Our core capability is being able to understand and exploit data from a variety of sources. This requires a systematic approach to data exploration, preparation and processing to achieve the desired outcomes. Our particular expertise is in finding and combining the right data to solve customer problems, whether that be from satellite imagery, IoT sensors or bespoke data soruces.
We routinely find and exploit the right data sources for our customers. This involves detailed data analytics to explore data, preparing selected data sets and then applying appropriate algorithms to exploit the insight that can be gained. To achieve this we have a dedicated team of data scientists who work with customer, commercial and publicly available data to solve problems.
By applying a systematic approach to data science, we can quickly find and evaluate data sources to see if they help in solving customer problems. However, while being systematic is the key to squeezing the most out of data and understanding its usefulness, an influential aspect of data science is creativity. By being creative, our data scientists can look at a problem and explore different ways in which it might be solved. This leads us to different data sets that may be of use, and different techniques which may bring better accuracy or insight, from statistical analysis through to machine learning and beyond.
Of course, creativity is important, but being systematic is crucial. By exhaustively evaluating each step of our process, we ensure that the right data sets are selected, that they are evaluated fairly, that the data is pre-processed correctly and subsequently that each algorithm which we apply is then compared on an equal footing. Ultimately this means that our customers have confidence in our solutions and that we can accurately detail the performance of the algorithms we apply.
Al-Khalili, J., Smith, A., Sen, P. (2017) "Gravity and Me: the Force that Shapes our Lives" BBC 4 science programme using an iOS and Android app to measure local relativity effects
Arscott, D., Venturini, B., Cheong Took, C., Templeton, M., Babatunde, A., Casey, M.C. (2016) "Delivering Water Security for All During Shale Gas Production" A report co-funded by Innovate UK, DECC and NERC and undertaken by the PyTerra Research Consortium Download
Ioannou, P., Casey, M.C., Grüning, A. (2015) "Spike-Timing Neuronal Modelling of Forgetting in Immediate Serial Recall" Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2015. Killarney, Ireland: IEEE Download
Smith, M.I., Casey, M.C. (2012) "Solving the Big Data Problem - Smart Ways to Work Together" Big Data: Turning Big Challenges into Big Opportunities, IET Seminar, 05/12/2012 Download
Casey, M.C., Pavlou, A., Timotheou, A. (2012) "Audio-Visual Localization with Hierarchical Topographic Maps: Modeling the Superior Colliculus" Neurocomputing, vol. 97, pp. 344-356, doi: 10.1016/j.neucom.2012.05.015 Download
Casey, M.C., Yau, C.Y., Barfoot, K.M., Callaway, A.J. (2012) "Data Mining of Portable EEG Brain Wave Signals for Sports Performance Analysis: An Archery Case Study" International Convention on Science, Education and Medicine in Sport (ICSEMIS 2012). Glasgow, 19-24 July 2012 Download
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