Statistics is the art of drawing conclusions about phenomena in which chance plays a role. The randomness may arise through a variety of reasons: the intrinsic random nature of a phenomenon, unavoidable noise in an experiment, conscious randomisation of experimental or measurement units, or as a best approximation to a too complex reality. The chance phenomena occur in a broad range of situations. This has rendered statistical science a highly multidisciplinary undertaking, but with a core body of concepts and methods that are common to the diverse applications.
Statistics for the life sciences is almost synonymous with biostatistics. It incorporates quantitative modeling and methods of data analysis for clinical and epidemiological research (e.g. survival analysis) that in the past twenty years have become indispensable in medical research. It also includes statistical methods used in genetic research and genomics, which have a classical foundation (for instance in the work of Fisher, one of the founding fathers of statistics), but are rapidly developing in answer to present-day opportunities provided by data from new experimental platforms, such as micro-arrays and whole-genome scans.
The program is targeted at human genetics as well as plant and animal genetics. Areas like systems biology make similar demands for new statistical methodology, and the analysis of medical images will increase in importance, both in research and in clinical applications.
It is no exaggeration to say that all empirical research in the present day social and behavioural sciences relies critically on statistical analysis. There is a long-standing statistical tradition in educational and psychological testing (psychometrics), and also in survey research, marketing research and quantitative demographics (sociometric).
Similar sub-domains that have emerged more recently are the quantitative study of the development of science and technology (scientometrics and bibliometrics), the quantitative study of stylistic forms and patterns in the use of language (stylometrics), the quantitative study of taste and smell (sensometrics), the quantitative study of history (cliometrics), and the empirical approach to the law (jurimetrics). The common use of the term ‘metrics’ here illustrates the important role of measurement problems in these fields.
More recently the role of statistics has gain in importance in many areas such as in biological psychology, in cognitive science with the advent of fMRI data and in forensics with DNA data.
Although some statisticians would argue that Data Science = Statistics, we prefer the point of view that there is quite an overlap between the two, but that they are not identical. Data Science is a combination of Statistical Science and Computer Science, and there should be no Data Science without Statistics, but also no Data Science without Computer Science.
Statistics has surfaced in Computer Science research areas such as pattern recognition (for example in speech recognition on your smartphone), data mining (for example used in online advertising) and network analysis and deep learning (for example used in Watson, IBM’s Jeopardy computer that is now used to perform medical diagnoses).
In practice data scientists are required to be able to work with large amounts of data and so competence in management and the use of databases is critical.
That is why we offer a specialization of Statistical Science called Data Science, in collaboration with the Leiden Institute of Advanced Computer Science (LIACS). Students in this specialization follow the mandatory courses in the basic Statistical Science program, and use the ECTS to be spent on elective courses in the program to follow necessary courses in Computer Science.
It is extremely convenient that Statistical Science and Computer Science are located in the very same building in Leiden (the Snellius Institute). Through this cooperation, Leiden University aims to fill a growing job market demand by offering its students a unique combination of theoretical and practical tools stepping into the promising area of Data Science.
“Statisticians are scarce, so they are highly sought after.”
“I have always been interested in a wide range of subjects, so I found even the broadest bachelor’s programmes too limited. Once I heard about this master’s specialisation, the decision was easy. This is the only department in the Netherlands that offers a complete programme including statistical research methods for almost every subject. I am not restricted to only the methods and techniques used in psychological research.
Using statistics you learn to handle data from all these different disciplines. You do a lot of research in this programme. What I like most is discovering how far your expectations agree with the results you obtain from the data you have amassed. And that’s relevant in every field.
It’s also good that you visit companies and research institutes as part of the programme. What’s more, they often come here and join in our lectures! I get to attend conferences and symposia on a regular basis, too. The advantage of all these contacts is that you get a good idea of what you might want to do after your master’s.
I don’t have any worries about my future: statisticians are scarce, and wherever data are collected, an applied statistician is more than welcome.”