Frequently Asked Questions

Apart from the theoretical modules, the course consists of research modules. You have to apply the acquired knowledge to solve 'wicked' and multisource multidisciplinary problems' in the research modules. The research cases are coming from real life cases, for example from an academic hospital or the agro-industry. Students work together on projects with peers, lecturers and stakeholders on such a research project where the outcome of the (research)case is not clear. The solution asks for creativity, critical thinking, teamwork and knowledge creation. You learn to apply knowledge as well as skills.

We have learnt that it is quite difficult for students to learn about programming and data science at the same time when they start without any programming skills. This is frustrating for you as well as your fellow students. Therefore we will ask you to practice these skills upfront so that you acquire basic skills before starting the programme. The minimum level is described in these documents: entry requirements Data Science, entry requirements programming and entry requirements biology.

Our experience is that, when you are familiar with one program language, it is easy to learn another. Note: Excel and HTML are not program languages. But R, C#, Java, C++ are. The principles and fundaments are the same. Knowing another program language is a good basis to start with.

You probably have a sound mathematical background which is very useful for this Master course. To assess whether you are eligible for this Master you can apply via studielink and upload your transcript of records. Furthermore, you can send a portfolio with demonstrated proof of acquired skills to the admission committee. See also the Admission Requirements.

There are three preparatory courses. A course for programming, a course for biology and a course for data-science. The courses take place from September till the beginning of November besides your other classes. The courses are intended for students without a sufficiently sound or broad background in life sciences. The courses serve to provide you with the required basics related to calculus, linear algebra, data analysis, programming, and biology which are necessary to successfully commence with the next modules. The preparatory courses are considered optional when the student has proven to already have the required entry level.

Omics

For the field of Omics you can take a look at the following book: https://openstax.org/details/books/biology-2e. You can also test your knowledge at this website.

Data Science

Examples of machine learning and data science code can be found on https://www.kaggle.com/.

Maths, statistics and linear algebra

A good website to practice maths, statistics and linear algebra is https://www.khanacademy.org/. If you search for math, statistics en linear algebra you will find good courses, which are a combination of videos and exercises.

There are many books on maths and statistics, and almost every book that explains the basics is fine. For statistics the book Statistics for data scientists: 50+ essential concepts using R and Python is a good start. If you are really a maths-lover you could look into books like Calculus.

"Hands-on Signal Analysis with Python: An Introduction", Thomas Haslwanter, 2021, Springer, ISBN: 9783030579029 (hardcover) / 9783030579036 (e-book), https://www.springer.com/gp/book/9783030579029 

"Python for data analysis: Data wrangling with Pandas, NumPy, and IPython", Wes McKinney, 2017, ISBN: 9781491957660 (paperback), https://www.oreilly.com/library/view/python-for-data/9781491957653  

Downey, Allen B. Think Bayes. " O'Reilly Media, Inc.", 2021. https://greenteapress.com/wp/think-bayes/