
Register for the launch event on 29 March 2021 here (registration has passed). Click here to visit the dashboard.
Visit our What Women Want dashboard!
Fast Data Science - London
Fast Data Science has been working on a digital Natural Language Processing dashboard for White Ribbon Alliance (WRA). WRA is a global nonprofit which promotes reproductive, maternal and newborn health and rights.
In 2019, WRA did a survey called What Women Want, where they asked over a million women and adolescent girls around the world:
What is your top request for your maternal and reproductive healthcare?
From 25 March 2021, you will be able to explore their responses on our interactive dashboard, powered by Plotly Dash and Python and running on Google Cloud Platform.
Using bespoke Natural Language Processing, we have categorised their responses into 39 categories, such as Water, Sanitation, and Hygiene (WASH). Using our dashboard, you can see clearly how survey responses in natural language are related to age, location, and type of ask. We even have an N-gram explorer view (unigrams, bigrams, trigrams).
Please click here (registration has passed) to register for the launch event on 25 March at midday UK time.
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Thomas Wood presents the Clinical Trial Risk Tool before the November meeting of the Clinical AI Interest Group at Alan Turing Institute The Clinical AI Interest group is a community of health professionals from a broad range of backgrounds with an interest in Clinical AI, organised by the Alan Turing Institute.

Fast Data Science will appear at Ireland’s Expert Witness Conference on 20 May 2026 in Dublin On 20 May 2026, La Touche Training is running the Expert Witness Conference 2026, at the Radisson Blu Hotel, Golden Lane, Dublin 8, Ireland. This is a full-day event combining practical workshops and interactive sessions, aimed at expert witnesses and legal professionals who want to enhance their expertise. The agenda covers critical topics like recent developments in case law, guidance on report writing, and techniques for handling cross-examination.
Guest post by Alex Nikic In the past few years, Generative AI technology has advanced rapidly, and businesses are increasingly adopting it for a variety of tasks. While GenAI excels at tasks such as document summarisation, question answering, and content generation, it lacks the ability to provide reliable forecasts for future events. GenAI models are not designed for forecasting, and along with the tendancy to hallucinate information, the output of these models should not be trusted when planning key business decisions. For more details, a previous article on our blog explores in-depth the trade-offs of GenAI vs Traditional Machine Learning approaches.
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