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Data science course at (water management) Rijkswaterstaat

Nowadays data-driven working is very important within Rijkswaterstaat. A Variety of employees is involved in analyzing the data collected on daily basis by Rijkswaterstaat. The goal is to ensure that we construct an infrastructure in the Netherlands which will be safe, accessible and liveable in the future.

In order to have appropriate knowledge to their disposal, the Network Monitoring consultants of Rijkswaterstaat have requested Leiden University to provide them with a dedicated collection of Data Science knowledge and AI techniques in the form of a course of 5 sessions. All preparations for the first session had been made and all students were looking forward to starting on Tuesday 17 March. Unfortunately Covid-19 threw a spanner in the works. After intense and proper consultation, the course was converted into an online training course, in which the participants were taught twice a week, two hours at a time on the various Data Science topics for a number of weeks. The final result was received with very positive feelings. At the end of June 22 participants received their certificate as a token of successfully completing the course.

One of the participants, Marieke Honer expressed her feelings as follows: 'We have received a broad and valuable overview of what is usable and applicable from the findings in the world of Data Science in the Ministry of Rijkswaterstaat. In addition, we have also been given concrete guidelines on how to apply Data Science and work with it ourselves. The employees of Network Monitoring at Rijkswaterstaat immediately put their new knowledge into use at work. For instance they now apply the data visualization tips in their daily work. Moreover, the course has also inspired them to start new projects, for example in the field of Text Mining. '

Leiden Centre of Data Science

Every year, the LCSD provides approximately 4 Data Science courses for various government agencies. The courses focus on the technical aspect of Data Science and cover modules such as Machine Learning, Pattern Recognition, Data Mining, Factor Analysis, PCA, Dimension Reduction, Visualization and Network Analysis.

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