I reviewed my tech joke like it was a pull request. First question: will it expire with the next trend or buzzword? Second: can someone read it from six feet away? Third: does the joke accidentally reveal something too specific from work? The third check caught me.

I changed the mockup and ran the joke through the checks again. It still worked without the private reference. That was the useful result. A company metric, internal dashboard, or current industry phrase can make a joke feel current, but it also gives it a short shelf life.

That is the lane I would want from a Geeksoutfit coding design. Generic enough not to expose a workplace, specific enough that programmers know exactly what happened. "Works on my machine" survives because the habit survives. My private ticket joke did not.

What would fail your code review first: the joke aging badly, being unreadable, or leaking too much context?

Hey everyone,

I am actively looking for a remote, unpaid data analyst internship or volunteer role to gain practical experience and contribute to real-world projects.

Skills & Tools:

• Advanced Excel: Data cleaning, nested formulas (XLOOKUP, INDEX/MATCH, SUMIFS), dynamic PivotTables, and Power Query workflows.
• SQL: Relational database querying, aggregations, CTEs, window functions, and joining complex datasets.
• BI & Reporting: Data modeling, building automated KPI dashboards, and basic DAX.

What I am looking for:

• Open to contributing to startups, small businesses, non-profits, or open-source initiatives.
• Flexible on hours and fully committed to meeting deadlines and project scopes.
• Looking primarily for feedback, mentorship, and the opportunity to solve real business problems.

If your team needs an extra pair of hands to clean data, build reports, or automate routine spreadsheets, please drop a comment or send a DM.

Thanks for your time!

I am almost at a year at my company working on a low code etl tool. I’ve gotten to think a lot about what I want to do and where I want to be in my career and I was wondering where to start on how to transition into more of the analytics side of things. Currently in my job it’s essentially a graph based sql with the occasional query within each component. But it just doesn’t give me the satisfaction that I would get then if I were actually coding. I think some of the skills I learned can be transferable in some way but i would like to not do this for another year if I could. Any tips on how I can make a transition?

Hi everyone! I’m kind of stuck on this and would really appreciate some advice.
I’m currently doing two bachelor’s degrees, Data Science and Business Information Systems (BIS). I have about 2 semesters left. I’m almost done with Data Science (only 2 courses left), but I’m only about 60% done with BIS.
The two programs are in different schools at my university, so there’s basically no overlap between the classes. I still have quite a few BIS core classes left, and lately I’ve been wondering if continuing is really worth the extra tuition, time, and workload.
I originally chose BIS because I thought Data Science + BIS would be a good combination for the kind of careers I’m interested in, especially data/analytics/BI. But now I’m honestly not sure.
I could keep going with BIS or just finish Data Science and graduate. My university doesn’t offer a BIS minor, only a general Business minor, so that’s another option I’m considering.
Since I’m already halfway through BIS, I’m having a hard time deciding whether I should just stick with it or let it go.
What would you do if you were in my shoes? Is finishing the second bachelor’s actually worth it? Any advice or perspective would really mean a lot! 🙏

Am I the idiot here? AI is so good the RMDs and Shiny apps I used to struggle in writing can be generated in a few days. Now that I can offload the CSS/JS part of the dashboard to the AI I can go nuts with reactivity/styling.

Based on the discussions in Reddit pbi/tableau/looker remains popular as ever but those things are expensive and are limited by the platform. I can see use for cheap and lightweight services like metabase where I can write the sql query and generate the dashboard in less than an hour but what about pbi/tableau? These services are expensive yet they remain popular what are the upsides of these products over say hosting my own shiny server/react/streamlit dashboards?

Like if I want an app that allows the user to define the cohorts themselves, save that definition, then load that into other dashboards/for future reference I can do that in Shiny but no idea about PBI.

What really is a semantics model? Why not just write a package that calculates the kpis then document it? The documentation also serves both the technical and non technical viewers alike.

Never been in a pbi/tableu job I don't understand how these products remain popular.

This has been bothering me more than bad data ever did you can have a clean dashboard everything reconciles nothing is obviously broken and somehow two people can look at it and walk away wanting completely different things paid looks healthy, sales feels slower finance is looking at another number entirely. The weird part is they can all be right because they’re measuring different pieces of the same thing.

