The EARLy career scholarship
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At Mango, we’re passionate about R and promoting its use in enterprise – it’s why we created the EARL Conferences. We understand the importance of sharing knowledge to generate new ideas and change the way organisations use R for the better.

This year we are on a mission to actively encourage the attendance of R users who are either in a very early stage of their career or are finishing their academic studies and looking at employment options.

We’re offering EARLy career R users a chance to come to EARL – we have a number of 2-day conference passes for EARL London and tickets for each 1-day event in the US. This year’s dates are:
London, 12-13 September
Seattle, 7 November
Houston, 9 November
Boston, 13 November

Who can apply?

  • Anyone in their first year of employment
  • Anyone doing an internship or work placement
  • Anyone who has recently finished – or will soon be finishing – their academic studies and is actively pursuing a career in Analytics

To apply for a free EARLy Career ticket, tell us why you would like to attend an EARL Conference and how attending will help you advance your knowledge and your career.

(Minimum 200 words, maximum 500 words)

Submit your response here.

Terms and conditions: ‘Winners’ will receive tickets for any EARL Conference of their choice. This does not include travel or accommodation. The tickets are non-transferable. The tickets cannot be exchanged for cash.

Join Us For Some R And Data Science Knowledge Sharing In 2018
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We’re proud to be part of the Data Science and R communities.

We recognise the importance of knowledge sharing across industries, helping people with their personal and professional development, networking, and collaboration in improving and growing the community. This is why we run a number of events and participate in many others.

Each year, we host and sponsor events across the UK, Europe and the US. Each event is open everyone —experienced or curious— and aims to help people share and gain knowledge about Data Science and to get them involved with the wider community. To get you started we’ve put together a list of our events you can attend over the next 12 months:

Free community events

LondonR

We host LondonR in central London every two months. At each meet up we have three brilliant R presentations followed by networking drinks – which are on us. Where possible we also offer free workshops about a range of R topics, including Shiny, ggplot2 and the Tidyverse.

The next event is on 27 March at UCL, you can sign up to our mailing list to hear about future events.

Manchester R

Manchester R takes place four times a year. Following the same format as LondonR, you will get three presentations followed by networking drinks on us. We also offer free workshops before the main meeting so you can stay up-to-date with the latest tools.

Our next event is on 6 February where the R-Ladies are taking over for the night. For more information visit the Manchester R website.

Bristol Data Scientists

Our Bristol Data Science events have a wider focus, but they follow the same format as our R user groups – three great presentations from the community and then drinks on us. If you’re interested in Data Science, happen to be a Data Scientist or work with data in some way then you are welcome to join us.

This year, we’re introducing free Data Science workshops before the meeting, so please tell us what you’d like to hear more about.

The Bristol meetup takes place four times a year at the Watershed in central Bristol. If you’d like to come we recommend joining the meetup group to stay in the loop.

BaselR

This meet up is a little further afield, but if you’re based in or near Basel, you’ll catch us twice a year running this R user group. Visit the BaselR websitefor details on upcoming events.

OxfordR

As you may have guessed, we love R, so we try to support the community where we can. We’ve partnered up with OxfordR this year to bring you pizza and wine while you network after the main presentation. OxfordR is held on the first Monday of every month, you can find details here on their website.

BirminghamR

BirminghamR is under new management and we are helping them get started. Their first event for 2018 is coming up on 25 January; for more information check out their meetup page.

Data Engineering London

One of our newest meetup groups focuses on Data Engineering. We hold two events a year that give Data Engineers in London the opportunity to listen to talks on the latest technology, network with fellow engineers and have a drink or two on us. The next event will be announced in the coming months. To stay up-to-date please visit the meetup group.

Speaking opportunities

As well as attending our free events, you can let us know if you’d like to present a talk. If you have something you’d like to share just get in touch with the team by emailing us.

EARL Conferences

Our EARL Conferences were developed on the success of our R User Groups and the rapid growth of R in enterprise. R users in organisations around the country were looking for a place to share, learn and find inspiration. The enterprise focus of EARL makes it ideal for people to come and get some ideas to implement in the workplace. Every year delegates walk away feeling inspired and ready to work R magic in their organisations.

This year our EARL Conference dates are: London: 11-13 September at The Tower Hotel Seattle: 7 November at Loews Hotel 1000 Houston, 9 November at Hotel Derek Boston, 13 November at The Charles Hotel

Speak at EARL

If you’re doing exciting things with R in your organisation, submit an abstract so others can learn from your wins. Accepted speakers get a free ticket for the day they are speaking.

Catch us at…

As well as hosting duties we are proud to sponsor some great community events, including PyData London in April and eRum in May.

Plus, you’ll find members of the Mango team speaking at Data Science events around the country. If you’d love to have one of them present at your event, please do get in touch.

Wherever you’re based we hope we will see you soon.

