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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This new video explains natural language processing: what it is, how it works, and what can it do for your organisation. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that focuses on giving computers the ability to understand human language, combining disciplines like linguistics, computer science, and engineering.
This free A/B test calculator will help you compare two variants of your website, A and B, and tell you the probability that B is better. You can read more about A/B testing in our earlier blog post on the subject. You may also be interested in our Chi-Squared sample size calculator which will help you calculate the minimum sample size needed to run a Chi-Squared test, given an expected standardised effect size.
See also: Fast Data Science A/B test Calculator (Bayesian) A/B testing is a way you can test two things, Thing A, and Thing B, to see which is better. You most commonly hear about A/B testing in the context of commercial websites, but A/B testing can be done in a number of different contexts, including offline marketing, and testing prices.
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