Hidden Insights
Experience the possibilities with Business Analytics
In our last blog we explored the potential impact of missingness in data in terms of its impact on models which require complete case analysis. We took a simple view that data was missing with an equal, independent, probability for any given model input. This week we explore cases where
During the pandemic, millions of people have made the leap to digital banking. Identity analytics can help banks provide a delightful customer experience while keeping fraudsters out in the cold. Fraud is often seen as a cost centre for banks, but there’s an opportunity for fraud teams to become a
As a huge road cycling fan, one of my favorite Olympic events is track cycling. With its speed and intensity, it is such a captivating set of events. While some events are sprint-focused and others are more endurance-focused, they all require powerful physical abilities, great bike-handling skills, cunning tactical expertise
In the previous section of this series we discussed ways of assessing the relationship between variables. This week we change the focus to the shape and sparsity of our dataset. One area of Explanatory Data Analysis which we’ve missed so far is the impact of missingness in data. Having missing
„Ich wusste gar nicht, dass man bei Ihnen einfach so anrufen kann.“
In the previous section of this series we looked at basic summary statistics. In this article we start to consider the relationships between variables in our dataset. As part of your Explanatory Data Analysis it is worth looking for correlation between variables. Generally, when referring to correlation we mean the