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 cloud. AML provides a platform to develop, train, test, deploy, manage, and track machine learning model

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 codes as this gives the audience the confidence that my codes work and is repeatable. The challenge wit

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 handle the presence of missing values , when your data is limited in size and you want evaluate the

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 us do these steps sequentially via a simple interface. In other words, Pipelines enables us to chai

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 Science. However, back then I had only gone through online examples and tutorials. If you have any experie

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 Server R Services is an in-database analytics feature that tightly integrates R with SQL Server. With thi

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 there is a better, more intuitive way? Wonder no more! There is an amazing package that can help you be

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 a lot of geospatial data which has meant that I’ve been putting some time into learning about plotti

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 product for each input node and associated weights is computed. In addition, to feeding data throug

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 do at work. As a data scientist at Elastacloud, I could not help but try to do some analysis on our

Recently I have been applying a range of different time series models to rich transport industry data. From anticipating the development of faulty parts to understanding the flow of traffic and how to better strategize timetabling adjustments, time series modelling is extremely powerful at improv

If you are involved in the data science/AI space, you definitely heard of Deep Learning and how it can be used to solve all our problems. From computer vision to language translation, Deep Learning is rapidly changing the Machine Learning and Artificial Intelligence field with solutions to pro

Extreme Gradient Boosting (xgboost) is a very fast, scalable implementation of gradient boosting that has taken the data science world by storm, with xgboost regularly online data science competitions and use at scale across different industries. Xgboost was originally developed by Tiangi Chen

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 basis. One week you may be working on building models for optimising energy usage and another week you c

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 that I have been running into significant performance problems with larger data sets, particularly whe

If you are learning R or Python, you may be interested in knowing more about different platforms that you can use to write your code and run it. When you are starting, your preference would be to get everything in your local machine, so you don't have to deal with anything else than installing the

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 capabilities closer to the data, thereby overcoming issues associated with big data such as moving, mer

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 where you will store your datasets, your scripts, your images, etc. and also how you will name all these

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 insights into specific scenarios. At Elastacloud we have seen enhanced model performance with the add

The new version of Azure Machine Learning has been released; with a desktop application as well, the Azure ML Workbench. One of the standout features for me is the ease in which data can now be quickly wrangled from a raw form into the correct form for a data scientist to start using. Transformation

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 differences that I have seen but also some similarities that I have found surprising. If you're reading

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 dataset can provide a quick insight into what the dataset looks like and also give you some idea of ho

As a data scientist parallel computing may seem alien to us, as we're usually not from computing backgrounds, the idea of optimising our code for performance can seem quite a daunting task. However, if you are running into performance problems whilst using R, perhaps trying to train multiple machine