Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
For a while, we have been running a blog series on cognitive biases and logical fallacies that data scientists should avoid. In this final blog on the subject, we look at some of the other logical fallacies and how they might crop up in data analytics.
With LinkedIn usage growing by two new members every second, you simply can’t afford to not be on the platform. Founded in 2003, LinkedIn has 590 million users with 260 million of those active every month.
The EQ Behind the IQ - Jaco Rossouw from PrincipaDecisions
For a while, we have been running a blog series on cognitive biases and logical fallacies that data scientists should avoid. In philosophy there are a host of informal logical fallacies – essentially errors in thinking – that crop up every day. In this series we have looked at the practice of data science to determine how these same fallacies also occur. Today we will be looking at fallacies and their manifestation in credit: The Monte-Carlo fallacy and the Hot-hand fallacy with some studies in the credit world.
Recently my team and I were sitting in a meeting with a potential client debating the basic functions of our originations software. To the business analysts who were leading the RFP process, the most critical feature seemed to be whether or not our solution would be able to offer web form fields that were customisable by the business user.