Master of Science in Statistical Data Analysis
Increasing computer power and the professional need to extract objective information from observed data have led to complex databases. International professional and research standards in various fields demand high quality data analysis, performed by qualified statisticians. The Statistical Data Analysis programme offers scientists from a variety of fields intensive training in modern statistical methods and data analysis.
What
Increasing computer power and the professional need to extract objective information from observed data have led to complex databases. Statistical science has become a broad discipline with well-developed methods and techniques for the design and analysis of a wide range of empirical studies. Information obtained from correctly analysed data allows to predict, adjust and even optimise processes based on evidence. Inefficient or haphazard data gathering and analysis, however, can lead to inferior or misleading conclusions, possibly with far-reaching consequences. Hence, international professional and research standards in various fields demand high quality data analysis, performed by qualified statisticians. This programme offers scientists from a variety of fields including biology, bioinformatics, economy and marketing, environmental and life sciences, engineering, mathematics and physics, psychology and social sciences, … intensive training in modern statistical methods and data analysis. The programme aims at improving problem-solving skills and evidence-based decision-making. This will enable scientists to play a distinctly important role within their field of expertise.
For whom
The admission requirements depend on your prior education (type of degree, country of issue etc.) or additional experience.
Structure
The curriculum (60 ECTS) consists of mandatory general course units (12 ECTS), course units specific to the chosen main subject (33 ECTS), and a Master’s dissertation (15 ECTS). In every course unit, the theory is supported by projects and assignments in order to develop skills of practical data analysis. In so doing, we provide hands-on experience with real data. You can take this programme either as a full-time one-year programme, or stagger it across two or more years. Several of our course units are taught in the evening. The curriculum consists of two main subjects.
main subject Statistical Science
This track provides a solid basis in statistical thinking and methodology, with a focus on understanding and applying statistical concepts and bridging the world of statistics and that of empirical sciences. A wide variety of elective course units allows students to tailor the curriculum to their own background and interests. Our lecturers are active researchers, and collaborate on projects with the industry and with society. Our elective course units offer modern statistical methods with a strong emphasis on application. Statistical Science graduates are all-round statisticians.
main subject Computational Statistics
The generation of increasingly complex and massive data sets means that statisticians need to work together with data managers and computer scientists now more than ever. This means that statisticians are expected to know the basics of databases, data management and data access. Many companies ask their statisticians to implement code to be able to perform highly specific data analysis tasks. This coding goes beyond the traditional statistical software packages such as SAS or R, and also involves other modern programming languages (e.g. Python, Perl, ...). The Computational Statistics main subject offers a balanced curriculum with course units on statistical data analysis methods as well as on databases and programming skills. This main subject aims particularly at students with solid computer skills and an aptitude for algorithmic thinking. During the second term, the students work on their Master’s dissertation. The Master’s dissertation is a unique opportunity for students to learn first-hand from an experienced statistician how to apply statistical methods to solve real-world problems. This is an important component of the programme. Students report on their research methods and results orally and in writing.
Labour Market
Students who finish the Master's programme successfully, have acquired an advanced level of statistical knowledge and data analytical skills. As independent experts, they are ready to contribute to multidisciplinary teams that design, perform, analyse and report on applied scientific research. Our graduates are in high demand in the industry, the banking sector, in government, in academia and research centres (in the profit as well as in the non-profit production sector). Our graduates are trained to handle practical problems in an objective scientific manner, and to gain insight into data structures and the underlying data models. Throughout the programme, we encourage their critical thought and creative problem-solving skills. Computational skills, flexibility, efficiency and a positive attitude towards lifelong learning are important qualities and indispensable for a successful career.
Quality Assurance
At Ghent University, we strive to educate people who dare to think about the challenges of tomorrow. For that purpose, we provide education that is embedded in six strategic objectives: Think Broadly, Keep Researching, Cultivate Talent, Contribute, Extend Horizons, Opt for Quality.
Ghent University continuously focuses on quality assurance and quality culture. The Ghent University's quality assurance system offers information on each study programme’s unique selling points, and on its strengths and weaknesses with regard to quality assurance.
More information:
Unique Selling Points
- Requirements: our unique one-year advanced Master’s programme is open to holders of any Master’s degree in an empirical science discipline. We build on our students’ scientific attitude by adding a layer of strong statistical knowledge and skills.
- Curricular freedom: our students have considerable curricular freedom to tailor the curriculum to their own field(s) of interest (main subject Statistical Science and main subject Computational Statistics) and prior education. It is also possible to stagger the curriculum across several year.
- Bridging theory and practice: we focus on understanding and applying statistical concepts and bridging the world of statistics and that of the empirical sciences. We train our students in modern statistical methods with a strong emphasis on applications.
- Multidisciplinarity: we train scientists to become responsible and professional statisticians with the ability to work in multidisciplinary teams (of professionals in biology, engineering, mathematics, medicine, psychology, …). We equip them with creative problem-solving skills, a firm basis of statistical thinking and knowledge of modern statistical methods and their applications.
- Multitalented graduates: students who successfully finish our programme have acquired an advanced level of statistical knowledge and data analysis skills. As independent experts, they are ready to contribute to a multidisciplinary team that designs, performs, analyses and reports on applied scientific research. On a labour market that clamours for statisticians and data scientists, our multitalented graduates are in high demand.
Strengths
- Our professorial staff consists of motivated lecturers who combine their passion for teaching with excellent research records. All of them are embedded in international networks.
- Our programme boasts a multidisciplinary team of lecturers from different faculties, and offers a wide range of electives. This multidisciplinarity is what makes our programme so attractive for students coming from a variety of disciplines.
- Our programme offers a modern perspective on data analysis, owing to the professionality of the teaching staff, who are all active in research, and as a result of the programme’s regular interactions with the industry and leading research institutes. All of these parties are also actively involved in shaping our programme (e.g. via the annual ‘MaStat Day’).
- Our programme focuses on understanding and applying statistical concepts, with many hands-on sessions and authentic at-home assignments as well as project assignments. This ensures that our graduates are well-prepared for national and international jobs in the private sector as well as in government and in academia.
- Our professorial staff and study programme committee guarantee a continuous focus on quality assurance..
Challenges
- Improved communication: we strive to make the information about the programme’s expectations (positive job outlook, study load, mathematics entry level, programming and communication skills, …) more accessible to prospective students by improving the MaStat website and other communication channels.
- Study efficiency: we strive for higher study success rates. On the one hand, we want to achieve this by tightening our admission requirements. This is the only way to make sure that incoming students possess the necessary entry levels with regard to software (R), calculus, and basic probability and statistics. We have taken initial remedial actions in the form of an admissions test and reductions in study pressure/workload, and found them to be successful. On the other hand, we aim for higher success rates by actively encouraging our students to take a start with their dissertation earlier in the academic year. This should prevent them from discontinuing the programme prematurely, i.e. after finishing all the course units but before completing the Master’s dissertation. Decisions to discontinue the programme at such a late stage are due to the high demand for data scientists and statisticians on the labour market. Although this may be attractive in the short term, we believe that it comes at the students’ disadvantage in the long run.
- External perspective: we strive to embed the perspective of academic peers involved in similar international programmes in a more regular and systematic manner.
This study programme is accredited by the Accreditation Organization of the Netherlands and Flanders (Dutch: NVAO). Accreditation was extended following the positive outcome of the institutional review in 2022. Programme quality was validated by a quality review, i.e. a screening of the Education Monitor by the Education Quality Board. The Quality Assurance Resolution (in Dutch) can be found here.
This information was last updated on 01/02/2023.
In case of questions or suggestions with regard to the publicly available information, please contact the study programme.