I don't think 30 is too late at all. Plenty of people switch into data analytics or data science in their 30s (and later). What usually matters more is whether you can demonstrate practical skills and solve real business problems.

The market is definitely more competitive than it was a few years ago, so I'd focus on building a solid foundation instead of trying to learn everything at once.

A roadmap that worked well for me would be:

* SQL
* Excel
* Python
* Basic statistics
* Power BI or Tableau

After that, spend more time on projects than on courses. Tutorials are useful to get started, but employers are much more interested in seeing how you approach real datasets, clean messy data, analyze it, and communicate your findings.

I'd also recommend creating a portfolio with a few end-to-end projects. It doesn't have to be complicated—just show your thought process, explain your decisions, and highlight the business insights you found.

Another skill that's often overlooked is communication. Being able to explain your analysis to non-technical people can make a big difference.

AI tools are worth using because they're part of many workflows now, but it's important to understand the underlying concepts rather than copying generated code without knowing why it works.

If you're coming from another field like finance, healthcare, marketing, sales, or operations, don't discount that experience. Domain knowledge can be a real advantage because understanding the business context is just as important as knowing the tools.

For learning resources, I'd look for programs or courses that include hands-on projects, real datasets, and interview preparation instead of only lectures. If you're considering a specific bootcamp or institute (for example, Technogeeks Data Analytics), I'd suggest reading recent reviews, checking what projects students actually build, and comparing the curriculum with a few alternatives before deciding.

Overall, I don't think age is the limiting factor. Consistency and a strong portfolio matter much more. If you can show that you know how to use data to answer business questions, you'll be in a much better position than someone who only lists tools on a resume.

Is it too late to career shift into Data Science/Analytics at 30?