I chaired a breakfast meeting for Women in Data Science recently, and one of the topics for discussion was how to retain talent. While demand is outstripping supply and the market is going crazy, it’s enough of a minefield finding good people in the first place.
Add to this that even after you’ve made an offer to someone, recruiters will be contacting them regularly to try to tempt them away to other roles. It’s impossible to prevent this. I’m a big believer in not playing games with recruitment – I know what I can afford and won’t get into a bidding war. If I’m paying a fair salary and they go elsewhere for money, then they are more likely to jump when a recruiter calls regardless of how well you incentivise them. This isn’t a big company or small company thing, if you want to keep hold of your team after you’ve done the very hard job of hiring them then you need to understand what motivates them and either make sure that you continue to provide those needs or plan to be hiring again in the next 12-24 months. Continue reading Incentivising data scientists
I work with many people who are recently out of academia. While they know how to code and are experts in their fields, they are lacking some of rigour of computer science that experienced developers have. In addition to understanding the problems of data in the wider world and testing their solutions properly, they are also unaware of the importance of source code control and deployment. This is another missing aspect from these courses – you cannot exist as a professional developer without it. While there are many source control setups, I’m most familiar with git.
I’ve recently written a how-to guide for my team and was going to make that the focus of this post, although I’ve seen some very good guides out there that are more generic, so I’d like to explain why source code control is important and then give you the tools to learn this yourself. Continue reading Source Code Control for Data Scientists
Back in those heady pre-internet days, if you wanted to learn something that you weren’t taught at school, it pretty much meant a trip to the library. I was pretty lucky, if I wanted a book and there was even a hint of anything educational in it, then it was bought for me.
I was further fortunate in that with a teacher as a parent, I had access to the Acorn Achimedes and BBC computers as they were rolled out to schools for the entirety of the school holidays. There was one rule: if you want to play games, write them yourself. While rose-tinted memory has me at the tender age of 7 fist-pumping and saying “challenge accepted”, I’m sure there was much more complaint involved, but I’m glad that I was encouraged. Continue reading Learning BASIC – blast from the past
Motivational posters, whether in their original form or the short images shared on social media, can instil multiple emotions. They can be positive or downright cringe-worthy, inspiring or bad advice… all superimposed on an image that may or may not correspond to the text.
The latest “fun” AI to go around is Inspirobot. This AI has been trained on the form and tone of motivational images, and at the touch of a button will generate one for you. There is a limited stock of images (I have had the same image more than once, but am still also getting new ones, so I’d estimate this is in the hundreds), but the text itself appears to be generated each time1.
It’s rare that I am intentionally provocative in my post titles, but I’d really like you to think about this one. I’ve known and worked with a lot of people who work with data over the years, many of who call themselves data scientists and many who do the role of a data scientist but by another name1. One thing that worries me when they talk about their work is an absence of scientific rigour and this is a huge problem, and one I’ve talked about before.
The results that data scientists produce are becoming increasingly important in our lives; from determining what adverts we see to how we are treated by financial institutions or governments. These results can have direct impact on people’s lives and we have a moral and ethical obligation to ensure that they are correct. Continue reading Why are data scientists so bad at science?
This week I was delighted to be at the Royal Statistical Society as a business representative for the launch of their Data Science Section. At over 160 years old, the RSS is one of the more established professional bodies and I like that it is questioning and making a difference as the application of their industry changes and when faced with an increasing challenge of abuse of statistical methods. I wish the general public had a greater understanding of statistics so they wouldn’t be so easily swayed by the media with a simple graph “proving” a point. Continue reading Professional body for data science? Yes Please
This week was the exam for my level 2 OU module MST210 on methods, models and modelling. This was a compulsory module, but had it not been I would have never chosen it. The module has been mostly applied maths, which has been really interesting, but what’s been a problem for me has been the mandatory team work modelling exercise, which makes up 16% of the continuous assessment. So much so, that I lost motivation to do the final TMA or revise for the exam as much as I wanted to. I thought it would be worth a short reflection on why I disliked this aspect so much (especially as it led to a repeat of last year when it came to revision…). Continue reading MST210 – Exam and modelling exercise reflections
A couple of weeks ago I got an iWatch. I’d had a Nike fuel band before and am no stranger to wearable tech, but I’ve never really worn a watch. I’ve been surrounded by things that tell us the time since I was a child so I’ve got used to not wearing anything on my wrist1 However, when my other half decided not to wear his, I thought I’d give it a go before we sold it. Continue reading Smart watch insights – now I can’t do without it
If you’ve been following this blog you’ll know that there have been great advances in the past few years with artificial image generation, to the stage where having a picture of something does not necessarily mean that it is real. Image advances are easy to talk about, as there’s something tangible to show, but there have been similar large leaps forward in other areas, particularly in voice synthesis and handwriting.