Over the last two years, we have been working with a cutting edge electronics company to build advanced algorithms using R and Python. Since they are at the research & design stage of the development process, their data structure is unique and challenging.
Each week they generate thousands of data sets that have a spatial, temporal and experimental component. Their data structure is unique - around 10,000 voltage curves distributed on a disk. This necessitates the use of novel machine learning algorithms, coupled with classical experimental design.
In theory, each disk should be identical. In practice, this isn’t the case. The goal of the project was to develop a set of algorithms and R packages that would identify outliers and deviations from the canonical data. The ultimate goal was to optimise product yield. A novel challenge to this was that any issues detected were then fixed. In fact, our training data set consisted of issues that had now been resolved as they improved their processes.
Data from their systems was obtained via APIs. We developed R software to automatically assess the data quality and remove any rogue readings. Second, we created machine learning tools to automatically highlight any potential failures and outliers. Third, we proposed new experimental formulations.
This was a fast-moving project, where the data type was changing and the data sources were being updated. We developed adaptable software that the client is still using. As the client only has a small data science team, we provided on-site training and regular assistance after the project has finished.
Jumping Rivers created a data pipeline and visualisation dashboard for public and policy maker consumption. The aim was to increase transparency and encourage positive change to the workplace environment. The application allows exploration of key performance indicators stratified by a host of protected characteristics.
The client came to Jumping Rivers having already written the code for their problem in VBA. They were trying to evaluate four measurements for agreements with their clients. However, VBA is limited in speed. Jumping Rivers were required to build a bespoke R package to replace and quicken the code.
At the end of 2017, contacted Jumping Rivers. AGR TRACS International estimates the volumes of oil and gas in subsurface reservoirs. Their work involves combining a set of inputs for each reservoir layer (such as area, thickness, and up to five other inputs) – and then multiplying these inputs together.
Over the last two years, we have been working with a cutting edge electronics company to build advanced algorithms using R and Python. Since they are at the research & design stage of the development process, their data structure is unique and challenging.
After winning a Water Hub Hackathon, Jumping Rivers were contracted to create a platform for the client using R Shiny, published on RStudio Connect. The platform aggregates raw data from many third party sources both internal and external to the EA, through bespoke APIs, commercial databases, and asset management systems.
The client wanted to assess the viability of R and R Markdown as a reporting tool for creating complex, bespoke documents. We recreated sample reports for them in R Markdown, showcasing that all of their specifications could be met, and provided them with example code and training.
Jumping Rivers built a tool for creation of experimental risk assessments via a centralised web application. The dashboard allows for collaborative working during the data entry and assessment formulation, report generation and tracks versions along iteration of the process.
The IFoA required a bespoke R and RStudio platform to run their new R-based Actuarial CS1 and CS2 exams. We developed a system that delivered the exam and provided a convenient submission portal. Examiners could monitor results and feedback via a Shiny dashboard.
The client had developed an advanced, in-house Shiny application. We transformed the application into a commerical project, that is now being used by their clients.
In the spring of 2018, NHS Scotland expressed a need to move from their existing software, SPSS and SAS, to using R. The difficulty they faced was that there are over two hundred data scientists in NHS Scotland, which made training everyone in the new software a logistical challenge.
In 2018, Northumbrian held their annual Innovation Hackathon. Our winning entry, allowed enginneers to reduce leakage throughout the North East.
In spring of 2020 Northumbrian water engaged with Jumping Rivers to build a modelling solution to better understand risks to the consumer within their network in order to provide a better service.
