GB-Salisbury: Could you or somebody you know win the Data Science Challenge

As an influential person in the data science community I thought that you, or one of your followers, would be interested in getting involved with the UK Government's sponsored Data Science challenges.

The Data Science Challenges will launch on 3rd April 2017 and are sponsored by the Defence Science and Technology Laboratory (Dstl) as well as a number of other UK government departments. The challenges are designed to encourage the brightest minds in data science to help solve real-world problems. Entrants will also be able to track their progress using live leader boards. The top three entrants in each challenge will share a £40,000 prize fund.

The Data Science Challenge begins with two separate competitions that will test the participants' ability to mine large unstructured datasets to extract useful information:

• SAFE PASSAGE: DETECTING AND CLASSIFYING VEHICLES IN AERIAL IMAGERY

Being able to automatically detect and categorise vehicles in aerial imagery will dramatically improve how quickly we can assess and identify them. This challenge asks participants to detect and classify vehicles such as buses, cars and motorbikes, from a set of aerial images.

• GROWING INSTABILITY: CLASSIFYING CRISIS REPORTS

Analysing data in documents such as media reports can provide a better understanding of a potential crisis situation, growing instability in a particular region or, specific theme such as terrorism. Using news material, this challenge asks participants to predict topic tags for classifying unseen reports so that they can be used to improve awareness and understanding.

The challenges are open to all data scientists (unless working for a challenge sponsor or supporting organisation). We would be very grateful if could please promote details of the Data Science Challenge within your network and community. Please let your network know about this great opportunity. To register or share, the link is https://www.datasciencechallenge.org/. Solutions can be submitted between the 3rd April and 17th May.

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