This is an issue that I’ve seen a few times over the years and have run afoul of early in my career. It’s also one of the most important lessons I ever learned.
It’s common knowledge that a large proportion of data science projects fail. This is often attributed to working models never making it into production and the difficulties around MLOPs and machine learning engineering. Before I continue, if you’re wanting a super simple guide to walking through the end-to-end process I wrote a post on this a while back that goes through the basics. …
I’ve seen this across many companies I’ve both worked in and consulted for throughout my career — we’re not hiring the right people for the job!
I still hear a lot of horror stories of data science teams underperforming, management losing confidence in data science in general, or data scientists getting frustrated in their roles. When you take a deeper look, you see a common pattern.
We keep hiring data scientists without really understanding where they bring the most value.
For many organisations, it’s analysts and data engineers that are really what’s needed. Sometimes, it’s not even data or insights…
It shouldn’t surprise you to know that I — like many data scientists — am a bit of a bookworm. I love to read and try to pick up new skills. There’s a special feeling when you hear something that just clicks and makes you think about something in a completely different way.
One thing that’s bitten me again and again throughout the years, however, is that gap between reading how to do something and actually doing it. …
The tech world seems to have gone mad for data scientists in the last few years: many people want to get into the role and plenty of organisations are looking to hire them. As someone that’s interviewed and hired scores of talented, capable individuals for the ‘Sexiest Job of the 21st Century,’ I can tell you, it can be a painful and difficult process for both sides. Whether you’re a candidate trying to find the perfect role or an organisation seeking the right fit — there are plenty of pitfalls to watch out for.
As techies, we can get really excited about learning the newest tools and approaches. There’s always some big discussion in the community, like R vs. Python, Power BI vs. Tableau, TensorFlow vs. PyTorch, etc. It’s easy to get drawn in. You’re passionate about tech. Good.
One thing that can really hold strong technical people back, however, is soft skills. Soft skills are hard because developing them is often uncomfortable and there’s no documentation.
In this article, I’m going to outline the top five soft skills that I think can make the biggest impact in any tech career.
From the earliest themes of artificial intelligence in Greek mythology, people have long thought about AI and the possibilities it may hold. With the advances in computation and mathematics, Alan Turing’s 1950 paper on thinking machines sparked the first real developments of this in practice. The first proof of concept was initialised through Allen Newell, Cliff Shaw, and Herbert Simon’s, Logic Theorist, a program designed to mimic the problem-solving skills of humans — considered by many to be the first artificial intelligence program presented in 1956.
AI is 60 years young, we’re only at the very beginning.
We’re often bombarded with how popular data science is as a career. It’s all too common to read things like data science being the “Sexiest Job of the 21st Century” or yearly comparisons of high salary expectations.
Data science has a lot to offer. It’s a challenging role with plenty to learn and keep you occupied. Compared to many other roles, data scientists can be given a lot of autonomy to explore and solve interesting problems. And, in many cases, you’ll get the opportunity to work with talented and skilled people in a variety of domains.
More than anything else, how you communicate will be the dominant factor in your success as a data scientist. Hear why and what you can do to turn communication into a strength.
I get asked a lot by aspiring data scientist questions about how to improve and what they should be focusing on. Things like:
While these are all valid things to be considering (in the right context) for the vast majority of people they’re not where you should be focusing…
I first started coding during an extended hospital stay back in 2010. I told myself, “If I’m stuck here, I want to learn something useful.” Like most people who take the plunge, I soon got carried away with this newfound power! Even the incredibly simple things I was able to do in C++ opened my eyes to the possibilities and wonder of it all.
I was first introduced to machine learning during my Ph.D. I was researching tools and techniques for optimisation in high-powered laser defence systems and stumbled upon reinforcement learning. …
Data science is a highly attractive career for many reasons — so the competition can be tough. Some of the tips below that make a great candidate stand out might surprise you!
I’ve written this article for a variety of audiences:
This is a question I often get at user groups and community events for aspiring or…