Master of Science in Statistical Data Analysis
David Mwakazanga (Zambia), was Trainee Data Analyst
Having obtained a degree in Demography and Statistics I was Trainee Data Analyst in the Tropical Disease Research Centre in Zambia. As a student who spent a considerable time searching the world for a statistical programme that would suit my personal and job requirements and a scholarship, I think it's essential that the Master in Statistical Data Analysis is offered as a one year full-time programme. The content is flexible enough to accommodate career needs and all sorts of student backgrounds. Moreover, the Professors give enough materials through classes and Minerva, such that there is no need for extra time for a student to search for more materials to pass a course. Additionally, potential scholarship funds are not very willing to sponsor a student for more than a year to get a master’s qualification.
Katrien Verschueren (independent consultant), worked at Ablynx (biopharmaceutical company Ghent)
After several industry jobs as bio-engineer (from Research and Development to Marketing and Sales) I decided to give my career a quantitative turn and to return to my ‘true love’, mathematics and statistics. However, and notwithstanding an engineering background, a job as biostatistician cannot be performed without thorough knowledge of the underlying statistical principles. I came into contact with the master’s programme and now, two and a half years later, I am absolutely convinced that all the time and energy spent to the programme was worth it! I have spread the programme over two years as I combined it with a full-time work assignment. It was a tough combination; however a very interesting balance between theory (in class) and practice (at work). Moreover, the numerous take home problems and project assignments made us apply the learned methods and principles to real-life examples making it very concrete. Most importantly the programme delivered the fundamental basics to understand the concepts and at the same time the tools and the information about how to tackle everyday statistical problems encountered at work.
Marijke Welvaert (Ghent University), teaching assistant and Ph.D. student in Psychology
Everyone thought I was going insane when I told them that I was going to follow the Master of Science in Statistical Data Analysis after I had got my master’s degree in Psychology. The link between psychology and statistics wasn’t that obvious. However, it was crystal clear to me. Statistics and data analysis were my favorite courses during my psychology education and, if I may say so, I was good at them. So, how hard could that master’s programme be … ? Beginner’s mistake: don’t overestimate your knowledge. Very soon, I understood that I didn’t know all about data analysis, on the contrary, the more the year continued, the more I realised that I knew nothing at all. During the programme a whole new exciting world opened up and slowly I became part of it. Now I’m working at the department of Data Analysis at the Faculty of Psychology and Educational Sciences and I’m finding myself at the other side of the classroom. Now it is me who is explaining what the meaning of a p-value is and how to interpret that parameter. The skills and knowledge I achieved in the Master help me now to anticipate the problems that the students have and make it a lot easier to explain what statistics are about. Let’s face it, psychology students and statistics make not a very lovely couple after all. But not only in teaching I can rely on the master, also for my PhD the practice I received will be indispensable. Honestly, it was a tough year but I look back at it with great satisfaction.
Mieke Van Hemelrijck (Kings College London), currently Ph.D. student Epidemiology
As a student in Biomedical Sciences (Ghent University), I really enjoyed Dr. De Bacquer’s class in epidemiology. It was during this time that I had my first foray into public health research, and more particularly began to develop my interest in epidemiology. Aiming to further develop my aptitude for quantitative public health research, I then pursued the advanced master’s degree in Statistical Data Analysis at Ghent University. This year of statistical theories, programming, analyses, and applications was a huge challenge for me. I spent days, evenings, and weekends trying to understand course notes, doing homeworks and projects, and writing papers. Nevertheless, I enjoyed it very much as it was an excellent opportunity to learn from classmates and professors from across the university applying statistics in different fields. Seeking further training, which would provide further opportunities to apply my background in Biomedical Sciences and Statistics to promote public health, I accepted an offer from the Harvard School of Public Health (HSPH) to pursue a Master of Science in Population and International Health. These last two years at HSPH have been a time of much professional and personal growth. In particular, the training in epidemiology has proven extraordinarily rewarding and engaging, as it embodies a balance between statistical methodology and practical application to public health that continues to inspire my academic and career aims. The master’s degree in Statistical Data Analysis provided me with much practical statistical knowledge, which has helped me throughout the course of my studies in public health. It will also be of great use during the next years, as I will continue my academic career in the field of epidemiology. As of August 2008, I will be a PhD student in Cancer Epidemiology at the Division of Cancer Studies from Kings College London. I earnestly encourage students to pursue this Master in Statistical Data Analysis, as it provides many different opportunities and instills a knowledge foundation for statistical applications in several fields. It is a very challenging programme, but also extremely rewarding afterwards!
