Module Leader

Dr Lorenzo Picinali

+44 (0)20 7594 8158

Learning Outcomes

On successful completion of the module, students should:
  • be able to use basic visualisation techniques, statistical analysis and machine learning methods and techniques, and to evaluate in which situations and conditions these are best applied
  • be able to interpret the results of such analysis, methods and techniques, to report them appropriately and to discuss the findings
  • be able to solve practical problems, where appropriate, using different analytical techniques
  • be able to implement such methods using Python and Matlab, apply them to appropriate case studies and adequately present the results


Description of Content

The module aims to provide students with sufficient tools and techniques to explore small and large datasets, to perform data analysis and to use key insights from data mining.

The main topics include:

  • Basics of data analysis, including: correlations and what to ask about your dataset (source of the data, bias, outliers, measurement errors, etc.)
  • Statistics – descriptive and inferential, parametric and non-parametric.
  • Advanced data science:

    ·         Supervised learning and predictive modelling
    ·         SVM and kernel methods
    ·         Rule-based learning
    ·         Brief introduction to deep learning  

During the whole module, tutorials will be structured around case studies that are appropriate for Design Engineering students, such as social media activity analysis.