Understanding of key data analysis concepts such as data collection, pre-processing, exploration and interpretation;
Understand the difference between quantitative and qualitative data and between structured and unstructured data sources; Apply techniques for dealing with missing, duplicate and erroneous data;
Understand the importance of data quality and the impact of data pre-processing on subsequent analyses;
Create meaningful visual representations of data to detect patterns, trends and outliers, and use appropriate visualisation tools;
Ability to interpret data visualisations in order to draw inferences, draw conclusions and make informed decisions;
Apply basic statistical methods to data analysis and recognise the significance and limitations of the results of statistical analyses;
Apply basic and more advanced machine learning algorithms to data and understand and evaluate learned models;
Understand data mining process models and pipelines;
