When Should You Learn Python?

Hey everyone 👋

When should you learn Python?

It’s a good question and one that comes up often from aspiring analysts and even current analysts.

Some will tell you to pick it up right away.

Others will tell you that you never have to learn it.

Neither of these views are necessarily wrong. But they also depend on the type of role you’re targeting and how far you want to go.

In today’s article, I’m going to go over Python’s proper place in the data tech stack with respect to varying data career paths.

Data Analysts

For Data Analysts just starting their careers, Python is not necessary prior to an entry-level to mid-level analyst role.

It’s good to have an understanding of it, as some of these roles will ask for it, but even then, they typically don’t require a strong proficiency.

However, a true entry-level role will usually not require Python. This is most common.

And again, many data analyst roles in general don’t require it.

In the beginning, focus primarily on Excel, SQL, and a BI tool like Tableau or Power BI.

I’ve seen too many people START with Python only to find out they never needed it to begin with. At least not for the type of role they were targeting.

So when should you learn it?

For Data Analysts already in an entry or mid-level role, this is the time to develop proficiency in Python.

Senior-level analyst roles will typically be the ones to require Python.

Because of this, picking it up after you’ve already begun your career in data is to me, the best move.

I should mention that there are still senior-level roles that don’t require Python.

Also, keep in mind that some “Data Analyst” roles disguise themselves as “Senior Data Analyst” roles.

But Python is also becoming an increasingly popular tool so investing in it is a forward-focused career move.

And with the rise of AI, I see Python becoming more in demand.

To recap: Begin taking on Python after you’ve already started a career in data with an entry to mid-level role.

Data Scientists and Data Engineers

I have never been a data scientist or data engineer, so please take this with a grain of salt.

However, I know many of them and have heard their perspectives at length.

For these types of roles, I’ve been told that you want to double down on Python.

SQL and Python are the two top skills for these roles.

This differs from analysts who spend a lot more time in BI tools creating insights from historical data.

But for engineers who design and maintain data infrastructure, and scientists who use machine learning to project future insights, Python is a far more integral skill to have.

In the end, your approach to Python depends on what your goals are and where you’re already at.

Approaching it correctly can save you quite a bit of time.

This week’s YouTube video:

In this week’s video, I lay out a step-by-step roadmap to becoming a Data Analyst in 3-6 months.

That’s it for this week.

See you next time

Matt ✌️ 

Whenever you’re ready, there are 2 ways I can help you:

1 | The Data Portfolio Guidebook

If you’re looking to create a data portfolio but aren’t sure where to start, I’d recommend this ebook: Learn how to think like an analyst, develop a portfolio and LinkedIn profile, and tackle the job hunt.

Join 500+ learners here

2 | 1:1 Coaching Call

For help navigating the data job hunt, consider booking a 1:1 career guidance session with me. There are a few options available to help you get to your ideal data job faster.

Schedule a 1:1 here

3 | Coaching Program

Interested in ongoing coaching? Fill out the form below for more information and to see if we’re a good fit.

Apply here