represented, organised, governed and used in real settings. Studying this at UCL, in a research-active environment, felt like the right place to develop both technical depth and critical thinking , with clear links to careers in information technology and AI.
Rigorous, but always grounded in real practice
KIDS was challenging in a good way: broad, fast-paced, and methodologically solid. Over time, I stopped thinking in terms of “which tool should I use?” and started thinking more like “what exactly is the question, what assumptions am I making, and how do I justify my choices?” That shift made my work feel more structured and more meaningful. I also really appreciated how supportive the lecturers were across KIDS. The teaching team was consistently approachable and helpful, not only academically but also in terms of day-to-day support and guidance for my next steps, including PhD applications.
Favourite learning moments
I particularly enjoyed learning that combined theory with practical work, the kind of work where you have to make trade-offs, justify choices, and explain outcomes clearly. One module that really stayed with me was Foundations of Machine Learning. The group coursework was a highlight for me, because working with classmates from different backgrounds made me think harder about how to judge results properly and how to explain our ideas clearly to each other. I was also pleasantly surprised by how transferable those habits were. They kept coming back across different topics, and then again when I moved into my dissertation, where refining the question and being careful about assumptions mattered just as much as the analysis itself.
London as an extension of the classroom
Studying in London added energy to the year. I have very fond memories of working in the UCL Student Centre. It became my go-to place to focus, organise my thoughts, and get assignments over the line. Even small routines like studying there, grabbing a coffee nearby, and then heading into a seminar made the whole experience feel busy and exciting, in a very “UCL” way.
How KIDS supported my next step
KIDS gave me a foundation I still rely on: asking clearer questions, choosing methods with
more confidence, sense-checking results, and explaining findings in a way that makes sense to different audiences. That combination supported my move into PhD-level research, where I now work with complex biomedical and clinical data and need both technical depth and careful interpretation.
A final piece of advice: be curious, and make it practical early
Be ready to learn across disciplines, that’s the superpower of KIDS. Stay organised, practise consistently, and make the most of your cohort. If you can, connect what you learn to a real problem early, through a small project or dissertation idea. Also, don’t be shy about speaking with lecturers and tutors. A quick conversation can give you surprisingly useful guidance and open up new ideas. It’s the fastest way to build confidence and direction.
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Benjamin Ward
Motivation for Choosing MSc Knowledge, Information and Data Science (KIDS) at UCL
Before starting the MSc, I completed my BSc Computer Science degree. It was an interesting degree, with a wide range of topics. During my time, I was particularly interested in the world of data, and I wanted to learn more. The Knowledge, Information and Data Science (KIDS) programme was a way for me to learn more about data, how it is gathered, stored, and used. UCL has always been a university that interested me for many reasons, such as the academic quality and historical ties. I found out about the specific course through the UCL website.
Favourite Modules and Learning Highlights
I enjoyed all the modules for many different reasons. ‘Machine Learning Methods’ was fun because we learnt about how AI actually thinks, and what new applications are being discovered. ‘Information Governance’ was enjoyable because it provided the industry/business perspective on data. Learning about topics such as UKGDPR has helped me prepare for working in the industry.
What Surprised Me About the Course Cohort
What surprised me was the background of everyone attending the course. There were people like me who had a computer science/mathematical background. But there were many others with completely different degrees. I came from education, but others came from work. If you reach the requirements, you will be welcome.
Learning Experience Beyond Traditional Teaching
I enjoyed the variety of coursework, such as programming, presentations, blogs and creating our own tutorials. You are not being scored on your memory, but on your ability to apply your knowledge. Also, we had some guest speakers who helped demonstrate how our studies are applied in real life.
Studying in London
Studying in London is a wonderful experience. Naturally, it is busy, but that means you always have something to do.
Impact of the Course on My Career
I was able to demonstrate my knowledge and skills in a variety of projects I worked on, which helped me secure my current job as an AI Engineer.
Advice for Prospective Students
If you want to learn practical skills that will help you understand the world of data, this is the course. There is a healthy mix of deep mathematics, explaining how models work, to higher-level topics like how businesses would see and use the technology.
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Crystal Yu
Why did you want to study on MSc Knowledge Information and Data Science (KIDS) at UCL?
When I reflect on why I chose to study the MSc KIDS at UCL, the decision was primarily driven by alignment between my background and my long-term academic and professional goals. My academic training in Finance and Management exposed me to quantitative and statistical models, and during my placement year I worked extensively with data-driven business strategies. However, I became increasingly aware that I was often applying analytical tools procedurally rather than understanding their underlying mechanics. I could execute models, but I could not always clearly articulate their assumptions, limitations, or theoretical foundations.
