Cramming for exams at university never worked for me. On exam days, I went straight to the canteen.
‘Two Red Bulls please.’
Then my knee would spend the next two hours tapping away but my brain would fail to connect the dots. I’d get upset when the thing I read 4-hours ago wasn’t bringing itself to the page.
5 years into a 3 year long degree, I graduated. But the most valuable thing I took away from university wasn’t the piece of paper. It was learning how to learn.
Instead of cramming a couple of days before the exam, I spread my workload out over the semester in 25-minute chunks. Nothing revolutionary by any means. But it was to me.
Now I do the same.
When I want to learn something, I try do a little per day.
If you want to learn something, the best way to do it is the same way to do anything, bit by bit.
For data science and programming, my brain maxes out at around four hours of concentrated focus. After that, the work starts following the law of diminishing returns.
I spend much of my time at Max Kelsen (MK) as a Machine Learning Engineer cleaning data, researching the internet for new ways to solve problems and coding up data science and machine learning pipelines to put into production.
So by the time I finish work, there’s no chance I’m going to be able to learn something new effectively. Instead of cramming in more information, I fill the time with movement, creating things and rest. And let the sub conscious do its thing.
On days I’m not at MK and want to level up my skills I use the Pomodoro technique. Remember the 25-minute chunks from before?
Again, nothing revolutionary. But I’m the kind of person who’s excited by everything. So if something as simple as setting a timer keeps me focused, I’m all for it.
On big days I’ll aim for 10.
Other days I’ll aim for 8. Sometimes less.
It’s simple. You set a timer for 25-minutes and do nothing but the single task you set yourself at the beginning of the day for that 25-minutes. And you repeat the process for however many times you want.
Let’s say you did it 10-times, your day might look like:
Phone in drawer.
Open Be Focused on Mac and setup a timer. Or you could use any other time keeping device (except your phone, too many distractions).
Now it’s not even 2:30 pm and if you’ve done it right, you’ve got some incredible work done. And you can check your phone now.
You can use the rest of the afternoon to catch up on those things you need to catch up on.
Don’t think 10 lots of 25-minutes (just over 4-hours) is enough time to do what you need?
Try it. You’ll be surprised what you can accomplish in 4-hours of focused work.
The schedule above is similar to how I spent nine months following my own AI Masters Degree before getting a job.
And it was the same the other day. Except I threw in a bit of longer break during the middle of the day to go to training and have a nap.
I was working through the Applied Data Science Specialization with Python by the University of Michigan on Coursera.
The first few lessons are all about different ways to manipulate data. I’m finding this to be one of the most important steps when working on a machine learning problem. And the course projects have been incredibly close to what I’ve been doing day-to-day as a Machine Learning Engineer at Max Kelsen.
So if you want to level up your data science skills, especially the preprocessing step, I’d recommend giving the free 7-day trial a go and seeing if the specialization is for you.
And if you can’t manage 10 Pomodoros worth of dedicated work in a day. Start with 1. Then 2. Then 3. Sometimes you might even decide to go longer than 25-minutes. Eventually, you’ll wonder how you worked any other way.