Finding and learning from technical mentors
How I got my first data science job during COVID (as a doctor)
|Christopher Lovejoy||Aug 17|| 6|
Hello from London!
A few months ago, I landed my first full-time job as a data scientist, after working for several years as a doctor. In the next several emails, I’m going to share reflections and learnings about making a transition into a technical field.
One of most common struggles I see, and that I experienced personally, is wanting to build coding or data science skills but not really knowing where to start. My top recommendation for this is to find technical friends who you can go to, whether for advice on what to do next, or to solve a specific problem with your code. In this email, I share how I made the technical friends that I still talk to today.
This week, I also shared a video about assessing the clinical impact of a machine learning model. Having a model that performs a task well is only the first step. It’s essential to also validate that it improves patient outcomes. In the video, I talk about issues to avoid and how to measure impact.
Have a great week :)
📈 Accelerating early progress with technical friends
Coming from a medical background, I didn’t have the first clue about how to develop coding skills or data science understanding. And neither did anybody around me.
This made it really important for me to branch out and find people who did. I quickly saw the benefits of doing so.
⚙️ Early inefficiencies
When I initially started out, I’d have to resort to Google or StackOverflow to try and solve my problems. These are great resources, but it’s hard to find what you want when you don’t really know what you’re looking for.
One of the top skills a developer needs to know is what to search to find the solution to your current problem. Without that skill, I would spend ages stuck at a relatively simple hurdle - like trying to manipulate a pandas dataframe in a particular way, or how to install and import the package I needed.
I’d heard that it’s best to think of your own projects as a means to learn. However, without insight into what it takes to build a project, and what’s possible, I would typically come up with over-ambitious projects with too many moving parts. I remember an early project idea was to build a chatbot patient for doctors to practice with, and I even started collecting transcripts from real conversations to help make this. In hindsight, this type of task was way too ambitious for someone of my technical level at that time (having just completed an Intro to Python course).
🙋♂️ Getting by with a little help from some friends
Having technical friends is great for overcoming both of these sources of inefficiency.
If you have a relatively simple technical issue, but don’t know where to go to solve it, a technical friend can point you in the right direction pretty quickly. This saves a lot of time and frustration.
Likewise, if you come up with a project idea, you can run it by a technical friend. They’ll be able to break it down into stages and ultimately advise you whether it makes sense to do and how best to go about it. This can save you a lot of time from barking up the wrong tree.
🙍♀️How to make technical friends
I don’t think there’s a “right” way to find technical friends and establish a relationship where you can ask them for advice. Here are a few principles that I find helpful.
Firstly, being open and honest about my intentions (“I’m learning to code and would love someone I could ping a message to when I get stuck”).
Secondly, being respectful of their time. I pushed myself to only ask for help if I’d truly searched for the solution and spent time trying to solve it myself. (To be honest, I think you also learn better this way.)
This wasn’t always easy. Sometimes I’d hit the initial wall of frustration and feel an urge to send off multiple messages, hoping for a quick solution. I tried to have a good crack myself first, but I’ll admit I sometimes caved in.
It was also helpful to have multiple people I could go to. I didn’t have to keep bugging the same person, reducing the risk of annoying them.
I personally met these friends from multiple places; from attending events that interested me (such as data science and machine learning meet-ups), from working on projects together (more on that in future emails) and from a smattering of formal ‘networking’, friends-of-friends and random LinkedIn messages.
At some point, when it felt comfortable, I’d reach out for advice on a specific problem or project I was working on. Sometimes the problems were simple or the project ideas were bad, so I had to put my pride to the side and seek out the constructive criticism.
(If you’re starting out, and looking for a technical friend to help you get started, feel free to reach out by hitting ‘reply’)
My favourite things this week:
(1) A blog (the tyranny of ideas): I highlighted almost every paragraph of this article on my Instapaper. The point that most resonated with me was about how reputation for certain types of ideas or content creation can become limiting.
(2) A Whatsapp newsletter (Read This Today): I was intrigued by the idea of a Whatsapp daily newsletter, so decided to sign-up. Every day, they send one article to the 62,000 subscribers. I’ve really enjoyed it so far.
(3) A podcast (“the intersection of coding and medicine”): I really enjoyed hearing Aaron Smith (a medical doctor and programmer) share his experiences of learning to code and using it to support his medical practice.
This week’s video:
To have a positive impact with a machine learning model in healthcare, it's important to ensure models generalise to new data and that they are appropriately incorporated into clinical workflows.
It's important to have strong evidence of a clinical benefit before widespread implementation.
In this video, we talk about how to achieve a positive health impact through machine learning and how to develop clinical evidence to support this.
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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.