How to be a more productive Data Scientist? Don’t do more, do it smarter!
A few years ago, I put a lot of work into a hobby project of mine: a self-learning Python chatbot that learns like a newborn baby. It was an exciting concept: I wasn’t allowed to pre-define different grammar rules, so the bot had to identify and learn every rule of the English language by itself. A fun project to work on, but it was also very, very complex. I came up against a lot of technical, logical and conceptual challenges.
I remember, one of my biggest obstacles at the beginning was that I couldn’t come up with a good enough structure to store the new words. I was writing and testing different solutions in Jupyter Notebook for hours, but none of them fulfilled all of the requirements I had… then I said, enough is enough. I switched off the computer and went out for a jog. And an hour later while I was having my after-run shower, magically, the solution just hit me!
I like to recall this story from time to time, because it helps me to remember one of the most important rules of being a more productive data scientist: you don’t have to work more, you have to work smarter!
In this article, I’ll give you six practical tips. Regardless of whether you’re a junior or senior data scientist or you’ve just started to learn, these will help you get the most out of yourself and be a better-performing data professional!
My colleagues always made fun of me in the office, when I was staring at my screen or at my notepad for 10 or 15 minutes without hitting any keys on my keyboard. Well, in a busy startup office, pondering seems like daydreaming. But the truth is that data science is a job that needs deep thinking. Without spending sufficient amount of time to fully understand your data, your analysis, your predictive model, etc. – data science often does more harm than good.
A good manager knows that. So even if it feels weird to just sit and think about things, don’t be afraid to do so. Your promotion won’t depend on how busy you look during the day but the quality of the results that you deliver at the end of the day.
So rule #1 is: Don’t be busy to impress your boss! Take your time thinking.
If a nicely delivered data analysis is the cake, then SQL, Python and pandas are the mixer, the tray and the oven. As a good chef you have to know exactly how these tools work. I’m not saying that you have to have every function of every Python package on the top of your head (that’s not even possible). I’m just saying that you should build on solid fundamentals. Writing complex scripts efficiently needs a flow state. If you have to keep looking up the syntax of an SQL JOIN, a Python for loop or a pandas data import, that will constantly break this flow. You won’t just slow down, you will lose motivation.
So if you don’t feel 100% confident with SQL, Python or pandas yet, take a few more tutorials and more importantly: practice, practice and practice – a lot!
I like companies where working remotely is supported. My best practice is to work four days on site, but take one day a week to work from home (or from anywhere else). For me, this worked really well. While being at the office to meet and to communicate with co-workers is really important, I always felt that my most productive days were when I worked from home.
Because what does a data scientist do? She comes up with statistical models, writes code, tries to understand the business impact of the analyses… These are very introverted tasks. To have full focus, you have to be alone in an interruption-free environment.
The key is interruption-free. So working from home won’t be productive, if:
When you work, you work.
It’s better for everyone if:
As you can see, the emphasis is not on working from home.