I’m starting to care less about making reporting prettier and more about whether somebody can look across all of it and decide what needs to change more dashboards haven’t really fixed that for me. If anything they sometimes make it easier to avoid the messy part where someone has to connect the numbers back to what the business is trying to do

hey everyone, wanted to gauge what the best strategies are as of August 2026 in terms of networking and recruiting for new positions. for context i’ve been working as an entry level analyst for about a year following my graduation in may 2025. looking to pivot into a less ad/hoc request based role (pulling reports and sending them off) but i feel like a fish out of water in this new meta of using AI in every stage of the outreach, application and interview process lol. i have no idea where to start.

what if I studied excel, sql server, python and power bi and made projects on them while I was learning them but after moving to one after one maybe I feel I forgot them what's the best advice from you to memorize what I learnt?

I thought it would’ve been obvious to a lot of people but since they’re a top 1% commenter in this subreddit I guess it isn’t.

Here are examples from the past week of their comments getting a lot of upvotes:

https://www.reddit.com/r/analytics/comments/1vv9b1x/comment/p576in8/

https://www.reddit.com/r/analytics/comments/1vx8kmq/comment/p5mvzp5/

https://www.reddit.com/r/analytics/comments/1vuqdwc/comment/p538ufk/

Here’s a post written about that specific account being a bot written by someone else: https://www.reddit.com/r/cscareerquestionsuk/comments/1thm4q6/psa_llm_bots_are_commenting_on_every_post_on_this/

Here are examples from that past of accounts with the exact same characteristics that were also bots:

https://www.reddit.com/r/dataanalysiscareers/comments/1smzsbt/fyi_uchocolate_asshole_is_a_bot/

https://www.reddit.com/r/dataanalysiscareers/comments/1stlf4h/fyi_bootyhole_licker69_is_a_bot/

Hi team CTM here, joined a new company, they use/ are assessing tools Aika (biorce) and ClearTrial which is by oracle. I used oracle in thermofisher, but unfamiliar with cleartrail, keen to get the inside scoop!

I have been working as an HRIS Analyst for about 2.5 years and am currently in my second analyst position.

In both of my positions, I have been the sole HRIS analyst for both companies and handle all reporting out of the HRIS. I am responsible for fulfilling all ad hoc report requests as well as creating power BI dashboards for executives to view metrics like turnover, training completion rates, offer acceptance rates, etc..

I am 100% self taught and have never worked on an analytics team. I currently source the data, clean the data, build semantic models, present dashboards to end users, make changes based on feedback, and handle all maintenance requirements for all dashboards using HRIS data.

I don’t feel I get paid enough to be a one man show and I’m curious how work is divided when you actually have an analytics team. Is the person who is building visuals and dashboards in power bi usually the same person that is sourcing and cleaning the data?

Any insight on how your analytics team is organized is appreciated. Thank you!

Hello,

I am interesting in transitioning into a career in healthcare data analytics and was hoping that someone already in this field could help give me some honest advice.

I have my Bachelors of Science in Health and Exercise Science. I worked as a Physical Therapy tech for a while and I currently work with insurance referrals for the same company I was a PT tech for. I have been in this clinic since I graduated from college (2 years). I recently have started looking into Healthcare Data Analytics and I think it could be a really good fit for me. I know I will need to learn how to use programs like Excel, SQL, Power BI and others. How else can I set myself up for success? And would I be able to get into healthcare data analytics with my current degree?

I know that breaking into this field can be hard. What should I be doing to set myself apart from other candidates? What positions should I be looking for to apply to?

And my other concern is that AI could take over this job. From what I’ve seen Healthcare is different from regular data analytics and seems to be a more secure route? But it is still a concern. If someone in the field could be honest with me about what the job field is going to be like with the implementation of AI, I would greatly appreciate it.

I find Polars to be fast and useful in some cases like loading the dataset faster and handling multiple instances of the dataframe without lag. But how much of the polars is actually used in Professional workspaces? Can you assess it whether it is a potential skill while in job search?

Been doing marketing analytics for a while and had a bit of a "duh" moment recently.

Every week it's the same loop: pull Google Ads, pull Meta, pull GA4, reconcile the date ranges, fix the naming mismatches, build the report, then finally answer the question someone actually asked me, like "why did CAC spike last week."