Maarten Daem, teacher
After my second year of studying Mathematics, I wasn’t sure whether to choose the option of Pure Mathematics because of the analysis courses, or the option of Applied Mathematics for statistics. Back then I went for the analysis, but my love for statistics never really went away. I think the programme is particularly useful, since many of the theories and formulas that for a mathematician usually stay just formulas, now are being brought into practice. You also come across a broad spectrum of research areas in which you can end up as a statistician. So regarding next year, after finishing the programme, I will be looking around for a while before deciding where exactly I want to go. But since statistics is used almost everywhere, I’m sure I will end up somewhere, and moreover I will like the job. But make no mistake, it’s really hard work, even more so for non-mathematicians than for mathematicians. But if you like statistics, it’s pleasant work! Well, at least most of the time!
Prof Dr Marleen Temmerman (Ghent University), Department of Obstetrics and Gynaecology
Contribution of statistics in research in reproductive health Appropriate and reliable data are much-needed for anyone involved in health care, whether at the level of planning, execution, research or practical implementation. Within the framework of clinical and operational research, the importance of a thorough experimental design, accurate data collection and a professional statistical analysis cannot be stressed enough. In particular, a good design of the study with involvement of the statistician from the very beginning is a rule of thumb which is very important but nevertheless often violated. In my research activities in Africa, where I worked on reproductive health (family planning, mother-child care, HIV/STD, women’s health in general), it quickly became clear that good and solid data are highly important for further planning, interventions, health economic decisions, etc. Especially in international research the quality of information is extremely important, and this requires knowledge which we as doctors do not sufficiently have. For this reason, I myself took a course in epidemiology and statistics, in order to understand the basics and to be able to think in a more goal-oriented manner with regard to certain research questions. But what this course especially made me realize, is how important the role of a professional statistician can be. Since then, in all our projects we cooperate with statisticians, preferably with knowledge of and interest in health. Important here is that the statistician is involved from the beginning and not just brought in at the end to save the data. Many of my former students and current colleagues in Africa are now active as researchers in various fields and have obtained knowledge in monitoring and surveillance, good clinical practice in which obviously evidence-based thinking is important. They collaborate in clinical trials and use their data to establish guidelines in cooperation with international organizations. They gained experience on the field and are all asking for a better education in operational research methods and biostatistics. I am convinced that this master’s programme gives an answer to an enormous need from field workers, where the important health problems cry out for a well-considered evidence-based approach.
Dr Marleen Boelaert (Institute Tropical Medicine), Professor of Epidemiology
Contribution of statistics in international health care Data analysis and statistics are a crucial discipline for people who aim to contribute to public health. Nobody will be surprised anymore that statistics is very important within clinical research. The development of new medicines or techniques is directly based on the capacity to evaluate the real effect of the new product in an independent manner. But there is much more. During my work in developing countries I was quickly confronted with the need to accurately describe and analyse the health problems in the region where I was working. In humanitarian work, correct and reliable information plays an important role, and very quickly I needed to become familiar with techniques of sampling and analysis. In this field we often work with professional biostatisticians: this ranges from the design of studies to monitor malaria resistance in Central Africa, over the modelling of epidemics, to the evaluation of new diagnostics. To identify risk factors for infections, logistic regression is used. A rational approach to health problems requires a well-considered choice of interventions, which should be evidence-based. Methodologies of meta-analysis and decision theoretic analysis (cost-effectiveness analysis) can be helpful here. Many of my colleagues in Africa today are dedicating themselves to the battle against AIDS, and more specifically to facilitating access to anti-retroviral therapy (ART). The clinical prognosis of these patients under ART needs to be evaluated and the surveillance of these patients through laboratory tests such as CD4 monitoring and viral load tests needs to be simplified. This also means: data management, data analysis, survival analysis, repeated measurements, association studies … and thus, well-trained biostatisticians.
Tom Van de Wiele (Google DeepMind), Computer Scientist, Research Engineer
About one year after starting my professional career I felt the need for a more fundamental knowledge base in data analysis. I had been working in a chemical plant as an automation engineer with a growing focus on process improvement and the master’s programme seemed to fill the gaps I was facing during my day job. In hindsight, it did not only fill the gaps but the programme also enhanced my knowledge of statistical computing. The course on the programming language R was particularly well taught and I have been using R daily for the past three years. The courses cover a wide range of topics in statistics in depth and I found all teachers to be highly knowledgeable. Combining the programme with a full-time job was very challenging and in the end, it took me three years to complete it. There is a strong focus on training the students as statistical consultants which explains the large number of tasks and several group projects. The assignments will surely keep you busy during the weekends so it is key to be passionate about data analysis! To be honest, I found it hard to maintain a healthy work-life balance in my first year when I was still hoping to complete the programme in two years. The knowledge I gained from the programme was fundamental to do as well as I did on Kaggle, the most popular competitive machine learning platform. I won a recruiting competition organised by Facebook which helped me land a dream job at DeepMind, the research group of Google in general artificial intelligence.