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SkillsCast

Transforming a Museum to be data-driven using R

15th March 2017 in London at CodeNode

This SkillsCast was filmed at Transforming a Museum to be data-driven using R

Skillscast coming soon.

How do you transform a traditionally un-data-orientated business into being data-driven armed with R, data science processes and plenty of enthusiasm? Alice Daish shares her experience of transforming the 250-year-old British Museum to be data-driven by 2018.

With the exponential growth of data, more and more businesses are demanding to become data-driven. Seeking value from their data, big data and data science initiatives; jobs and skill sets have risen up the business agenda. R, being a data scientists’ best friend, plays an important role in this transformation. But how do you transform a traditionally un-data-orientated business into being data-driven armed with R, data science processes and plenty of enthusiasm?

The first data scientist at a museum shares her experience on the journey to transform the 250-year-old British Museum to be data-driven by 2018. How is one of the most popular museums in the world, with 6.8 million annual visitors, using R to achieve a data-driven transition?

• Data wrangling

• Exploring data to make informed decisions

• Winning stakeholders’ support with data visualisations and dashboard

• Predictive modelling

• Future uses including internet of things, machine learning etc.

Using R and data science, any organisation can become data driven. With data and analytical skills demand higher than supply, more businesses need to know that R is part of the solution and that R is a great language to learn for individuals wanting to get into data science.

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Transforming a Museum to be data-driven using R

Alice Daish

Alice Daish is the Data Scientist at The British Museum focusing on making the museum data-driven by 2018. Co-Founder of R- Ladies Global and mentor at R-Ladies London. Registered Scientist previously trained in ecology and quantitative biology. Interests include R and data science, datafication, data analysis, predictive modelling, data visualisation, data communication, gender diversity in STEM