After this course, the student is able to:
- Critically evaluate the quality of a data set and deal with any peculiarities (outliers, missing data, etc.) in a sound way;
- Evaluate on and use the relevant data analysis technique for given combinations of data set and question;
- Apply (at non-expert level) several common data analysis techniques for dealing with time series, spatial data, and multivariate data;
- Interpret and discuss the results of the selected set of analysis techniques, taking the limitations of the data set into account;
- Present and elaborate on the results of a data investigation in a clear and transparent manner.
Observational data are an important source of information for understanding and predicting the behaviour of water systems. Monitoring the state of water systems is becoming an increasingly wide-spread practice. Hence large datasets become widely available to benefit the management of these water systems.|
To extract information from data, a wide variety of analysis techniques and tools are available, each with its own merits and drawbacks. This course treats a selection of techniques commonly used in the field of water engineering and management. Since real world data sets tend to be imperfect (missing data, outliers), and the professional reality is that you have to select the most appropriate analysis method yourself, this course will also teach you a general strategy on how to properly perform a data investigation and interpret the results in a sound way.
The extent to which the student has achieved the learning objectives of the course is assessed by means of an assignment and a written exam at the end of the quartile.
Is knowledge of programming skills necessary for this course? Please specify the skills / knowledge of which programme(s) is / are needed.
Basic skills in Matlab programming are needed, The core of matlab scripts that are needed in the various exercises and the assignment are provided, but these scripts need to be understood and modified individually by the student.