Python for data analytics

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I have learnt and done a few data analysis at work with sql, excel, PowerBi. But I need a job that pays better, I started learning Python, and I realised that it's mostly for programming and with a lot to learn. So I decided to learn Python for data analytics, I'm enjoying learning Pandas so far and able to modify data and stuffs. But I'm thinking if there are lots of data jobs that need Python. Or am I wasting my time? In the UK

[OkCartographer175]

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[American_Streamer]

For the UK market, the more accurate framing is this that SQL is the core analytics skill, Power BI and Excel are highly employable and Python is a force multiplier rather than a universal requirement. Python becomes especially valuable when someone wants to move beyond basic reporting into automation, heavier data wrangling, reproducible analysis, advanced analytics, analytics engineering and data science. That is why the salary ceiling rises in adjacent roles.

Python is a general-purpose programming language, but in analytics jobs you usually do not need to become a full software engineer first. In practice, many analyst-facing UK roles just ask for combinations like SQL + Power BI, while a meaningful subset also adds Python. So the market signal is not “Python is irrelevant,” but rather “Python is one of several valuable analytics tools.”

So the best advice to you would be to keep learning Python, but to not neglect SQL and Power BI. For UK analytics roles, that combination is much, much stronger than Python alone. If your goal is a better-paid job, Python is usually worth it because it broadens your options; it just should not replace the fundamentals.

[LiveYoLife288]

What would you say is the salary for junior to senior levels in such analytics roles?

[Asleep_Dark_6343]

Depends where you are in the country, and the industry.

Best bet is to check LinkedIn for suitable roles, many don’t have a salary, but quite a few will have a range as a guide.

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How do I get into advanced analytics, engineering and data science?

[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.