Should I go on a project-based course / bootcamp?
Lessons from Becoming a Data Scientist
One of the most common questions I get asked is how to get started on the route to becoming a data scientist (or building technical skills, more broadly).
There are things that are easy for me to suggest, such as good online courses to take or projects to try.
But, in my opinion, one of the most important things if you are serious about building technical skills is your environment.
🌎 The power of environment
I think the perfect environment to learn is a full-time technical role; a data scientist, machine learning engineer, software developer or similar role.
This is great because:
You have a lot of time (and don’t have to squeeze projects in between other commitments)
You have a clear end-goal (usually in the form of a deliverable)
You are getting paid (learning + earning = win-win)
Skill development is expected (employers have an interest in you building your skills, and will often support you)
However, getting straight into a technical role can be really hard. (Particularly from a ‘non-conventional’ background - i.e. not a computer science degree or similar)
It’s not impossible. I know a handful who have done it. But it takes motivation, intelligence, persistence and a bit of a luck.
🧮 The solution
This is where project-based courses and bootcamps come in.
These are courses ranging from around 5 weeks to a few months, and are centred around a group project that produces a tangible output. There’s typically a partnership with a commercial client who has a genuine interest in what you are building.
They provide you many of the benefits of a full-time technical job; a defined project, a clear end-goal, a supportive learning environment. But they don’t have the same requirements. They’re not hiring you permanently, after all.
I personally went on the “Science to Data Science” (S2DS) virtual course and found it super-charged my skill development. I worked every day on a clearly-defined project, for a commercial stakeholder, with several technical mentors at my fingertips. I constantly came up against new technical challenges, and had the time to learn the solutions. It was a fantastic learning experience.
There are many similar courses in the UK and elsewhere. I’ve heard good things about is the ‘ASI Data Science’ course (who I think have now re-branded as ‘faculty.ai’).
A word of warning: some courses charge a lot. This is a reflection of the high demand for these skills, but not necessarily the value of the course. The S2DS course was £800, which I felt was fantastic value, but I’ve seen some in the region of £5,000+.
My favourite things this week:
(1) A first in medical AI (blog post): This week, the first reimbursement for AI augmented medical care was granted. It was to a company called Viz.ai who detect strokes early on CT. This post from Luke Oakden-Rayner is the best summary of it’s significance that I’ve seen.
(2) The story of Deep Reinforcement Learning (podcast): I really enjoyed this episode of the Artificial Intelligence podcast with David Silver. David leads the RL research group at DeepMind, and was a central figure in the first AI algorithms that beat the world Go champion and, later, several other games. It was fascinating hear the whole story from the horse’s mouth. David previously did a fantastic lecture series at UCL on RL, that I’d highly recommend.
(3) Happy, Smart, Useful (a blog post): Another simple and insightful blog post from Derek Sivers. When making life-changing decisions, seek to be happy, smart and useful.
This week’s video:
Getting started with machine learning is hard. There's a lot to learn and it's difficult to know where to start. In this video, I share my thoughts about how best to go about it, as a healthcare professional:
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Hi! I’m Chris Lovejoy, a Junior Doctor and Data Scientist based in London.
I’m on a mission to improve healthcare through technology (particularly AI / machine learning), but along the way I want to share learnings that are relevant no matter your career choice or background.
In this weekly newsletter, I share my top thoughts and learnings from each week, as well as links to the best things on the internet that I come across.