Module Leader

Dr Michel Cardin

Dr Lorenzo Picinali

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 statistics and machine learning.
The main topics include the basics of data analysis, statistics, and advanced data science.
During the whole module, tutorials will be structured around case studies that are appropriate for Design Engineering students, such as social media activity analysis.         

Learning Outcomes

On completion of this module, students will be better able to:

  •   Apply basic descriptive and inferential statistical analysis and data visualisation techniques
  •   Apply simple machine learning techniques
  •   Interpret the results of such methods and techniques, and report them appropriately
  •   Solve practical problems using different analytical techniques
  •   Apply such methods using Python and Matlab            


Description of Content

Basic of data analysis:
  Dataset features
  Introduction to descriptive statistics
  Introduction to inferential statistics
Advanced data science:
  Supervised learning: classification
  Overfitting and feature selection
  Train, test and validation sets