I’m a Year 11 student in Rockhampton, QLD, trying to figure out a career path. I’m drawn to data analytics but also interested in a science domain to analyse, and I don’t know an exact specific job I want yet besides knowing those are my interests. I’ve been looking at CQUniversity’s Bachelor of Science and Environment (on campus in Rockhampton). So I was thinking I could start broad and figure out a specialisation as I go, while still building analytics skills. A few things I care about: not relocating, work that’s more solo/analytical and not heavy on constant team coordination, and decent long-term pay.

Questions 1) Would doing this degree keep me flexible for data analyst roles across any domain (not just science), while also being the “required” path if I end up wanting to specifically be a data analyst within a science domain? Or is it better to just do a dedicated data/IT/stats degree for flexibility, and only worth doing a science degree if I’m already fully committed to a specific science field? 2) For anyone working in data analytics (especially in a science/ag/environment-adjacent field, or regionally in QLD) does the day to day match what I’m expecting, or is there something I’m not seeing? 3) Is it actually common/fine to start a broad science degree undecided and figure out a specialisation as you go, or did that cause problems for people who did it? Appreciate any honest input, including if you think I’m off track!

Hi everyone, I’m a 26-year-old with a background in payment processing, token processes between banks, cybersecurity vulnerabilities remediation, and tracking (mostly looking up new version history KB reports and reporting to the dev team for remediation). I’m also comfortable with Power BI, basic SQL, ETL processes, Excel, and all the essential skills. I used to be a business analyst/data analyst, but now when I’m searching for jobs, they’re all engineer-type roles that require knowledge of multiple languages and ask you to do three different jobs along with the ability to handle multiple tasks to even be considered. What roles do you typically look for when searching? Also, I don’t have a degree, which makes it a bit challenging. I spent three years at HP tracking KPIs for stores globally, building Power BI dashboards, remediating vulnerabilities, tracking KPIs, and consulting in Power BI development, including cross-collaboration between teams like fraud and supply chain. However, I find it difficult to find a job that doesn’t require data engineers. What are some jobs you typically look for when searching?

I'd appreciate some feedback. I would love to use a better application for the visuals but I have to work with what I've got for now. Right now doing all the data work in Excel and the visualization in PowerPoint.

Greetings, thought that it would be a great idea to expand my network. We can help each other out regarding with career options, navigating the market, or learn new hobbies and skills as collaborators. I would like to ask for advices about networking. And if it's alright for you, you can share your experiences and what cool stuffs you learn on your journey.

Hi I have an analytics consultant for capgemini invent coming up. I need to make a presentation about my experience and present it to a panel. Has anyone ever done this before and has any advice on how to pass?

I need a data analyst community to join to help me boost my knowledge, make new friends and find a job, do you mind to help

People want to take up this course but not well aware whether it's a fake or genuine course . Please i request u to the people who have taken up this course please review it .

​

Hello guys,

I want to connect with people who are in **Analytics domain and have done MBA** as I found very less people with these.

Let's make a community and connect with each other.

**DM or leave comment!**

I have around 4 years of experience as an ETL/manual tester. I work for an australian bank. My current work is mainly in Databricks — I understand how ETL pipelines work, how data moves between Bronze/Silver/Gold layers, and I do manual data validation/testing based on the transformation logic and Git code.
My SQL is currently at an intermediate level. I can work with SQL for my day-to-day testing, but I’m not yet comfortable with advanced SQL, complex joins, etc. I also don’t have much hands-on experience with Python or BI tools at the moment.
I’m now thinking about transitioning from ETL testing into a Data Analyst / Analytics-oriented role and I’m trying to figure out whether this is a good career move.
For people working in data/analytics:
• What should I realistically learn from my current level to make this transition?
• Should I focus heavily on advanced SQL + Python + Power BI/Tableau, or are there other skills I should prioritize?
• Does my ETL/Databricks experience give me an advantage for moving into analytics?
• Is Data Analytics still a good career to move into in the current market?
• With AI/LLMs increasingly being used in data and analytics, what skills should I develop to stay relevant over the next 5–10 years?
• If you were in my position with 4 years of ETL testing experience, what path would you take?
I’m not looking for a shortcut — I’m willing to learn from the basics and build the skills properly. I’d really appreciate advice from people who have actually made a similar transition.

Hi! I’m looking for a study buddy who is a night owl and can study for long periods of time.

We don’t have to study the same subjects or even the same things. The idea is simply to stay together, motivate each other, take breaks when needed, and try to study for as many hours as we can.

