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

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

At Elastacloud, it is important for us to let people know what goes on on a day to day basis. We use social media tools (e.g. Twitter), Channels and other avenues to showcase what the awesome stuff we

I just rounded up a project with a client that centred around Natural Language Processing. One of the things I enjoy about working at Elastacloud is the diversity of projects on a week to week basi

I am a self confessed convert to the tidyverse way of doing data import, tidy and analysis in R, making use of packages such as stringr, dplyr and purrr every day. However, I have recently found th

My last post demonstrated a successful use case of executing R scripts as a stored procedure on SQL Server . The main advantage of using R in SQL Server is that is moves intelligent analytical cap

Every time that you start a new project in Data Science, you need to create several folders to locate the different inputs and outputs of your project. So, for each project, you need to decide wher

Enriching data sets is common activity carried out in data science projects, whereby new data sources such as demographic, social media, geospatial and weather data are obtained to provide better i

As it approaches a year since I joined ElastaCloud as a Data Scientist I have been thinking about how working here compares to my previous life as a research associate in academia. There are many diff

I stumbled upon a package in R just last week called the xda package. Typically in R, there are several ways of performing exploratory analysis. Data scientists know that the initial look into your da