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data science

The theme of this year’s British Science Week (6 – 15 March 2020) is “Our Diverse Planet”. We’ll be getting involved by speaking to school children about the work we’ve been doing with Oxfordshire-based OceanMind (part of the Microsoft AI for Good programme) to help them combat illegal fishing, hopefully inspiring some of the next generation of data scientists!

My first month as an apprentice at endjin

by Ed Freeman

Structured apprenticeships provide a great way to build skills whilst getting real-life experience. Endjin’s apprenticeship scheme has been refined over years, with an optimal mixture of training, project work, and exposure to commercial processes – a scheme which is designed to build strong foundations for a well-rounded Software Engineering consultant. This post explains the transition from university to an apprenticeship at endjin, including the types of work an apprentice could end up doing, and some examples of real-life learnings from a real-life apprentice.

Welcome to an internship at endjin!

by Ed Freeman

A career in software engineering doesn’t need to start with a Computer Science degree. The underlying traits of problem solving, a willingness to learn and the ability to collaborate well can be built in any field. Internships provide a great way to get your foot-in-the-door in the professional world, and arm you with some real-life experience for future endeavours. This post describes an internship at endjin, including the type of work you could be asked to do and what you could learn.

Automated R Deployments in Azure

by Jess Panni

It’s been great to see Microsoft embracing the R language on Azure, being able to easily operationalize R assets is changing the way organisations think about their analytical workloads. While it is trivial to publish an R model as a web service in Azure Machine Learning, there is still no easy way to integrate this […]

Machine Learning – the process is the science

by James Broome

What do machine learning and data science actually mean? This post digs into the detail behind the endjin approach to structured experimentation, arguing that the “science” is really all about following the process, allowing you to iterate to insights quickly when there are no guarantees of success.

This post looks at what machine learning really is (and isn’t), dispelling some of the myths and hype that have emerged as the interest in data science, predictive analytics and machine learning has grown. Without any hard guarantees of success, it argues that machine learning as a discipline is simply trial and error at scale – proving or disproving statistical scenarios through structured experimentation.