Data Analytics Capstone Project: predicting GDP and per-sector markets using alternative data

How can we use alternative data to gain a competitive edge?

This course is a detailed examination of how luminescence data (light emissions from industrial or materials production) can act as a proxy for macro variables like GDP or the performance of a sector.

Suitability

This course is for data analysts with some background in Python. It’s also suitable for asset managers, trading desks, structurers and others.

Learning Outcomes

Participants will be able to:

  • Describe the challenges of turning alternative data into a form amenable to analysis
  • Clean a dataset
  • Assess the statistical significance of correlations and other signals in the data
  • Optimise a model to fit and predict market price outcomes

Course Content

  • Overview: how luminescence data is gathered and challenges in ‘cleaning’ it
  • Participants decide how to clean, then clean a luminescence dataset that has outlier or missing values
  • Statistics recap: measuring significance with chi-squares, p-scores etc. Workshop: how is this done in Python?
  • Multidimensional regression modelling – which features or combination of features in the dataset can lead to a significant prediction of e.g. GDP or industry sector performance?
  • Workshop: find a significant correlation or predictor using Python
  • Concluding thoughts: was the 2020 recession predictable using these methods?

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