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If you’re based in the North East of England and you’re looking for a place to discuss all things R with like-minded people, then you might want to check out the North East Data Scientist (NEDS) Meetups! Hosted every two months in the amazing Catalyst building in the centre of Newcastle upon Tyne, the NEDS meetups are an excellent opportunity to network with like-minded data science enthusiasts and professionals. We hold two talks at each session presented by data science experts from across the North East, and we have have recently started running pre-event workshops delivered by our very own JR trainers!

If you came here looking for an introduction to water, or a synopsis of the 2003 TV series about teenage mermaids you have sadly come to the wrong place. The H2O that we will talk about is H2O.ai, a company which develops products for easy, scalable, machine learning and artificial intelligence. Introduction Machine learning and artificial intelligence (or AI for short) are topics which have had a lot of interest over the past 4-5 years.

The trainers here at Jumping Rivers have been busy developing a host of new courses for your programming pleasure! We have recently developed several new courses, which are now available to view on our course list. As well as R and Python, our new courses focus on two new tools: H2O.ai and Tableau. Whether you want to start from scratch, or improve your skills, Jumping Rivers has a training course for you.

Maintaining training materials Over the last few years, we increased both the number and types of training courses we offer. In addition to our usual R courses in {dplyr} and {shiny}, we also offer training on Docker, Python, Stan, TensorFlow, and others. As the number of courses we offer increased, so did the maintenance burden of our associated training materials (lecture notes, slides, exercises, and more). To ease this burden, and to assist in ensuring that our training materials build consistently, we developed an R package called {jrNotes2}.

https://xkcd.com/1513 Linting is a process for identifying bugs and stylistic errors in your code. The process is carried out by analysis tools called ‘linters’, which are widely available for every major programming language. Linters will flag issues and style violations in your code, sort of like a spell checker! In addition to linters, there are a wide range of ‘auto-formatters’ that can also carry out these checks, and even make the necessary changes for you.

R version 4.0.0 was released almost two years ago. The change in the major version, 3.x.y to 4.0.0, represented significant and potentially breaking changes. For an organisation to start using these new features, everyone in the company must have access to that version; otherwise code isn’t shareable. This naturally slows down adoption. We moved our internal R projects to depend on version R 4.0.0 around twelve months ago - a few months after the release date.

In November 2021, I took part in the third edition of the 30 Day Map Challenge created by Topi Tjukanov. Participants are given a theme for each day of November, and are tasked with creating a map within that theme. Details of the challenge can be found here. My own contributions can be found on GitHub. Creating thirty maps was indeed a challenge, but over the course of the month I developed a process for approaching the problem.

In line with the continuous growth at Jumping Rivers, we are looking to expand our team of dedicated professionals working in our teams. If you are enthusiastic and keen to develop your skills in cutting edge data science or infrastructure please read on! Who are we and what do we do? Jumping Rivers is an analytics company whose passion is data and machine learning. We help our clients move from data storage to data insights.

This blog post was written by our intern Osheen Macoscar. 2020 is a year most of us would like to leave behind. But not all change is bad, and many interesting developments, especially in education, happened due to the constraints imposed by COVID. Like many other training providers, we had to pivot to online learning, which brought with it challenges but also new opportunities. This review will hopefully offer some insight into what the year looked like for our trainers and training course attendees with some key facts and figures along the way.

In June 2020, GitHub announced that is was moving the default branch name from master to the more neutral name, main. GitLab followed suit in a few months later. Tobie Langel makes the salient point on why changing the name is a good thing: So master is not only racist, it’s also a silly name in the first place. The purpose of this post is summarise some of the challenges we faced when moving from master to main, with the goal that if you decide to make the same change, you’ll hopefully avoid some of the issues.