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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.

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!

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.

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

Suppose the same enterprise website shows different conversion counts in Adobe Analytics and Customer Journey Analytics. Before calling it a tracking bug, what do you compare first: consent scope, identity stitching, event mappings, dataset latency, time zones, or session and attribution definitions?

Which differences do you document as expected, and which point to instrumentation? I’m looking for a practical triage order rather than tool recommendations.

You are looking at your hospital's performance and see two Power BI dashboards showing average patient wait times. One says 28 minutes and the other says 41 minutes.

I wouldn't jump to the conclusion that one of the dashboards is incorrect. I'd want to know how wait time is being calculated in each dashboard. Maybe one measures the time from check-in to seeing a doctor, while the other measures the time before triage. They could both be correct but measuring different things.

Date ranges, department filters, missing data points, etc. There are several things that could cause this. It's always worth double-checking your metrics and how they're being calculated.

This becomes more of an issue when people start using these dashboards to hold themselves accountable or dig deeper into why patients are waiting so long. Even if you have great dashboards, you can still run into problems if everyone isn't on the same page about what a metric actually means.

Anyone else in the analytics/BI world run into this? How do you typically go about finding the cause?

I manage a small Data & Analytics team in a large organisation, supporting several departments. Increasingly, we're doing substantial technical and analytical work, sometimes spending months developing solutions, only for other departments to present the results as their own achievements, with little or no acknowledgement of our contribution.

What's particularly frustrating is that we're sometimes picking up work that other teams were responsible for delivering, whether due to gaps in expertise, planning or follow-through. We step in, resolve the problems and get things delivered, but the original department still receives the recognition. Some of this is probably unintentional, but other instances feel considerably less innocent.

Senior leadership actually recognise and appreciate our team's contributions, and they've been supportive when I've raised concerns. I've tried framing the issue around capacity, prioritisation, ownership and accountability, particularly as our already stretched team keeps absorbing additional responsibilities.

But I don't want to keep raising it with them. I worry about becoming a broken record or appearing overly concerned with credit, especially when they've genuinely been supportive. They understand the problem, but the behaviour across departments hasn't really changed.

Edit to clarify: We have asked that we be included in all presentations where we do the groundwork, or at least invited, and cited or noted in documentation etc - but this tends to be forgotten and we tend to find out after the fact (if at all), I often find when these have happened through the minutes or recordings showing up on sharepoint.

What bothers me most is that I'm responsible for advocating for my colleagues. They put considerable expertise and effort into work that other people get recognised for, and I worry about the impact on their visibility, morale and career progression, which they've expressed too.

I also value our relationships with other departments and don't want to create friction or turn every collaborative project into an argument about recognition.

How have other managers handled this?

Do you address it directly with other departments, establish clearer boundaries around ownership and attribution, or simply focus on making your team's work visible independently?

I'm particularly interested in how you advocate for your team when leadership already understands the issue, but you don't want to keep escalating individual incidents or sounding like you're constantly asking for credit.

Any and all insights, thoughts and advice would be greatly appreciated!

I honestly don't know what to do anymore. In my local job market, tech salaries are stuck between $100 and $180 a month, which is honestly a joke.
The frustrating part is that my actual skills and projects are way past that. I’ve built heavy, complex projects dealing with Machine Learning, Data Science, and advanced analytics. But locally, companies still try to lowball everyone and act like everybody is just starting out, completely ignoring the actual level of work we put in.
I really want to transition to international remote work, but I'm kind of lost on how to actually break through. For those of you who managed to land remote gigs abroad, how did you do it? What platforms or strategies actually worked to get eyes on your portfolio instead of getting ignored?
Would appreciate any real advice or experiences. Thanks!
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Background
MSc in Data Science, about 1.5 years full-time (closer to 2 with internships).


GenAI experience (first year)
- AI engineer intern: fine-tuned LLMs for domain-specific QA generation, worked on hallucination reduction and evaluation, fine-tuned ViT/DETR for document symbol detection.
- GenAI associate at a healthcare product company: built an agentic backend for clinical triage and diagnosis POCs, and an ICD-10 primary diagnosis validation pipeline using RAG and multi-agent reasoning, deployed as FastAPI + Docker microservices.


Current role (about 11 months)
Took a large consulting firm offer for the pay (13.3 LPA: 11.8 fixed + 1.5 variable). The work is attrition and demand forecasting, with classical ML and time series models in production on Databricks, using MLflow and pipelines. No GenAI at all.


The problem
LLMs and agents move fast, and I've spent a year away. My knowledge of agentic systems and LLM evaluation is about twelve months stale, and it keeps getting staler. I'm not on notice and I'm not rushing. I need to relocate away from Mumbai for family reasons (Bangalore, Hyderabad, Pune are all fine).


What I'd like advice on
1. How would you rebuild current depth in LLMs, agent evaluation and AI engineering interview topics in 2 to 3 months while working full-time?
2. What do AI engineer interviews actually ask now?
3. For people who switched without a pay cut: portfolio, referral or interview volume?
4. Does a year of ML + time series on Databricks read as a gap or as useful breadth?

I'm currently working in Spain as a Data Analyst / Analytics Engineer, and lately I've been wondering where I should take my career over the next few years.