The data was never really the problem. It's always been sitting there. The problem was the 30-45 minutes of stitching it together before I could even start answering anything.

I started messing around with an MCP connection (Windsor.ai's, specifically) that lets you pull connected marketing data straight into Claude or ChatGPT and just ask it questions directly instead of building a report first. So instead of "let me pull this into Looker Studio and get back to you," it's closer to "here's what's driving the spike, and here's the channel it's coming from."

Not saying it replaces a proper dashboard for ongoing tracking, still building those. But for the one-off "why did X happen" questions that used to eat half my afternoon, it's cut that down to a couple minutes.

Curious if anyone else here has started using MCP style connections for ad-hoc analysis vs. building a full report every time, or if you're all still doing it dashboard-first.

Now- should we even focus on making warehouse

Hi everyone,

I have a question for people who developed their own software before eventually working with, or joining, a company that wanted to use it.

I’m very interested in sports analytics and have spent a long time developing my own analytics software. I’ve recently been contacted by a few sports teams through my website, and some of them are interested in working with me and using what I’ve built.

The problem is that the software was originally built for me to operate, not as a polished product that I could simply hand over to somebody else.

Right now I use the software myself, analyse the data, and then produce reports for the teams. The software itself is quite complex and has a lot of interconnected parts, and realistically I think someone would need months of training to understand how to use it properly and understand what the different outputs actually mean.

That leaves me unsure about what the best setup would be if I started working with one of these teams.

For people who have been in a similar situation:

• Did you keep operating your software yourself and provide the company with the results/reports?
• Did you eventually train employees to use it?
• Did the company expect ownership or access to the software?
• Did you license the software separately from your employment?
• Or did your software simply become part of your job and remain your own tool?

I’m especially interested in hearing from people who built something themselves first, and then had a company become interested in both them and the software.

How did you structure that relationship, and is there anything you wish you had done differently at the beginning?

Sorry if this is a slightly unusual question, I’m finding it difficult to explain the situation clearly.

​

My company is a consultancy based in Bangalore, and I'm deployed to a US-based global company as an Analytics Consultant. The US team refers to my role as in-house performance tools developer.

I do genuinely advanced work here. My responsibilities center on GTM (Google Tag Manager), covering all tags, triggers, variables, DOM, custom tags, GA4, and Klaviyo across a company valued at over a 6 billion dollars.

Historically, our sites relied on DOM scraping for tracking. I mapped all of that over to a native dataLayer instead. At the time, the setup was WordPress on the frontend with Shopify on the backend. With DOM scraping, any code or design change on the frontend carried a real risk of silently breaking tracking, since DOM-based tags depend on matching page elements that can shift at any time. Moving to a native dataLayer removed that risk entirely — tracking no longer depends on WordPress markup or design changes.

Now the company is rolling out new additional sites with Shopify as both frontend and backend. The obvious approach would've been to spin up a new GTM container for these Shopify-only sites — but that means rebuilding every tag, trigger, and variable from scratch, and reconfiguring every ad pixel again (we run 5+ pixels — Meta, TikTok, Reddit, GA4, and others), since each pixel needs its own event and payload setup. With 6–7 sites already live and me being the only person handling this end-to-end, running two containers in parallel would've meant permanently double-maintaining everything going forward.

So instead, I architected it so the new Shopify frontend + backend sites plug into the \*same\* existing container as the WordPress frontend + Shopify backend sites. The key was keeping the native dataLayer schema — event names, parameters, structure — identical across both setups. Since GTM reads from the dataLayer and not the underlying platform, once the schema matches, the frontend platform (WordPress or Shopify) becomes irrelevant to GTM. One container, one set of tags/triggers/variables, one set of pixel configurations — covering every site regardless of what platform is rendering the frontend.

On top of the architecture, I also own the actual event and payload design for business and marketing use: defining what events matter (purchases, add-to-cart, checkout steps, sign-ups, content engagement, etc.), building out full payloads for each event so marketing and analytics teams get consistent, complete data — item-level details, revenue values, user attributes — and setting up GA4 custom dimensions and metrics so this data is actually usable for reporting and decision-making, not just collected. The same event data feeds every ad pixel, so conversion tracking stays consistent across Meta, TikTok, Reddit, and GA4 as well.

