Building a project portfolio
Lessons from Becoming a Data Scientist
Hey squad,
My favourite things on the internet this week were:
This podcast on how to get papers published. Great for medical students and early-stage researchers.
This blog post from Thiago Forte on “The Rise of the Full Stack Freelancer”. Has got me thinking about how best to bring together the different projects I’m working on. It’s beginning to crystallise in mind - will share in due time.
This blog post from Derek Sivers on getting out of a bad state of mind: Focus on the now, clear your schedule, and do the basics.
This week, I shared:
my process for reading machine learning for healthcare papers, both as a video and a PDF summary
some learnings about building a portfolio of projects, for getting a technical role such as a Data Scientist (BELOW)
a Tweetstorm about transitioning from being a doctor to a data scientist
Have a great week,
Chris
📑 Building a portfolio: the rule of 3
To be taken seriously as a technical person (or any area, really), you need to have something to show. Anyone can say “I can code in Python”. You have to make something to prove it.
When I was starting out, a mentor gave me the advice:
Build a portfolio of three great projects.
Initially, for me, this just meant striving to get three projects under my belt. I went to hackathons, interned at companies and designed my own.
Once I achieved that, I kept looking at how I could build on them or replace them with new and cooler projects.
It’s so simple, but I’ve found it a useful way to frame it. You’re only as good as your top three projects.
To this day, the lower right-hand side of my CV is dedicated to my top three projects at that moment in time.
If a round of job applications comes up, or if somebody asks me about work I’ve done, I know what to talk about. These three projects are always in mind.
📂 So how do you get projects?
I’m a big advocate of designing our own projects, both for learning and for potentially contributing to community. But it’s not always easy, particularly when starting out.
A good source of ‘ready-made’ projects to work on is Kaggle. They provide a dataset and often specific challenges. You can also see other people’s solutions, which is a great source of learning.
A great way to devise a project within a team is to attend hackathons. These are typically weekend sprints to develop a solution to a problem and are held in most major cities around the world.
You can also look to your community. If you’re a medic, are there any hospitals or start-ups doing interesting projects? Could you help in some small way?
One thing I found really helpful was attending a project-based course. So much so, that I’ll devote the next email to it.
How to read a Machine Learning for Healthcare Paper
In this video, I share a framework for reading machine learning for healthcare papers, so that you can build up understanding and keep up-to-date with the field.
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About Me
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.