...Testimonials We caught up with 3 graduates from the latest Accelerator cohort. They spoke about why they chose the Accelerator, how the programe helped them, their experiences of being mentored...
...data science open, available, and accessible enables scrutiny from Parliament, the public and peers, and enables reuse of code. We work in challenging contexts and our analysis needs to be...
...a post-COVID world”. The festival is an opportunity to bring together data science practitioners from across the public sector to share their work and experiences with each other. It’s also...
...However, this becomes more complex when we start to consider interactions between fields, or different types of data such as free text and GPS locations. An example of the type...
...presented a pilot app using machine learning to assist triaging in accident and emergency. Queen’s Hospital sees over 290,000 emergency department admissions per year. The app aims to support NHS...
The Homelessness and Troubled Families Analysis Team at MHCLG have recently published the latest 2019 Rough Sleeping Snapshot statistics using a Reproducible Analytical Pipeline (RAP). This felt like a real achievement as we are one of the first teams in …
...by mobile phone network across the railways. In conversations with people on different mobile phone networks I had never twigged that the degree of signal loss I experience is not...
...GOV.UK, to building better A/B testing for analysts that resulted in a HackerNews featured blog. Several of the projects themselves and the knowledge gained were brought into 80% work straight...
...as Amazon Web Services (AWS) S3 buckets. They set up processes to clean and manipulate the data by writing version-controlled code (for example, using Python) that automatically provides us with...
...to make an analogy – is simply a computer in the cloud. You can use it to install code libraries, run your machine learning and store the results somewhere. Virtual...
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