The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

Thomas Maydon

Thomas Maydon

Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

Recent Posts:

Key Considerations When Managing Your IFRS 9 Forward-Looking Overrides

As part of the group that was the second company worldwide to become IFRS9 compliant, IFRS9 has been at the forefront of what we do.  We have assisted nearly 20 companies on their IFRS9 journey over the last two years.  This blog forms part of a more extensive series on IFRS9. In this blog, we explore the administering of management overrides.

How To Dodge The Simpson's Paradox In Descriptive Analytics

The Simpson's Paradox is a phenomenon in statistics illustrating how easy it is to misinterpret data. (Click to Tweet!) It occurs mainly in descriptive and diagnostic analytics (see our blog on the different types of analytics) where an analyst may jump to a conclusion driven by motivated reasoning and not by objectively assessing the evidence.

5 Correlation Types In Data Science And How To Not Fool Yourself

As part of our blog series on cognitive biases and logical fallacies that data scientists should avoid, today we address a prevalent logical fallacy: the "correlation proves causation" fallacy. Correlation due to causation is just one of the five main categories of causation, and this blog will look into each of the five.

P-Hacking - Are You Guilty Of Data Fishing?

A year ago, I published an article about motivated reasoning and how that can damage the data analytics process. It is part of a blog series on cognitive biases and logical fallacies that data analysts should avoid. Today I’d like to extend this conversation into a topical matter: p-hacking, also known as data fishing.

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