Time is the main limiting factor for every task, for every project. If you had unlimited time, you could do and achieve basically anything. So it is smart to optimize your approach to everyday tasks in a way they take less time. Of course, we want the amount of work done and the quality to stay the same. If you save some extra time in one place, you can spend it on other things.

In this article, I’ll share 5 tips on how to optimize the way of working and save some extra free minutes here and there while working with data science projects. Actually, most of these tips are quite general and applies not only to data science, but other things as well.

1. Use scripts to automate small repetitive tasks

I believe this idea is not something new to you. But how often are you following this advice in reality? I’ve seen many people typing the same lines, again and again, every day repeating exactly the same pieces of code, text, commands in places where it is so easy to avoid that.

Don’t write the same code twice.

I bet you have heard such a phrase, and maybe even follow this rule when writing code for some open source or commercial product. But that rule works not only for the source code alone. What about your small everyday tasks? I’ll provide a small example for you to get the idea better.

For my data science projects, I typically need quite a lot of tools. Like Jupyter notebook, some .txt document for storing temporary notes and links, console terminal opened in my project’s home directory, file browser, internet browser, just to name some. And in my internet browser, I typically like some predefined tabs to be opened, like my e-mail, my calendar, Kaggle, Matplotlib functions reference, and so on.

So what I’m doing first when starting my computer — I’m launching a lot of programs and applications. I have measured it — I can manage to start that all in about 2 minutes. Not that much, given that I need to do it only once a day, right? But then I calculated, how much time does it take on a longer period. 2 minutes every working day sums up to 10 minutes per week. Which sums up to 40 minutes per month. Which sums up to 480 minutes per year. That, by the way, is exactly 8 hours or 1 working day. So basically, every year I was spending one full working day for just turning on my computer.

And then one day I wrote a small script with about 20 commands, which starts Jupyter notebook, opens a terminal, opens browser, and all other things I need. Among others, it even checks my internet connectivity, as sometimes my computer fails to catch wireless network and I need to restart the network interface several times to get it working. My script does it for me.

Now I can spend those 2 minutes on a different task. For example, I can check my email on a mobile phone while my computer is starting up, or do some other small things. I know, that’s just 2 minutes a day. But things sum up.

If you spend just 2 minutes every working day on some repetitive task, that sums up to the full 8-hour workday by the end of the year.

#work-efficiency #data-science-workflow #tips #time #data analytic