data analysis in Water ENgineering & management
In modern water management, observational data play a crucial role in understanding, predicting, and managing the behavior of water systems. With the increasing use of monitoring technologies, large and complex datasets are now widely available, offering valuable insights for both research and operational decision-making. This course provides you with the analytical tools and strategic thinking required to extract meaningful information from real-world water system data. It introduces a selection of commonly used data analysis techniques applicable to time series, spatial, and multivariate datasets in the field of water engineering and management. Special attention is given to the practical challenges posed by imperfect data—such as missing values and outliers—which are frequently encountered in professional practice.
Beyond technical methods, the course emphasizes the development of a systematic approach to data investigation: selecting appropriate analytical methods, critically assessing data quality, and interpreting results with an awareness of their limitations. The goal is to empower participants to make informed, transparent, and reliable decisions based on observational data.
Key Topics Include:
- Data quality assessment and handling of outliers and missing values
- Selection of appropriate analytical techniques based on data and objectives
- Application of core methods for time series, spatial, and multivariate data analysis
- Interpretation of results in the context of water system behavior and data limitations
- Clear and effective communication of analytical findings
By the end of the course, you will be able to:
- Critically evaluate the quality of a dataset and address common data issues (e.g., missing values, outliers)
- Select and justify appropriate data analysis techniques for different types of datasets and questions
- Apply a range of commonly used techniques at a non-expert level to time series, spatial, and multivariate datasets
- Interpret analysis results with consideration of dataset limitations and underlying assumptions
- Clearly present and explain the findings of a data investigation in a professional and transparent manner
For whom? Professionals with a (HBO) degree in Civil Engineering with a basic skills in programming and knowlegde of statistics
When? 10 November 2025 - 19 December 2025, with on campus lectures each week
More in depth information on the course in our online course catalogue Osiris : link to osiris course information
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