Data science as a team sport
Posted July 24, 2017
By Vicky Lucas
Training Development Manager of the IEA
Our champion data team
In my view, the technical strength of the IEA data team comes from the combination of excellent domain scientists along with top software and computing skills. This combination of talents has produced an excellent data science team who tackle environmental data and provide solutions for environmental issues in both an innovative and thorough way.
I joined the IEA in 2015 as it was established out of the School of Mathematical, Physical and Computational Science at the University of Reading, with its foundation in the domain sciences of Earth observation, weather and climate, along with a proven e-research strength.
IEA data team’s skills put us top of the league
A quick look at our team today reveals expertise in data-driven decision support, designing software with intuitive interfaces and accessible visualisations, programming in high and low level languages, administrating e-infrastructure, data intensive analysis and software engineering. And me, I’m the training manager, interested in the talents within our IEA team and the skills gaps out there in academia and industry necessary to enable efficient data intensive research and development.
The world of data science evolves rapidly and whilst to be broadly aware of a spectrum of technologies, standards and methods is essential, to be expert in all would be impossible. The IEA team combines individual expertise with good communication and an overriding sense of curiosity and innovation that enables cross-fertilisation and develops solutions.
The EDISON Project
The EDISON project is a two-year European undertaking to build the data science profession, concluding later this year. The outputs of the project include a Competences Framework and a Model Curriculum, encapsulating the essential skills and knowledge and thoroughly researched and referenced from a range of sources. The EDISON model curriculum has been helpful to separate skills into manageable sections:
- Data Analytics
- Data Management
- Data Engineering
- Data Science Research Methods
- Business Process Management
- Domain Knowledge
In my role as provider of training activities and skills advice to PhD cohorts, industry and science funders, I have used the EDISON curriculum as a framework. It is useful to be able to see all the facets of data science, to see them catalogued by the EDISON work and to establish how individual training activities would fit into the framework. I also consider if they are training activities that the IEA technical team would value.
Decentralisation seems to be gathering pace in many domains. Monoliths were a feature of the 20th century and the 21st is increasingly characterised by distributed systems, from computing to energy production even collective collaborative leadership to engage and deliver across organisational boundaries. And data science is no exception, it’s building a team across the full range of complementary skills that counts.
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