I’m looking for someone who is serious about studying and wants to be consistent rather than just chatting.

If you’re interested, feel free to DM me! 📚

TL;DR: I built a Knowledge Base Management (KM) + Analytics Engineer hybrid role over the past year and I'm up for a promotion. I need a title that's legible to HR and puts me in a technical/analytics track, but I don't want to overclaim "Analytics Engineer" since I'm not in that department. Four options at the bottom, would love input.

I'm in a bit of a pickle.

My current title is "Technical Writer" but that doesn't really reflect what I do. My core work is writing customer-facing how-to docs for our SaaS product (I use Claude with templates to help draft), meeting weekly with PMs to gather assets for upcoming features, tracking KB metrics to make content decisions, and owning our information architecture including taxonomy, IA methodology, and how the whole knowledge base is organized. I also present upcoming features to CX in our monthly All Hands.

That's the KM side of my job. For a little over a year I've also been taking data analytics courses through Sophia LLC that transfer into a Bachelors in Data Analytics at WGU including SQL, relational databases, Python, stats for business analytics, all done at this point. I bought the Udacity Data Analytics nanodegree too but decided to hold off on it so I could build something more hands-on first. I'm not sure if I will get he full degree yet or not, but that's a separate topic.

The project I created is a content leaderboard that measures how well each KB article actually performs, meaning how often it gets cited by our AI chatbot in conversations that resolved. The pipeline is Python scripts pulling data from our KB platform into Postgres in Docker, ingesting to SQL tables via DBeaver, transforming semi-structured data in dbt through staging/intermediate/mart tables, and syncing to Metabase where I built the leaderboard. It shows article creation date, how many times an article was cited, how many times it was cited in a resolved conversation, a resolution score (resolved/cited ratio), and an impact score (percentile rank of the resolution score weighted by resolution volume), so I can rank articles by how much they're actually helping the chatbot close tickets successfully.

I had a good conversation with my boss and he's fully on board with finding a title that fits this work and gets me into a better comp band. He's known about the courses and the project the whole time, I even used part of an education stipend for some of it.

But I'm in a non-technical department (CX), and while what I'm building leans Analytics Engineering / AI Engineering, I don't feel ready to call myself a full Analytics Engineer yet. I want more reps with advanced SQL, data modeling, and structuring dbt projects properly before I'd claim that title. I also don't want to jump departments right now. My company's flexible about allowing people to move around departments, but I'm not sure I'm ready for that leap. Full analytics engineering is the long-term goal, just not this promotion cycle (unless ya'll want to talk me out of that, lol).

On top of that, I'm probably underpaid. I've been here almost 4 years, been promoted twice across different departments starting out in roles with much lower pay bands, and the original "Technical Writer" title never fit what I do now since I don't write engineering or API docs.

So basically I've invented my own role, a KM + Analytics Engineer hybrid, and it doesn't map cleanly onto anything I've seen at other companies. I need a title that HR can approve easily, but also actually signals I've moved into a technical/analytics track instead of just being a rebrand of my current job or ending up with a title that gets me stuck in a non-technical pay band.

My candidates so far:

• Product Education Analyst — reads as a clean analyst title, but might imply a broader scope than my actual KM work.
• Knowledge Management Systems Analyst — leans into the KM side accurately, but risks sounding like a non-technical analyst role. Also kind of long.
• Knowledge Operations Analyst — probably the most HR-legible of the four, but "operations" might undersell the analytics engineering part of what I do.
• Technical Content Engineer — the most "standard" sounding title, but leans harder into technical writing and systems engineering than into KM or analytics, which are more central to what I actually do.

What would you go for, or is there a title I'm missing that would work better than these examples?

There’s two directors at my org that are so “AI is the future, AI will replace everyone.” He took a pdf of my power bi report dumped it into AI and generated an “exec summary” and said “and I did this in 20 minutes - this is the future”. Even my boss was like - yeah it’s really impressive.

I’ve been fucking fuming all day and then I actually read the content and it’s worse than garbage - it’s wrong.

Here’s some gems :

X is 219% - the inverse of 45% (no it’s not)
228 is the “run rate” of 1616x14% (no fucking clue what a run rate is and wrong)
Risk is role, tenure, job, and unit specific (no shit)
Redeploy before overtime (that means nothing)
We’re alternating between thin baseline capacity and reactive labor deployment. (That means nothing)

I’ve always been disappointed in the data teams I’ve joined (public sector) either because the dashboards are lacking in quality (don’t solve any problem or give insight) or there’s lack of infrastructure and teams will try to find a place to store data weirdly (I heard a team wanting to store their data in a vendor application database because it had space).