I initially attempted to address this gap through self-study, completing online courses and tutorials in programming and data analysis. While this approach strengthened my technical skills, it lacked conceptual structure. Without a coherent curriculum, it was difficult to distinguish between foundational principles and specialised applications. I found myself accumulating techniques without developing a systematic understanding of how and why they work.
The MSc KIDS programme at UCL offered what self-study could not: intellectual rigour, theoretical depth, and a structured framework for understanding data science beyond surface-level applications. I was not simply seeking to enhance my technical proficiency; I wanted to engage with the epistemological and methodological foundations of knowledge and information systems. The programme’s interdisciplinary approach provides a robust framework for understanding how information is generated, organised, and interpreted. This depth is essential to my goal of applying data science responsibly, critically, and with a clear awareness of its conceptual underpinnings, rather than using analytical tools in a purely operational manner.
How did you find the KIDS course?
I found the KIDS course highly engaging and academically enriching. The curriculum covered three main dimensions of information and data science: quantitative foundations, computational and modelling techniques, and data management and knowledge systems. This structure allowed me to see clearly how statistical reasoning underpins machine learning models, and how both connect to the way data is structured, stored, and interpreted within information systems.
One of the programme’s strengths was its carefully designed learning structure. The compulsory weekly tasks and assessments ensured that all students developed a solid grasp of the core concepts. At the same time, advanced exercises and recommended readings were available for those who wanted to explore topics in greater depth. This layered approach allowed me to control the depth of my engagement depending on my interests and academic goals.
Several modules also offered flexibility in focus, allowing us to tailor research topics and explore areas that aligned with our interests. This balance between structured guidance and intellectual autonomy encouraged independent thinking while maintaining academic rigour.
Overall, the course provided both a strong academic framework and meaningful flexibility. The clarity of the teaching materials and the support from faculty were essential in helping me navigate complex concepts with confidence.
What modules did you particularly enjoy and why?
I particularly enjoyed the Machine Learning Methods module. The course was intellectually demanding, but it was also one of the most rewarding components of the programme. What stood out to me was the professor’s integration of recent research papers and up-to-date developments in the field into the lectures. Rather than focusing solely on established techniques, the module exposed us to current debates, methodological innovations, and emerging applications in machine learning.
Engaging directly with academic papers significantly deepened my understanding. Although reading and analysing research articles was challenging, the process strengthened my ability to critically evaluate methodologies, interpret results, and assess the broader implications of different approaches. Class discussions further enhanced this learning, as they encouraged analytical thinking and the articulation of complex ideas.
Importantly, the module helped me gain clarity about my own academic interests. By being exposed to diverse research directions within machine learning, I was able to reflect on which topics resonated most with me and begin identifying potential areas for further study or specialisation. Overall, the course not only strengthened my technical and theoretical knowledge but also provided valuable insight into how research in the field evolves.
What surprised you about the course?
What surprised me most about the course was the steep learning curve throughout the year, particularly in some modules. While the programme began with foundational topics such as statistics and logic that felt manageable, certain modules quickly became much more abstract and technically demanding.
In those modules, I found that fully understanding a single lecture often required several additional hours of independent study. The content was cumulative, so if you did not stay up to date, it was easy to fall behind. Missing one key concept could make subsequent topics difficult to follow, and in some cases, you might not fully recover that gap in understanding.
This experience made me realise how important consistency and active engagement were. Some modules required steady effort each week, and you could not rely on last-minute revision. Although challenging, this pushed me to become more disciplined and proactive in my learning.
Asides from teaching, what else did you find useful/enjoy about the course?
Beyond the teaching itself, the cohort experience was one of the most valuable aspects of the programme. During the first term, we all attended the same core modules, which fostered a strong sense of community. Because everyone was engaging with the same material, it was easy to approach one another with questions, whether academic or practical.
Informal interactions, such as having lunch together between lectures, strengthened these connections and created a supportive learning environment.
I am particularly attracted to the diverse cohort. Some students joined directly after completing their undergraduate degrees, while others brought five to ten years of professional experience. A few were even pursuing the programme as a second master’s degree. This range of backgrounds significantly enhanced both classroom discussions and informal conversations. Different professional perspectives often led to varied interpretations of the same problem, which deepened collective understanding.
What stood out to me most was that each individual brought expertise from their own field, whether finance, technology, public policy, or other domains. The programme created a space where these diverse experiences converged, encouraging us to think creatively about how information and data science could be applied within our respective careers and across disciplines. This collaborative and interdisciplinary environment was both intellectually stimulating and professionally motivating.
How did you find studying in London?
Studying in London was a dynamic and sometimes overwhelming experience. The city moves quickly, and there is always something going on, talks, exhibitions, industry events, or informal meetups. It took some adjustment at first, but over time I learned how to make use of these opportunities in a way that suited my interests and schedule.
