“Do I need a graduate degree to get a job as a data scientist?”
I get asked some version of this question on a regular basis.
It’s a fair assumption—given how often MSc. or Ph.D. degrees are listed in the requirements of job postings.
As a Ph.D. dropout, I want to answer with an emphatic no. I had a less than ideal experience as a grad student. And it did not prepare me at all for the job market.
But the full answer is a bit more complicated.
This post was inspired by a Twitter thread that got a lot of love.
Landing a job as a data scientist, machine learning engineer, or really any kind of role writing software takes more than just math and programming knowledge. In reality, these roles require you to make hundreds of decisions every day.
These might be big decisions, like:
Or tiny decisions, like:
Keeping a daily journal is probably the most useful habit I’ve developed. It has increased awareness of how I spend my time and been a catalyst for other productive habits. It took me almost a year of experimenting with habit strategies to develop one that stuck.
Here’s what I learned.
It should be easy and obvious. I use the terminal application for Joplin. I spend most of my day working from the command-line, so being able to open my journal with a single command makes it easy to start (plus, it helps reinforces my identity as a programmer). If you…
One lesson I find myself learning over and over again is how important implementation intentions are for forming habits. It’s a simple premise. If you schedule a time and place for a habit, you’re more likely actually do it. The general formula is:
At [time/place] I will [behaviour]
I first came across this concept in Atomic Habits, but I also noticed the benefit from my own personal experiments. While tracking the habits I wanted to start, I noticed that the only ones I actually maintained were the ones that I had scheduled explicit time for. …
I’ve been sleeping in since kindergarten. If there was somewhere to be in the morning, I was either slightly late or really late. Getting to school on time was a rarity and I was the worst paperboy my neighborhood had ever seen. As a grad student, my text history with my advisor was a long string of texts from me along the lines of, “Sorry, running late.”
Over the past year, however, I’ve managed to change my behavior. I consider myself a “morning person” now. I’m not doing anything spectacular, like getting up at 5:00am and going to the gym…
Two years ago I left a PhD in physics, joined a startup, and taught myself to be a software developer. I had to radically alter the way I learned and approached problems.
An education in physics is very much a bottom-up approach. Before you can grasp the intricacies of electromagnetism, quantum mechanics, and general relativity, you need a firm footing in the fundamentals. I believe this is the right learning model for physics. If you’re trying to determine the underlying truths about the universe, you should have a solid understanding of first-principles.
This mindset, however, can lead to a “Physicist’s…
“Quit while you’re ahead” is an effective strategy for forming new habits when it is applied locally rather than globally. I don’t mean quit a habit once you’re good at it, but quit practicing in specific instances while your enjoyment or satisfaction is high, e.g. stop writing while you still have momentum.
There are psychological reasons for why this is a good strategy. The peak-end rule, first described by Daniel Kahneman, is a “psychological heuristic in which people judge an experience largely based on how they felt at its peak (i.e. …
Joining an early-stage startup was a turning point in my life. I joined SharpestMinds two years ago as a relatively ignorant PhD dropout. But, since then, I’ve gotten a crash course in startup culture and best practices and, in an effort to keep up with the growth of the company, become a bit obsessed with self-improvement and productivity hacks.
As I learned more about lean startup principles, I also started researching and experimenting with effective habit formation. I’ve noticed a lot of parallels with the advice in both areas. Below is an attempt to map some lean startup concepts to…
We live in an age of distraction. Smart phones, the internet, and a wealth of instant communication tools are constantly vying for our attention and making it harder than ever to stay focused for long periods of time. Long periods of deep focus, however, are incredibly valuable.
Using my time more productively is a skill I’m actively trying to develop. It’s not easy, especially while working at a computer all day where so many distractions are readily available. Here are some specifics habits I’ve developed that have been improving my ability to stay focused.
Most of the products you interact…
AI and machine learning algorithms are becoming increasingly relevant in the technology we use and it’s important for everyone to understand the implications. There is a key difference in how machine learning algorithms are programmed compared to traditional algorithms. Instead of being logic-based, they are data-based. We’re going from software that is explicitly programmed (if this condition is true: do a thing; otherwise, do another thing) to software that “learns” to generalize from example data.
Let’s see this in action using a famous machine learning algorithm called Word2Vec. Word2Vec was developed in 2013 by engineers at Google and is widely…