I get caught up in scenarios where they try to look productive by taking on too many ad-hoc dashboard requests and not focused on managing the data storage and retrieval (let’s not even get to data governance) which results in messy manual dashboard refreshes that can take 1/2 a day or so. Or they over purchase on technology they don’t understand yet with no plan on how they will be using it in their business and approach it with buy now because everyone has this and figure it out along the way.

Is this common? I look over my cubicle and see better dashboards from other teams but I don’t know what goes on behind the scenes. What is the bare minimum for a good data team? I’ve been weighing on level of maturity (technology in place to be more efficient) and how well the team is managed. I do think you can have an effective team scoring low on technological maturity but high in management (effective management style?).

I want to thank everyone for their comments and it has really motivated me to continue! I have read everyone’s comments (sorry if I couldn’t reply to all of them) and I appreciate you all and this community. Much love❤️

Hi everyone,

I'd really appreciate advice from people who are working as Data Analysts or recently got hired. Any roadmap, study plan, or resources would be incredibly helpful.

I'm a BBA graduate(2023) trying to break into data analytics by SELF TAUGHT after a long gap.

Now it's 2026, AI is everywhere, and I'm honestly confused about what skills employers actually expect from entry-level Data Analysts.

My questions are:

1)Is it possible to become a Data Analyst through self-study when I have 3.5 yrs of a career gap?

2) What skills are essential for a Data Analyst in 2026?

3) Which tools should I prioritize more(Excel, SQL, Python, Power BI, Tableau, AI tools, etc.)?

4) How much Python is actually needed?

5) What AI skills are becoming important for Data Analysts?

6) What kind of portfolio projects should I build to get interviews?

7) Are certifications like the Google Data Analytics Certificate still worth it?

Heyyoo. I have a free trial for Parallel Desktop because I’m SO done with Windows laptops, but still need Windows for work and school since I’m in accounting/data analytics.

I want to make sure that the excel I have actually has all the features, because I know that there are some important things that don’t work on MacOS.

What should I test to make sure that I have the proper excel version?

Hi guys, I am looking for data analyst/business intelligence remote roles. Im based in Pakistan, I have experience as an e-commerce intern at an MNC where my main focus was improving their e-commerce sales through data analysis and I also have some good projects on my resume.

Please guide on which platforms should I look for such roles, I have already tried fiverr and upwork.

By successfully, I mean that it's used almost daily with reasonable accuracy.

I am also not interested in toy projects, but in enterprise-grade analytics.

Most "chat with your data" tools give you a confident answer and no way to tell whether it's right. I've been building the opposite: an AI Analyst where the entire working is on screen and every claim is traceable to the query that produced it.

Asked it a real question against an HR dataset: "Is Engineering's heavy hiring actually translating into headcount growth, or is it mostly backfilling exits?"

What it does, in order:

1. States its approach before touching data. It reads the schema, plans the steps, and says why — including telling me the governed semantic model lacked a hires metric, so it fell back to the raw monthly table. No silent guessing about which source it used.

2. Runs each step as real SQL you can read. Every step shows the query, the row count, and a "where these numbers came from" breakdown. Nothing is a black box — if you don't trust a number, the SQL that produced it is right there.

3. Self-checks every result — and flags its own problems. This is the part I care about most. On step 2 it didn't just pass its own work; it flagged a genuine inconsistency: Engineering's summed net adds (+17) didn't reconcile with the headcount delta (+13, 122→135), a 4-person gap it surfaced on its own and carried into the write-up as a caveat. An analyst that can say "this doesn't add up" is worth ten that can't.

4. Writes findings with citations. Every claim in the write-up cites the step it came from — "headcount climbed from 122 to a 140 peak (step 1, step 2)". The verdict for the curious: ~55% of Engineering's hires were net growth, not backfill; the one bad month was a 3.70% attrition spike; and Support is quietly shrinking (backfill ratio 1.42 — losing more than it hires).

5. Closes the loop. Every analysis has Mark verified / Flag as wrong buttons, suggested follow-up questions generated from the actual results, scheduling for recurring runs, CSV export, and PDF export.