What I appreciated most was the everyday exposure to different perspectives. Whether through classmates, guest speakers, or people I met outside university, conversations often extended beyond coursework and into current industry practices or global issues. These interactions felt natural rather than organised, and they added another layer to my learning.
At the same time, living in a fast-paced city required balance. Managing academic workload alongside everything London offers was not always easy, but it helped me develop better time management and independence. Overall, studying in London shaped not only my academic experience but also how I engage with people and opportunities more broadly.
Overall, London is a vibrant and fast-paced city that encourages initiative and curiosity. For me, studying there meant not only academic development but also continuous exposure to new ideas, industries, and communities.
How did the course help you in your career journey?
The course played a significant role in shaping my career direction. It provided me with the opportunity to explore different areas within information and data science, which helped me clarify where my genuine interests lie. Rather than viewing the field as a collection of disconnected technical skills, I developed a more structured understanding of its conceptual landscape and potential career pathways.
A key contribution of the programme was its strong emphasis on methodology and theoretical foundations. This training enabled me to move beyond surface-level tool usage and understand the assumptions, limitations, and reasoning behind different approaches. As a result, I became more confident in evaluating methods and selecting appropriate techniques for specific problems.
Equally important, the course taught me how to independently acquire knowledge in a rapidly evolving field. Through reading academic papers, presenting complex concepts, and engaging in critical discussions, I developed the skills needed to continuously update my understanding. Given how quickly technologies and research in data science advance, this ability to learn efficiently and critically is essential.
Overall, the programme equipped me not only with technical competence but also with the intellectual tools and learning strategies necessary to support my long-term career development.
What advice do you have for anyone considering the course?
My main advice for anyone considering the course is to approach it with a clear sense of purpose. Exploration is a central part of the programme, and it offers significant flexibility in terms of depth and direction. However, to make the most of this flexibility, it is important to reflect in advance on what you hope to gain, whether that is technical specialisation, theoretical grounding, career transition, or research preparation.
Having a defined goal does not mean limiting yourself; rather, it provides a framework for decision-making. It helps you choose modules strategically, prioritise readings, select research topics thoughtfully, and allocate your time effectively. Given the intensity and breadth of the course, clarity of purpose allows you to maximise both academic and professional outcomes.
In short, the more intentional you are about your objectives, the more rewarding and productive your experience in the programme will be.
Kaede Hasegawa
From Psychology to Data Science and Choosing MSc KIDS
Coming from a social sciences or non-STEM background, you might be thinking about switching your career path into data science. Maybe you’ve realised that data science feels more exciting than your current subject, or maybe you’re curious about riding this big wave of generative AI. That was basically me back in 2022: a psychology student who found myself more interested in R programming than psychology itself. As a career switcher, this master’s degree was the perfect way to build the required skill set and become a better candidate before entering the highly competitive tech job market.
Openness to Non-Technical Backgrounds and a Supportive Cohort
The MSc Knowledge, Information and Data Science at UCL is one of the few data science conversion programmes available to students without a technical undergraduate background (and it took me days of Googling to find this out). This openness to diverse academic backgrounds was one of the main reasons I applied. Once you join the course, you quickly realise that you are not alone—many of my classmates also came from non-technical and even non-STEM backgrounds. Studying alongside people with similar aspirations was both reassuring and motivating.
Diversity of Modules and Building a Strong Technical Foundation
The diversity of the course goes beyond the student body and extends to the modules as well. The programme covers a wide range of areas within data science, and by the time I completed all the modules, I felt I had built a solid foundation. One of the most unique and exciting modules I took was Graph Databases and Semantic Technologies. Graph databases are still quite niche, yet they have strong real-world applications; I even used this module in my dissertation to work on a routing algorithm. I now use graph databases regularly in my work, and having this niche specialism gives me a lot of confidence.
Honest Reflections on Academic Challenges
While I hope to motivate all career-switchers, it’s important to be honest about the challenges too. This course involves mathematics (such as differentiation and integration) as well as programming. If you are someone who feels intimidated by mathematical formulas, I would recommend familiarising yourself with these areas before starting the course. This is particularly true for the Foundations of Machine Learning module, which is now compulsory. The course also sits within the Faculty of Arts and Humanities, which means that some of the career events organised by the faculty may not always align with tech-focused career paths. That said, studying in London gives you access to countless external career fairs and networking events, which I found incredibly valuable.
Lecturer Support and Advice for Prospective Students
I initially worried about studying in a completely new field, but the lecturers were always supportive and willing to make time for questions. Don’t hesitate to ask for help and make the most of the support available to you. I am very grateful to all the lecturers for their encouragement throughout my journey. If you are considering this course, I hope you feel encouraged to challenge yourself—it is absolutely worth it.