Field Guide to the R Ecosystem
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Mark Sellors, Head of Data Engineering

I started working with R around about 5 years ago. Parts of the R world have changed substantially over that time, while other parts remain largely the same. One thing that hasn’t changed however, is that there has never been a simple, high-level text to introduce newcomers to the ecosystem. I believe this is especially important now that the ecosystem has grown so much. It’s no longer enough to just know about R itself. Those working with, or even around R, must now understand the ecosystem as a whole in order to best manage and support its use.

Hopefully the Field Guide to the R Ecosystem goes some way towards filling this gap.

The field guide aims to provide a high level introduction to the R ecosystem. Designed for those approaching the language for the first time, managers, ops staff, and anyone that just needs to get up to speed with the R ecosystem quickly.

This is not a programming guide and contains no information about the language itself, so it’s very definitely not aimed at those already developing with R. However, it is hoped that the guide will be useful to people around those R users. Whether that’s their managers, who’d just like to understand the ecosystem better, or ops staff tasked with supporting R in an enterprise, but who don’t know where to start.

Perhaps, you’re a hobbyist R user, who’d like to provide more information to your company in order to make a case for adopting R? Maybe you’re part of a support team who’ll be building out infrastructure to support R in your business, but don’t know the first thing about R. You might be a manager or executive keen to support the development of an advanced analytics capability within your organisation. In all of these cases, the field guide should be useful to you.

It’s relatively brief and no prior knowledge is assumed, beyond a general technical awareness. The topics covered include, R, packages and CRAN, IDEs, R in databases, commercial versions of R, web apps and APIs, publishing and the community.

I really hope you, or someone around you, finds the guide useful. If you have any feedback, find me on twitter and let me know. If you you’d like to propose changes to the guide itself, you’ll find instructions in the first chapter and the bookdown source on GitHub. Remember, the guide is intentionally high-level and is intended to provide an overview of the ecosystem only, rather than any deep-dive technical discussions. There are already plenty of great guides for that stuff!

I’d also like to say a huge thanks to everyone who has taken time out of their day to proof read this for me and provide invaluable feedback, suggestions and corrections. The community is undoubtedly one of R’s greatest assets.

Originally posted on Mark’s blog, here.

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Prelude

Maybe you’re looking for a change of scene. Maybe you’re looking for your first job. Maybe you’re stuck in conversation with a relative who you haven’t spoken to since last Christmas and who has astonishingly strong opinions on whether cells ought to be merged or not in Excel spreadsheets.

The fact of the matter is that you have just encountered the term “data science” for the first time, and it sounds like it might be interesting but you don’t have a clue what it is. Something to do with computers? Should you bring a lab coat, or a VR headset? Or both? What is a data and how does one science it?

Fear not. I am here to offer subjective, questionable and most importantly FREE advice from the perspective of someone who was in that very position not such a long time ago. Read on at your peril.

I. Adagio: Hear about data science

This is the hard bit. It’s surprisingly difficult to stumble upon data science unless someone tells you about it.

But the good news is that you’re reading this, so you’ve already done it. Possibly a while ago, or possibly just now; either way, put a big tick next to Step 1. Congratulations!

(By the way, you’ll remember the person who told you about data science. When you grow in confidence yourself, be someone else’s “person who told me about data science”. It’s a great thing to share. But all in good time…)

II. Andante: Find out more

But what actually is data science?

To be honest, it’s a fairly loosely-defined term. There are plenty of articles out there that try to give an overview, but most descend into extended discussions about the existence of unicorns or resort to arranging countless combinations of potentially relevant acronyms in hideous indecipherable Venn diagrams.

You’re much better off finding examples of people “doing” data science. Find some blogs (here are a few awesome ones to get you started) and read about what people are up to in the real world.

Don’t be afraid to narrow down and focus on a specific topic that interests you – there’s so much variety out there that you’re bound to find something that inspires you to keep reading and learning. But equally, explore as many new areas as you can, because the more context you can get about the sector the better your understanding will be and you’ll start to see how different subjects and different roles relate to each other.

Believe it or not, one of the best tools for keeping up to date with the latest developments in the field is Twitter. If you follow all your blog-writing heroes, not only will you be informed whenever they publish a new article but you’ll also get an invaluable glimpse into their day-to-day jobs and working habits, as well as all the cool industry-related stuff they share. Even if you never tweet anything yourself you’ll be exposed to much more than you’d be able to find on your own. If you want to get involved there’s no need to be original – you could just use it to share content you’ve found interesting yourself.

If you’re super keen, you might even want to get yourself some data science books tackling a particular topic. Keep an eye out for free online/ebook versions too!

III. Allegretto: Get hands-on

Observing is great, but it will only get you so far.

Imagine that you’ve just heard about an amazing new thing called “piano”. It sounds great. No, it sounds INCREDIBLE. It’s the sort of thing you really want to be good at.

So you get online and read more about it. Descriptions, analyses, painstaking breakdowns of manual anatomy and contrapuntal textures. You watch videos of people playing pianos, talking about pianos, setting pianos on fire and hurling them across dark fields. You download reams of free sheet music and maybe even buy a book of pieces you really want to learn.