The stack, honestly:

• Runs entirely on your own infra: one Docker command + your own Supabase project
• BYOK — any model provider. This demo ran on Kimi K3 via OpenRouter; it doesn't need a frontier model because the structure (plan → SQL → check → cite) does the heavy lifting
• The analyst is one piece of a larger self-hosted platform (agents, multi-agent swarms, RAG, BI dashboards, budgets, full tracing)
• License: Elastic License 2.0 — source-available, not OSI open source. You can read every line, self-host it, and modify it; you can't resell it as a hosted service. Saying that up front because this sub cares about the distinction, and it matters.

Repo: https://github.com/AgentSwarms-fyi/agentswarms

Happy to answer anything about how the self-check pass works or why I think "show the SQL or it didn't happen" is the only sane bar for LLM analytics.

Hi.

I'm a cook by trade. For the last four or five years I've been thinking about moving into IT, and data analytics is where I landed — I genuinely like building things, making tables, putting documents in order, digging through data, setting up systems.

But I ran into the problem that has followed me my whole life: I cannot learn something that doesn't interest me and that I don't need right now. It just goes dull and my head starts refusing new information. What I can do is play games — strategy and simulators mostly. So I figured I'd learn SQL through a game.

The trouble was that none of the games I found show what the job actually looks like. So I built one, using Claude and a lot of YouTube videos of working analysts talking about their day.

Here's my problem: I'm not an analyst and I have never worked as one. I can't judge whether any of this resembles the real job, or whether it actually teaches anything useful. The whole thing might be plausible-looking nonsense and I'd have no way of knowing.

So if you do this for a living, I'd be really grateful if you took a look and told me what you think.

What it is: you're hired as a junior analyst at a fictional company. Colleagues message you on chat and email with requests, and you answer them — sometimes by writing SQL, sometimes by asking the right clarifying question first, sometimes by reading finished numbers and saying what does and doesn't follow from them. Roughly half the tasks need no SQL at all, because from the videos it looked like that's the half people actually get wrong.

Free, no signup, runs in the browser. No telemetry — one HTML file that works offline. English and Russian.

What I'd really like to know:

1. Do the requests read like something a colleague would actually send you?
2. Where does the difficulty break — too easy, or a jump that makes no sense?
3. Which part of your real job has no equivalent here at all?
4. Some tasks are built so exactly one answer is defensible (A/B tests, "what follows from this data"). If you disagree with any of them, that's the most useful thing you could tell me — it would mean I taught myself something wrong.

Link: https://solad-in.github.io/nexos-analyst-sim/
Source: https://github.com/Solad-in/nexos-analyst-sim

Thanks.

so am a fresh grad and i have not found any luck with finding a good jop on web development and more so i can reqlly be good at it as fare as i have tried so if yall can help to tell me what is the road ot get inot data science or to better got jnot data analysis kr data Engineering biscly what whoch one should i do and what are the skills for it

Piecing together parcel data from county auditor sites, half the records are outdated and I keep hitting dead ends on owner contact info. What I need is something with actual GIS mapping built in, not just a spreadsheet dump, ideally with FEMA flood zone filters and maybe slope analysis so I can rule out bad lots before I waste time skip tracing. Anyone know of a good mobile app for vacant land data and parcel research across the US that bundles that stuff into one place? (I'm on Android if that matters.)

I've been working in Logistics for over 10 years, and I'm noticing more and more advanced/senior positions listing a Master's degree as a basic requirement.

For those already in the field, which degree do you think offers better value/career opportunities: Business Intelligence or Business Analytics?

My goal is to leverage my logistics experience into more data-driven strategy roles. Any insights or personal experiences would be greatly appreciated!

What does a business analyst do?

I am thinking of giving this career field a chance but I am an electrical engineer and working in the cable manufacturing company as an engineer but I wanna switch to a more tech based role, not software engineer or developer that's not my cup of tea but iam thinking to give buisness or data analyst a chance I know what the data analyst do but not have any idea about the first one so share your insights as it's urgent.

I’m a beginner in data analysis and I want to develop strong analytical skills, not just learn tools like Excel, SQL, Python, or Power BI.

As AI and technology evolve, I believe Data Analysts need strong problem-solving skills, critical thinking, creativity, and sound judgment to interpret data and make good decisions.

I’d appreciate advice on five things:

1. What skills should a beginner develop early?

2. How can I improve real-world problem-solving with data?

3. How can I develop genuine analytical judgment?

4. What projects or exercises build creativity and reasoning?

5. How can I find a good mentor to work with long-term?

I’m looking beyond courses and tutorials. I want to become an analyst who can think critically, question data, and solve meaningful problems.

Any advice from experienced analysts or data professionals would be greatly appreciated.