My role is pretty end-to-end. I work with SQL, build data models and pipelines, automate processes, connect to APIs, create dashboards, and handle ad hoc requests from the business side. I also work closely with stakeholders to understand what they actually need and turn that into something useful.

The thing is, I'm automating more and more of my own work. I barely write SQL queries from scratch anymore, and things that used to take hours now take minutes.

I'm not really a domain expert in any particular business area either, so I'm starting to feel like a big part of what I bring to the table could eventually be handled by AI.

For those of you who've been in the industry for a while, where would you go from here? What skills or areas would you focus on to stay relevant over the next 5-10 years?

TL;DR: Data Analyst / Analytics Engineer here. I'm automating more and more of my job and I'm worried AI will eventually take over a big chunk of it. What would you learn or specialize in to stay relevant?

I do some analytics engineering work (bronze/silver/gold layered marts in a cloud DW) but I don’t actually know where the semantic layer is.

Google’s GA4 default channel group now includes AI Assistant for recognized assistant referrers, while non-ad clicks from Google AI Overviews and AI Mode remain Organic Search. Pooling those together can make an “AI traffic” total hard to interpret.

A practical review would be:

1. Fix the property, timezone, and a completed date window.

2. In Traffic acquisition, record Session default channel group → AI Assistant, then inspect Session source / medium for the contributing sources.

3. In a separate Exploration, filter Session source to a documented list of assistant domains actually observed in the property. Compare the two definitions instead of adjusting the filter until they match.

4. Break results down by landing page and a selected meaningful key event; use backend or CRM outcomes where relevant.

5. Keep controlled attribution tests separate. A Direct session without a captured referrer or trustworthy campaign tag remains unknown.

A source label alone cannot prove that a person clicked an AI answer, and a citation is not necessarily a visit. If you maintain a custom source list, how do you handle differences from Google’s AI Assistant channel? Have you found an actionable mismatch caused by tagged links, redirects, or app clicks?

Disclosure: I work with SEOVisitor. AI helped edit this post. This summarizes a guide our team prepared; the method is included here so the discussion does not depend on an external link.

AI is being added to almost every tool now, but what would you genuinely feel comfortable handing over to it?

In a B2B HR software test, we changed three things:

• "Get Started" became "Talk to an HR Solutions Expert."
• We added copy about the business benefits.
• The button changed to a darker orange.

Across 50,655 users, form-submission rate went from 1.59% to 1.79%, a 12.6% relative lift.

That gives us a result for the complete variation. It doesn't tell us how much came from the wording, the supporting copy or the color.

I'd use that test to decide which version to run. If we wanted to claim the wording caused the lift, a follow-up would need to hold the other elements constant.

Testing several changes together can answer a useful business question. The mistake is turning the result into a rule about one of those changes.

I understand data analytics and data science can both be predictive or descriptive, but in general, in the industry this is mostly how the jobs are split correct ?

Data science is more predictive like what will happen

Data/Business Analytics is more descriptive and diagnostic like what happened, whats happening, and why

Data engineering moves the data

Business analysis is analysing processes and prescribing solutions

For analytics apps that do need fresh data , would u use Lakebase for serving results or query the warehouse directly ? Or some other solution similar to it? What has worked better for you in tour org/experience ?

The last time I looked for a job was 4 years ago. I received 2 jobs offer after 4 weeks of looking and very good callback rate for jobs I was qualified for. I started looking again this year, tailoring my resume and only applying for jobs I qualify for.

Took me 1 month just to get a single interview and very poor overall callback rate despite using AI to only apply for jobs that I was a match for.

I heard people tell me how bad it is out there and now that I’ve tried it myself it really is that bad! I think I will have better luck doing an internal transfer but this job market really is garbage 😂

I'm a Data Analyst planning to build my first Databricks project for my portfolio. I have experience with SQL, Excel and Power BI, but I'm new to Databricks. Could you suggest some good project ideas that would help me demonstrate Databricks skills from a Data Analyst perspective?

I'm trying to decide what to put first in my learning: SQL for getting data into shape, or Python for doing more with it. For someone early in analytics, which gives the better foundation?

Recently applied for a position and heard back from a recruiter for a technical screen for an R&D analyst role! Does anyone have advice on what questions to expect for a 30-minute interview?

I had a job in analytics and am starting to realize that most things can be broken down into smaller sections regions - states - locations or brand - category - item. Is this what everyone realizes or is this just me?

Overstock, stockouts, ordering too early, ordering too late...curious what others have learned the hard way.

hey everyone, sharing this because it’s directly relevant to analytics teams that are being asked to put AI agents on top of company data.

packt publishing is running a hands on workshop on Oct 31 on harness engineering, building an AI data analyst you can actually trust, with bruno goncalves.

what gets covered:

• timeouts, retries, re planning and execution budgets, so an agent run can’t loop or burn spend
• structured traces, so every run ends in a state you can inspect afterwards
• permissioned tool access, so risky queries are blocked before they reach your data
• a fault injected test suite, where you break tools on purpose and check the agent stops safely instead of inventing an answer

4 hours, online, Sat Oct 31, 9:30 AM to 1:30 PM ET. runs on a laptop with a mock model, so no GPU or API key is needed.

Full details and the workshop link is in comments

happy to answer any questions about what gets covered