Setting up a Data Science Practice: Analytics Processes

In this third post on setting up a data science practice, I address some of the analytics processes that need to be in place to maximise value from analytics. After more than two decades of practice and development, there are now well- established data analytics frameworks like the Cross Industry Standard Process for Data Mining. … More Setting up a Data Science Practice: Analytics Processes

Setting up a Data Science Practice: People Dimension

In the previous post, we discussed the key principles of setting up a data science practice. In this post, we’ll discuss the people dimension. One should read the below as suggestions, not prescriptions. There is more than one way to set up a data science practice. Critical to the success of a data science practice are … More Setting up a Data Science Practice: People Dimension

Setting up a Data Science Practice: Fundamental Principles

I have been involved in the setup of several data science practices in both industry and government. Here are a few key principles I use in establishing a data science practice. Principle 1: Building a predictive enterprise is, first and foremost, about building a human infrastructure. Many companies mistakenly believe that analytics is primarily about software … More Setting up a Data Science Practice: Fundamental Principles

Customer Lifetime Value and Its Application in Retail Analytics

Customer Lifetime Value (CLV) is a relatively new framework stemming from the idea of “treatment of customers as an asset”, in use at innovative companies like Harrah’s, IBM, and Capital One. The definition is a fairly natural one: CLV is the net present value of profit from all the future purchases a customer is going to … More Customer Lifetime Value and Its Application in Retail Analytics

Lifting the Fog on Machine Learning Maths

Confession: As a computer scientist, I have always been comfortable with discrete mathematics. However, continuous maths, especially the type commonly seen in statistical machine learning, have always been a challenge for me. In fact, I lived through the last 15 years of my professional life in a more-or-less constant fog of partial understanding when it … More Lifting the Fog on Machine Learning Maths

Privacy Preserving Outlier Detection: A Tutorial

Outlier detection is an important tool in risk modelling. In the context where data are distributed across multiple locations and data privacy is a concern, we need to start looking at privacy-preserving techniques for doing outlier detection. Linked here is a tutorial introduction to this topic I recently prepared. Privacy Preserving Outlier Detection The presentation … More Privacy Preserving Outlier Detection: A Tutorial