But at some point… you need to play a piano.

The good news is that with data science, you don’t need to buy a piano, or find somewhere to keep it, or worry about bothering your family/friends/neighbours/pets with your late-night composing sessions.

Online interactive coding tutorials are a great place to start if you want to learn a new programming language. Sites like DataCamp and Codecademy offer a number of free courses to get yourself started with data science languages like R and Python. If you are feeling brave enough, take the plunge and run things on your own machine! (I’d strongly recommend using R with RStudio and using Anaconda for Python.) Language-specific “native-format” resources such as [SWIRL]() for R or this set of Jupyter notebooks for Python are a great way to learn more advanced skills. Take advantage of the exercises in any books you have – don’t just skip them all!

Data science is more than just coding though – it’s all about taking a problem, understanding it, solving it and then communicating those ideas to other people. So Part 1 of my Number One Two-Part Top Tip for you today is:

  1. Pick a project and write about it

How does one “pick a project”? Well, find something that interests you. For me it was neural networks (and later, car parks…) but it could be literally anything, so long as you’re going to be able to find some data to work with. Maybe have a look at some of the competitions hosted on Kaggle or see if there’s a group in your area which publishes open data.

Then once you’ve picked something, go for it! Try out that cool package you saw someone else using. Figure out why there are so many missing values in that dataset. Take risks, explore, try new things and push yourself out of your comfort zone. And don’t be afraid to take inspiration from something that someone else has already done: regardless of whether you follow the same process or reach the same outcome, your take on it is going to be different to theirs.

By writing about that project – which is often easier than deciding on one in the first place – you’re developing your skills as a communicator by presenting your work in a coherent manner, rather than as a patchwork of dodgy scripts interspersed with the occasional hasty comment. And even if you don’t want to make your writing public, you’ll be amazed how often you go back and read something you wrote before because it’s come up again in something else you’re working on and you’ve forgotten how to do it.

I’d really encourage you to get your work out there though. Which brings us smoothly to…

IV. Allegro maestoso: Get yourself out there

If you never play the piano for anyone else, no-one’s ever going to find out how good you are! So Part 2 of my Number One Two-Part Top Tip is:

  1. Start a blog

It’s pretty easy to get going with WordPress or similar, and it takes your writing to the next level because now you’re writing for an audience. It may not be a very big audience, but if someone, somewhere finds your writing interesting or useful then surely it’s worth it. And if you know you’re potentially writing for someone other than yourself then you need to explain everything properly, which means you need to understand everything properly. I often learn more when I’m writing up a project than when I’m playing around with the code in the first place.

Also, a blog is a really good thing to have on your CV and to talk about at interviews, because it gives you some physical (well, virtual) evidence which you can point at as you say “look at this thing wot I’ve done”.

(Don’t actually say those exact words. Remember that you’re a Good Communicator.)

If you’re feeling brave you can even put that Twitter account to good use and start shouting about all the amazing things you’re doing. You’ll build up a loyal following amazingly quickly. Yes, half of them will probably be bots, but half of them will be real people who enjoy reading your work and who can give you valuable feedback.

Speaking of real people…

  1. Get involved in the community

Yes, that was indeed Part 3 of my Number One Two-Part Top Tip, but it’s so important that it needs to be in there.

The online data science community is one of the best out there. The R community in particular is super friendly and supportive (check out forums like RStudio Community, community groups like R4DS, and the #rstats tag on Twitter). Get involved in conversations, learn from people already working in the sector, share your own knowledge and make friends.

Want to go one better than online?

Get a Meetup account, sign up to some local groups and go out to some events. It might be difficult to force yourself to go for the first time, but pluck up the courage and do it. Believe me when I say there’s no substitute for meeting up and chatting to people. Many good friends are people I met for the first time at meetups. And of course, it’s the perfect opportunity to network – I met 5 or 6 of my current colleagues through BathML before I even knew about Mango! (If you’re in or near Bristol or London, Bristol Data Scientists and LondonR are both hosted by Mango and new members are always welcome!)

Postlude

Of course, everything I’ve just said is coming from my point of view and is entirely based on my own experiences.

For example, I’ve talked about coding quite a lot because I personally code quite a lot; and I code quite a lot because I enjoy it. That might not be the case for you. That’s fine. In fact it’s more than “fine”; the huge diversity in people’s backgrounds and interests is what makes data science such a fantastic field to be working in right now.

Maybe you’re interested in data visualisation. Maybe you’re into webscraping. Or stats. Or fintech, or NLP, or AI, or BI, or CI. Maybe youare the relative at Christmas dinner who won’t stop banging on about why you should NEVER, under ANY circumstances, merge cells in an Excel spreadsheet (UNLESS it is PURELY for purposes of presentation).

Oh, why not:

  1. Find the parts of data science that you enjoy and arrange them so that they work for you.