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I see a "how do I get into analytics" post here almost every day, so here's what I'd actually do if I was starting over now.

Quick context: when I started, everything was manual. Now AI can write a lot of the SQL for you. The technical bar to get in is lower than ever, and what separates people is whether they understand the business problem. Plan for that.

1. Learn the basics, don't overthink the course

Udemy or YouTube is fine (Alex the Analyst is great, and free). SQL first, then Excel, then Power BI or Tableau. Aim for "I can pull data, clean it and chart it", not mastery.

2. Don't start applying yet. Solve problems.

Biggest mistake I see: everyone grabs Superstore, builds the same sales dashboard, and ends up with the same portfolio as thousands of other applicants.

Don't build a dashboard. Solve a case study. Start from a question a manager would actually ask, work it out, and write down what you'd recommend.

Full disclosure, I'm building one of the places to do this, Trialday.co . You run analytics for a simulated company end to end: messy data, questions from colleagues, decisions that change what happens next. It's free during early access (invite only, there's a waitlist). If you'd rather not, Danny Ma's 8 Week SQL Challenge is also free and good.

3. Put the thinking on your resume, not the tools

"Found X was driving Y, recommended Z" beats "Built a dashboard in Power BI." With AI doing more of the technical work, showing you can solve a business problem end to end is what stands out.

4. Skip the HR queue

Instead of hitting apply 200 times, message the analytics manager or director where there's an opening. Short, specific, one project link. In my experience the reply rate is way higher, because they're the ones feeling the empty seat.

Happy to answer questions in the comments.

I come from an FP&A background with 3 years of experience. I left my job over a year ago at this point because I wasn't satisfied with the work I was doing at the time and wanted to take some time off to figure out a new roadmap to a different career. I'm really considering getting into IT at this point, roles like an IT analyst seem achievable given my background, although im really open to anything IT related (help desk, cybersecurity, etc.). The part im still trying to understand is the exact roadmap, what steps should I follow to make this transition happen? Should I narrow down a job title and find certifications to strengthen my resume? Should I consider going back to school? Any help would be greatly appreciated.

We’re adding customer-facing analytics to our app and customers keep asking if they can type a question and get a chart back instead of using filters.

Has anyone shipped something like this to real customers? We’re not looking to build in-house

Hey everyone,
I’m currently based in Vietnam and planning a career transition to the US. I have around 8 years of experience in fraud risk at Standard Chartered, and I’d like to get some honest advice on how to position myself for the US job market.
A quick summary of my background:
Payment Fraud & Mule Risk: Experience managing country-level payment fraud, designing processes to align local regulations and Group standards, and creating/fine-tuning fraud/ money mule detection rules.
Tools & Systems: Experience with ThreatMetrix
Other areas: Basic exposure to card fraud and chargebacks, plus general fraud investigations, both external and internal fraud
I want to stay in the Fraud Risk / Financial Crime , but I’m wondering:

1. Which roles should I target? Given my background, what job titles would be the best fit?
2. What should I focus on improving? Are there specific technical skills (like heavy SQL/Python), certifications (like CFE), or US-specific regulations I should study to make my profile more competitive?

I'm actually moving to the US through family-based immigration path so I wont need any visa sponsorship from employers.

I’m curious about how data analysts can go beyond traditional reporting and actually help businesses improve sales and identify missed revenue opportunities.
For example, how would you approach analysing which leads have the highest potential to convert, how they should be prioritised, and how to allocate them more effectively to sales teams?
I’m also interested in whether machine learning or AI could be useful for this, or whether simpler approaches using SQL, Python and historical data would be more practical.
Has anyone worked on similar projects? What approaches did you use, what challenges did you encounter, and how did you measure whether your recommendations actually improved performance?
I’d really appreciate any advice or ideas!