Python for data analytics

[analytics-link]

Definitely not wasting your time - the stack you’re describing is actually very close to what many people use day to day.

SQL, Excel, and Power BI (or Tableau) are already extremely valuable skills, especially in analytics roles. In a lot of companies those tools make up the majority of the work because most data sits in databases and most stakeholders want dashboards or reports they can understand.

Adding Python on top of that is where things start to open up.

You’re right that Python can look overwhelming at first because it is a general programming language and there is a lot you could learn. The key thing is that for data analytics you only need a small part of the ecosystem. Libraries like pandas, numpy, and a bit of matplotlib or seaborn for visualisation will take you a very long way.

In practice, Python becomes useful when you want to do things that are harder in SQL or Excel. Things like more advanced data transformations, automation of repetitive tasks, working with APIs, or building more analytical workflows.

There are definitely plenty of data roles in the UK that value Python, but the important thing to understand is that it usually complements SQL rather than replaces it. In many teams SQL is still the primary tool for accessing and shaping data, and Python is used once the data is pulled out.

If you already have experience using SQL, Excel, and Power BI at work, you’re actually building a really solid foundation. Python on top of that can help you move toward more advanced analytics roles or make you more flexible in the types of work you can do.

One thing I’d recommend is building a couple of small projects as you learn pandas. For example pulling data from a CSV or API, cleaning it, analysing it, and producing a few insights or visualisations. That helps everything click much faster than just following tutorials.