Getting stuck due to syntax issues
To do data science, sooner or later, you’ll need to learn how to code (mostly in Python, SQL, bash and/or R). And for everyone who’s new to coding, by far the biggest struggle is syntax.
I know that if you’ve never coded it might look intimidating.
But you have to understand one very important thing:
A computer is a powerful tool. It does automations and heavy calculations that we, humans, would never be able to. But a computer is not smart. So you have to tell it in a very meticulous way what you want to get done.
In programming, a single mistyped character, a line break, or a missing comma can cause an issue. You missed a space? Your script will break. This is how it is!
But if you type in everything accurately, your script will just work.
So when you code, put an extra emphasis on making sure that you avoid any typos. If you follow a tutorial, make sure you follow it very thoroughly. If you write your own code, know the syntax!
a missing coma in an SQL query — » error! (Image by author)
Being afraid of error messages
Here’s another scary-for-beginners concept in data science and coding: error messages!
If your script or query fails, you get ugly error messages returned to your screen, right? They don’t really make any sense at first glance. But don’t get intimidated by them! They are there to help you!
Okay, I admit they are long and ugly and a big part of the text you’ll see is not even useful. But read your error messages carefully and you will see that they actually do a good job of describing the issue in your code.
They’ll show you two things for sure:
- Where the problem is.
- Both in Python and SQL, there is a ^ character that shows where your mistake is within the script you’ve written. (See the pictures.)
- What the problem is.
- And in most cases you get a one-line summary of the problem, too. (eg. “expected an indented block”, “column does not exist”, etc.)
Debugging will get much easier if you know where and what you are looking for!
Learning only the exciting stuff
Hey, I know!
Machine learning is exciting. Deep learning is exciting. Predicting the future is exciting!
But here’s the thing:
The “exciting” things above are only 5% of the job. Or even less, if you are a junior.
Let’s play a game!
Guess what’s the most time-consuming part of the data scientist job!
…
Ready?
Okay, the answer is…
…data cleaning!
There’s a joke among practicing data scientists that says: “80 percent of data science is data cleaning. And 20 percent is complaining about data cleaning.”
Yes, it’s a joke, but it gives you the idea.
#self-learning #careers #coding #data-science #mindset #data analysis
