1. Data Science: innovation for the public good
Peter Fullerton, ONS: 9 May 2019

2. Campus origins: The Bean Review, March 2016
“Although better use of [data] has the potential to transform the provision of economic statistics, ONS will need to build up its capability to handle such data.
This will take some time and will require not only recruitment of a cadre of data scientists but also active learning and experimentation.
That can be facilitated through collaboration with relevant partners – in academia, the private and public sectors, and internationally.”
Independent Review of Economic Statistics
Professor Sir Charles Bean, 2016, p.11

3. Our Journey so far Mar 2016: Funding announced in Budget
Oct 2016: Apprentices and first data scientist recruited, research commences.
Dec 2016: Prototype SDG platform launched, MOUs signed with ATI and individual universities
Mar 2017: Formal launch & 1st first international MOU
Oct 2017: First students on MDataGov course
July 2018: First Advisory Board meeting, offices open in London & Titchfileld, Civil Service Live training delivered to 560
Sept-Mar 2019: 30 data science projects completed or in progress
Mar 2019: Headcount reaches 65, degree-level apprentices start, faster indicators & two-year review published

4. Growing Data Science Skills
Level 4 Apprenticeship in Data Analytics:
Two-year programme, 12 months at the Campus followed by 2 x 6 month rotations across ONS
Level 6 Apprenticeship in Data Science:
Three-year programme, approved for England in 2018, launching in England and Wales in 2019
Data Science Faculty:
In-house training unit delivering operational courses in programming (R, Python) and fundamentals of Machine Learning, NLP etc

5. Growing Data Science Skills
Accelerator:
12-week mentoring programme for Government analysts delivered with GDS, in parallel with in- house ONS Data Science Academy
Masters in Data Analytics for Government:
2 year part-time MSc in Data Science and Statistics for government analysts, delivered by UCL, Oxford Brookes and University of Southampton. Expanding in 2020/21

7. Case study: Analysis of Automatic Identification System data to understand shipping and ports
Explore the operation, use and relationships between ships and ports in the UK.
These insights could be used by stakeholders to analyse port statistics and model port relationships to predict delays and maximise efficiency.
The Department for Transport is reusing part of our code to decode AIS messages and generate port statistics that are currently produced by an external organisation.
In addition to these direct impacts, the Campus developed its ability to work with big data. We are applying the learning from this to work on projects developing faster economic indicators, including analysis of trade in goods and estimates of gross domestic product.

8. Faster economic indicators
These indicators, published in March 2019 included producing a ‘diffusion index’ using early VAT returns to show whether, on average, UK businesses are seeing turnover grow or not.
We created an index which uses ship tracking data to monitor the level of traffic coming in and out of each UK port and an index using road traffic camera data to track the number of lorries on England’s roads.
These new measures were created to provide an early picture of changes in the UK economy to potentially aid economic and monetary policy makers and analysts in interpreting the economy.
Disclaimer: it is important to note that these indicators are not attempting to forecast or predict GDP or other headline economic statistics and the indicators should not be used in this way.

11. Data Science: innovation for the public good
Peter Fullerton, ONS: 9 May 2019