O nly four weeks to go until our official launch date of 28th October. It feels like it’s been a long build up but we
believe it will be worth the wait! In the meantime here’s a bit more information about the kind of things we do, why we are different
and what motivates us. If you’re interested please do get in touch.
What do we do?
T here’s a huge interest in data science at the moment. Businesses understandably want to be a part of it. Very often they assemble the ingredients (the software, the hardware, the team) but then find that progress is slow.
Coppelia is a catalyst in these situations. Rather than endless planning we get things going straight away with the build, learn and build again approach of agile design. Agile and analytics are a natural fit!
Projects might be anything from using machine learning to spot valuable patterns in purchase behaviour to building decision making tools loaded with artificial intelligence.
The point is that good solutions tend to be bespoke solutions.
While we build we make sure that in-house teams are heavily involved – trained on the job. We get them excited about the incredible tools that are out there and new ways of doing things. This solves the problem of finding people with the data science skill set. It’s easier to grow your technologists in-house.
The tools are also important. We give our clients a full view of what’s out there, focusing on open source and cloud based solutions. If a client wishes to move from SAS to R we train their analysts not just in R but in the fundamentals of software design so that they build solid, reliable models and tools.
We teach the shared conventions that link technologies together so that soon their team will be coding in python and building models on parallelised platforms. It’s an investment for the long term.
Finally we know how important it is for the rest of the business to understand and get involved with these projects. Visualisation is a powerful tool for this and we emphasize two aspects that are often forgotten: interactivity (even if it’s just the eye exploring detail) and aesthetics: a single beautiful chart telling a compelling story can be more influential than a hundred stakeholder meetings.
Why are we different?
O ne thing is that we prioritise skills over tools. There are a lot of people out there building tools but they tend to be about either preprocessing data or prediction and pattern detection for a handful of well defined cases. We love the tools but they don’t address the most difficult problem of how you turn the data into information that can be used in decision making. For that you need skilled analysts wielding tools. Creating the skills is a much harder problem.
Coppelia offers a wide range of courses, workshops and hackathons to kickstart your data science team. See our solutions section for a full description of what we offer.
Another difference is that we are statisticians who have been inspired by software design. We apply agile methods and modular design not just to the tools we build ourselves but also to traditional analytical tasks like building models.
Collaboration using tools like git and trello has revolutionised the way we work. Analysis is no longer a solitary task, it’s a group thing and that means we can take on bigger and more ambitious projects.
But what is most exciting for us is our zero overhead operating model and what it enables us to do. Ten years ago if we’d wanted to run big projects using the latest technology we’d have had to work for a large organisation. Now we can run entirely on open source.
For statistical analysis we have R, to source and wrangle data we have python, we can rent hardware by the hour from AWS and use it to parallelise jobs using hadoop.
Even non-technical tasks benefit in this way: marketing using social media, admin through google drive, training on MOOCs, design using inkscape and pixlr, accounting on quick file.
Without these extra costs hanging over us we are free to experiment, innovate, cross disciplines and work on topics that interest us, causes we like. Above all it gives us time to give back to the sources which have allowed us to work in this way: publishing our code, sharing insights through blogging, helping friends and running local projects
What are we excited about?
A nything where disciplines are crossed. We like to look at how statistics and machine learning can be combined with AI, music, graphic design, economics, physics, philosophy.
We are currently looking at how the problem solving frameworks in AI might be applied to decision making in marketing.
Bayesian statistics and simulation for problem
What technology are we into?
I t’s a long list but most prominent are R, python, distributed machine learning
(currently looking at Spark), and d3.js. Some current projects include a package to convert
R output into d3 and AI enhanced statistical modelling.