Championing Data Science

Microsoft have recently released an updated version of their Azure Machine learning service. At Elastacloud we have been using AML since the first release to deploy machine learning models to the clou

I have had the opportunity to speak at various DataScience MeetUp events in Nottingham, Loughborough and London. Typically, rather than use the usual PowerPoint presentation, I prefer running LIVE cod

Missing data is problematic for machine learning models. The majority of models do not run when missing values are present. Although there are a few algorithms (e.g. k-nearest neighbours) that can

As data scientists, time and again, we need to perform tasks which follow some form of sequence. A general example could be Extract data, Pre-process the data and then train a model. Pipeline helps

Shiny is an R package that makes it easy to build interactive web apps straight from R. I came across shiny apps early this year (2017) whilst looking at various ways of productionizing Data Scienc

The ability to execute R scripts on SQL Server provides data scientists with a powerful way to operationalise data science. In a Microsoft blog written earlier this year it was explained “SQL Serve

Have you ever used setwd("pathtofolder") at the beginning of an R script? Are you pasting folder names and paths into string to read multiple files from your folder? Have you ever wondered if the

Visualising data is an important part of the data science life cycle and any data scientist should know how to create good visualisations of different types of data. Recently I have been working with

Briefly, neural networks make predictions via forward propagation, whereby information advances in a single direction from the input nodes, through any hidden layers to the output nodes and the dot