6 Beauty Industry Consumer Trends Marketers Should Be Watching
By Michaela VoglOct 2
Published April 24th 2019
Learning to code is a big commitment, although I’ve never heard someone complain that they took the plunge.
And, while it isn’t always easy, it’s often possible to start creating things that work pretty quickly. With just a few weeks’ practice I used Python to make my very own text-based version of Sim City (or, as I like ot call it, Sim Shitty). I’m still a massive amateur, but making toys is great fun.
Toys aside, there are lots of tasks that can be sped up or otherwise benefit from some coding experience, and social data analysis is one of them.
I chatted to Peter Fairfax, a Brandwatch veteran now sitting in both the Research and Data Science teams, who uses Python to generate cool insights from Brandwatch data and helps improve our products. I wanted to know what drove him to learn to code, and, selfishly, to get some advice for advancing.
Here’s our Q&A:
“I think that there comes a time in a lot of analysts’ careers where they realise they’ve become too good at Excel. They realise they’re pushing the limits of what that software is designed to do and want to work faster and smarter while broadening their horizons.
“Personally, I came to this realization when I found myself staring at the little grey box at the bottom right of the screen which tells you the percentage completion when you’re running a big job. I realised that I was developing an unusually close and probably unhealthy relationship with this tiny box, like it was the master of me.
“When I started to learn to code, I realised that I wasn’t just escaping from that little grey box in Excel, though. I was also escaping from a box which I’d built for myself by limiting my types of analysis to the tools I had available.
“Learning to code helps solve this in two very obvious ways – one, you can use or adapt the wide variety of tools that have already been made by other people, and two, you can make tools yourself if they don’t already exist.”
“I don’t really think there’s a clear answer to this.
“Personally I code in Python because it’s relatively easy to pick up as you go along, but also because a colleague of mine was kind enough to show me the basics, and she happened to be a Python programmer. Python is also great because it has a large standard library, with lots of external libraries available, too.
“I suspect that I could have had an equally good experience working with some other languages, though – like R.”
“In my opinion, people who aren’t sure which language to learn can get a lot from just taking the plunge and choosing one, even if they aren’t 100% sure it’s the best one for them.
“Even if you end up deciding that language isn’t for you, you’ll learn some really valuable transferrable skills about programming fundamentals which will help a lot if you decide to learn another language. So go for it!”
“It’s probably a good idea to do some research about what libraries might be available to help you do the sort of analysis you want to do and if there are any free resources available to get you started.
“I used Codecademy in the evenings, and also watched a lot of YouTube videos.”
“I mentioned to my boss that I had started learning to code, and he immediately took the bold move of putting me on a project where I needed to learn a lot more, which was exciting and terrifying in equal measure! So I think I muddled through for the first couple of months.
“I found that when I was working as an analyst, I got by knowing only the bare basics, but recently I’ve needed to learn a lot more, so I’m still “getting it”.”
“Loads of stuff – from a significant change of career (in moving into the Data Science team) to feeling like, with enough time and thought, I’m able to do more or less anything I want to.”
“Honestly, probably not. I think that some people enjoy learning technical skills, and that’s great. But there are many sides to social listening analysis and market research – people in analyst roles can gain a lot by developing their skills and knowledge of research design, communicating findings, keeping abreast of industry trends, and so on. It’s a broad field, and I don’t think you can expect a single person to excel in every one of these different aspects of the job, which is why it’s great to have a varied team.
“I do, however, think it’s extremely valuable to have at least some people on an analyst team who are comfortable doing some coding.”
Thanks a lot to Peter for sharing his thoughts with the Brandwatch blog. You can read more from our Analyst Problems series here.