A British artificial intelligence firm involved in the Vote Leave campaign has been handed a £400,000 contract to tap data from places such as social media sites to help steer the Government's response to Covid-19. Official documents from the Government show Faculty Science was awarded the contract by the Ministry of Housing, Communities and Local Government (MHCLG) in April to provide data scientists who could set up "alternative data sources (e.g. They would, the contract said, apply data science and machine learning to the data, which could help identify trends, and then develop "interactive dashboards" to inform policymakers. It is understood the contract, awarded through the Government's G-Cloud framework, was designed to address an urgent need for the department to analyse real-time data and monitor the effect of Covid-19 on local communities. Faculty's AI technology can be used to process vast amounts of data and in the past was used for polling analysis by the Vote Leave campaign, run by Boris Johnson's adviser Dominic Cummings.

With a new year upon us, let's take a fresh look at the current state of the data science puzzle. What are the most important constituent concepts of the data science landscape? How do they fit together? Which of these have been elevated in importance since the previous installment, and which are less important? As a few years have passed since I last treated this particular topic, it might be worth having a look at this out of interest, and for comparison.

Data science will also transform medicine. Already, IBM's Watson machine is crunching data from individual patient records to identify best courses of treatment. In the future, the impact of machine learning will expand to combat entire categories of disease. We imagine all biologic matter becoming sequenced eventually, with all resulting data residing in a database for computerized analysis. The insights that could arise from computer-powered pattern recognition in this kind of data are staggering.