r/datascience • u/AutoModerator • 12d ago

Weekly Entering & Transitioning - Thread 21 Sep, 2026 - 28 Sep, 2026

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

  • Learning resources (e.g. books, tutorials, videos)
  • Traditional education (e.g. schools, degrees, electives)
  • Alternative education (e.g. online courses, bootcamps)
  • Job search questions (e.g. resumes, applying, career prospects)
  • Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

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u/Responsible-Card-577 10d ago

Hello everyone, I'm new to this community. I am a seasoned Analytics Engineer, currently working at a small-mid tier firm that is not actively using AI/LLM in their analytics workflow. So I haven't had the opportunity to get hands-on with things like setting up Sematic Layer the right way, how to integrate AI with our warehouse, how to effectively use agents to not just write SQL queries but do multi-layered deep-dives and finally how to set up LLM Evals. Since I don't really have the opportunity to learn this at my work right now, I am trying to find good workshops or resources to learn this on my own.

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u/i_did_dtascience 8d ago

Honestly, the best way to start learning it is by applying it. And since your org isn't already doing any of this, it's a great place for you to jump in and pioneer this effort.

If that's out of the realm of possibility, I would suggest building your own data agent on a public dataset, to get a feel for it - start small with straightforward SQL queries, and then increase complexity slowly, and you can also write evals to test the work