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:
Correlations
Dataset features
Statistics:
Introduction to descriptive statistics
Introduction to inferential statistics
Advanced data science:
Supervised learning: classification
Overfitting and feature selection
Train, test and validation sets
Contact us
Dyson School of Design Engineering
Imperial College London
25 Exhibition Road
South Kensington
London
SW7 2DB
design.engineering@imperial.ac.uk
Tel: +44 (0) 20 7594 8888