Technical Blog from our Data Science Team

Read the latest technical blogs from our data science team.

Can we completely automate forecasting for industry?

Guest blog by Nick Finch, CTO at DataPA Ltd We’re delighted to be able to bring you a technical blog from another of our speakers who would have been speaking at DataTech20 back in March.   To do any sort of business planning, you need forecasting. Our reason for existing is to help our customers

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Digital security and how not to do it

Guest blog by Colin Gillespie, Data Scientist at Jumping Rivers Colin was due to speak at DataTech20, which was unfortunately cancelled due to COVID-19. We’re delighted that he has put together this brilliant blog on digital security for us. How not to do security Digital security is everywhere. Unfortunately, bad digital security is also everywhere. Take

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Understanding Customer Behaviour Using Uplift Modelling

Guest technical blog by Garry McFarlane, Senior Analyst and Dr. Paul van Loon, Head of Analytics, both at Forecast. Garry and Paul were scheduled to speak at DataTech20, and have kindly presented us with this blog instead. Enjoy.   What is uplift modelling? Targeted marketing is so commonplace, nearly synonymous to online marketing, that consumers

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Highlights from NVIDIA’s Online GPU Technology Conference

The rise of deep learning has led to an ever increasing need to speed up computations when processing large amounts of data. If you are a data scientist or a machine learning engineer, you probably know that NVIDIA’s hardware is playing a major role in this AI revolution, as all the fancy deep learning frameworks

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A Brief Profiling of Data Professions

Do you recall how many times you’ve read articles titled “This is what a Data Scientist does” or “Differences between a Data Scientist and a Data Analyst”? Such articles usually come with various colourful (and sometimes funnily shaped) Venn diagrams, arbitrarily presenting the overlap of the various data professions and highlighting the distribution of different

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Synthetic data in machine learning

 Machine learning algorithms are currently applied in multiple scenarios in which unbalanced datasets or overall lack of sufficient training data lead to their suboptimal performance. For example, approaches focusing on disease prediction are often affected because data in the health sector is generally difficult to acquire and disease training examples are limited. Fraud detection in

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Using Shiny for interactive displays of health data: The Scottish Burden of Diseases

The Accelerator programme run by The Data Lab between 19 April 2018 – 06 September 2018 was a Scottish Government collaborative project, open to employees of the Scottish Government, the Information Services Division, the National Records of Scotland and Registers of Scotland. Employees applying to take part had a background in statistics, economics, operational research and social

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