Federico Trotta - from Medium

July the 8th

Let's talk about how to know all the Python you need to know to get your next job

Hey friend,

if you're trying to change your job, maybe for a career switch, you may be afraid of how much Python you should need to know.

You read job posts for entry-level positions and you're attacked by fear and panic.

It seems you'll never meet the requirements until you commit for a lifetime, but they've posted for an entry-level position.

And it doesn't matter if you're searching for an entry-level position as a Python developer (Back End Software Engineer, maybe) or as a Data Scientist.

The situation is the same.

Well, maybe it's worst if you want to be a Data Scientist because the market is very confusing today, and they may ask you for skills typical for Data Engineers.

Not to mention that you may be asked for a Ph.D.

This is frustrating, isn't it?!

Sometimes it seems you've better give up. Why struggling so much? For a high-paying job, maybe? For remote work and flexibility?

Whatever your motivations are, let me tell you that you are right.

But the struggle it's real.

Sometimes you may have difficulties sleeping, because you continuously think about how you can reach the goal to get your first job.

It's just the first job that matters, then you'll be in the field, make experience and everything will be downhill.

Am I right?!?

So, how can you reach your goal with all of these requirements? You may be working full-time and trying to change your career, so you have little time to study and practice.

Also, if you have a family, the time at your disposal is also less and you feel you never get to it.

So, here's the solution to your dilemma.

But I'm warning you: you may not like it.

The solution is simple, and it may annoy you.

And here it is:

you can't!

You simply can't know everything. Full-stop.

So, given that you can't know everything, how do you solve the problem that makes you lose hours of sleep?

How can you get your first job if you don't match all the requirements but also can't know everything?

Here's a process you can follow to reach your goal:

1. You have to stop the fear of “I have to know everything”. I've written a Medium article on how I reached this goal which makes me at peace with myself (read it for free here). You have to realize that if you match all the requirements, you'll never learn anything. The truth is that you just need to match a part of the requirements. Sometimes, a half (something even less it's ok). Don't you believe it? Make applications and interview, and thank me later.
2. You have to understand the code. The real power of a Developer or of a Data Professional is not to know all the needed Python code. Is to understand it when you read it. But to understand it you have to commit to reading the documentation and develop a lot of software, for personal projects. This means that if you've solved a programming trouble by asking ChatGPT or by copying a piece of code from StackOverflow, the best favor you can do to yourself to accelerate your career is to read the documentation of the method you've used, and try and test them in other scenarios. The idea is to use them out of your actual project so that you get confidentiality with the new code you just learned.
3. Write a lot of comments and document your code. Commenting on your code and documenting it gives you another superpower. In fact, since you can't remember everything, when you solve a new coding challenge the idea is to use some piece of code you've used before. But if you used a piece of code just once in the last six months, you can't remember how it works if you don't write proper comments on it. Also, documenting your projects and your code is important. If you're using GitHub to store your code (as I highly recommend!), write a "README" file to document it. Also, when you create classes and functions write proper documentation on how they work (I deepen the methodology in a Medium article. Read it for free here, in particular, the paragraph "Docstrings (and how to invoke them)").
4. Organize your code. One of the goals of programming is to automate stuff. The best you can do is to automate everything that you can automatize. As we know, we encounter similar problems to solve when we program, so we can use similar software solutions we used to solve similar problems in the past. To do so, the best idea is to modularize our code. In simple words, when we create a program we have better create functions and classes in independent Python programs (called modules) and import them in a "main" Python file to use them. This way, you can reuse the modules for future, similar problems to solve. I deepen the concept of modularizing Python code on a Medium article (read it here for free).

"Ok Federico, but how long will this take?!?! I want to change my job now!"

It depends on you and on how much time you can give to reach your goal. It can even need just six months, believe me.

For an entry-level job, of course.

If you want to be more focused, then consider that modularity (point 4) can be performed after a while. So, concentrate mostly on the first three points of the process, and you can leave point 4 to improve your skills and elevate yourself to the next level.

That's it.

Begin stopping the fear that you don't know a lot of things.

You mostly won't, but you'll be able to solve problems because:

• You exercised with a lot of code on personal projects (and have a portfolio to show to recruiters).
• You understand your code because you read the documentation and test it in other situations.
• You documented your code so that, when you see a coding challenge that can be solved with a solution you used in the past, you know where to find it and how to use it.

Remember: it's not about knowing it all.

It's about problem-solving.

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MY LATEST ARTICLES

Here're the articles I published this week:

• If you have some experience with data manipulation, you know that Pandas and Numpy are the kings in Python. Anyway, you may also know that, sometimes, their features don't allow us to perform the analyses we need to do. Maybe the dataset is too big, or maybe you'd need to label your arrays. This is why I found three less-known Python libraries that can integrate Pandas and Numpy to improve our data manipulation stuff: read it for free here.
• Also, on data manipulation, you may want to speed up your processes. Let's be honest: even if we perform some tasks in Pandas every day, we Google how to do something in Pandas daily. To save time, I've created an article on the top 7 operations you can perform in Pandas columns. You can bookmark it and use it whenever you need, instead of Googling each time for searching the same features again. Read it for free here.

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That's all for this week.

Have a lovely weekend,

Federico

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