I also wear a dev hat when needed — I create PRs in dev, and after QA validation and dev approval, they go safely into production. I write custom JavaScript for custom tags and variables as well.

Right now I'm migrating our entire setup from web GTM to server-side GTM, and I've also implemented domain aliasing between WordPress and Shopify (using something like shop.site.com) so Google treats them as the same site — allowing data to match under the same logic as server-side GTM.

Given all this, I'm genuinely unsure what to call my specialization. Would this fall under Senior Analytics Engineer, MarTech Engineer, Growth Engineer, or MarTech Architect?

I am very interested in this! What is the best videos or course I can start with? please and thanks

I am in Product Management in a tech company from Vancouver, Canada and a sessional faculty for Analtyics Part time in a local University. As part of giving back, I am looking to teach a small group of serious students/career switcher which will help them get jobs (free).

Can anyone share their technical interview experience for data Analyst 4 position. Typical questions and style of interview that can be asked. Role says specifically focused on A/B testing.

Currently work in manufacturing. It's a loosely defined role that I turned into analytics / continuous improvement. Considering quitting to do TEFL abroad temporarily.

FWIW I have a BS in Math and an MEd in Instructional Design (corporate training / talent development). Currently in my early 30s, only have 2 years experience in this. Had a difficult time getting a job out of college with my BS so my career path is a little unconventional and not straightforward.

I get paid a decent amount and would like to stay in analytics, but I've been dealing with some existential stuff in the last year.

I'm going through divorce, and while I'm over the emotional turmoil of the relationship itself ending, I've been feeling incredibly restless/misaligned and looking for a reset.

It really doesn't help that my company is insanely dysfunctional (I mean, *dysfunctional*) and hasn't provided much direction or clarity; it's wearing me down. And frankly, I'm concerned about my job security, with the way this place has been going.

For a while I'd been wanting to just quit a job and travel, and have always wanted to live abroad. I have a healthy amount of savings, so I can do it, especially now that I'm single, but...the analytics job market has become insanely competitive, and I don't feel like it's getting any better.

I just worry that if I pull the trigger and potentially come back, I'd get caught in a shitty position of being either underemployed or losing money very quickly.

Thoughts?

We’ve got plenty of support data. Transcripts, CSAT, AHT, resolution rates, transfers and QA scores. The issue is turning any of it into something useful. If AHT jumps we can see it right away. Figuring out why is another story. Someone usually ends up digging through calls trying to spot what changed. Same thing when CSAT drops. QA feels similar. We review a sample of calls but I keep wondering what we’re missing in the other 95%+ and we have been looking at tools like Cresta that analyze all conversations and connect certain topics or agent behaviors to things like CSAT and resolution. Sounds useful on paper. I’m just wary of ending up with yet another dashboard nobody checks after a month lol. What are the next steps after choosing a tool for the team because this is the first tool , like the rollout and the rest.

I'm curious to hear from current Pega professionals about what you’re actually seeing in your companies/projects.

Are you getting new development and major features, or is most of the work now maintenance and small enhancements? How healthy is the Pega project pipeline?

Also, are your leadership/clients talking about reducing Pega usage, moving away from Pega, or replacing it with other technologies? And are you personally planning to move away from Pega, or do you still see a strong long-term career in it?

Overall, does Pega feel stable, declining, or still growing in your experience? Is this just happening in certain companies, or are you seeing it across the industry?

Would really appreciate honest perspectives. Please share your general company/project situation and type of work without revealing any confidential information.

Good Afternoon,

I am a Student in Mumbai who is studying Biotech at a Good College - I am currently also learning Phython and Excel, I need some advice regarding my career.

Currently I am in my second year and I hate my Field a lot, yet everyone tells me I can only get a job through this field. However I realize I cannot work in Research Setting. I feel by continuing this course I will waste more of my Father's Hard owned Money. Already the Course is expensive - I was planning to work after Bachelors but I heard most high paying jobs come after Masters. I am tired and sick of the Course, and I am afraid I have no motivation left.

I am planning to quit College and work on Data Analytics and Engineering. Focus on building my resume and such - I feel I am making a stupid descision, So therefore I want to ask for advice. I will get a certificate in Biotech but not degree, I feel like I am Idiot for following through but I have no choice, Thank you if you give advice

Got a monthly inventory report from one of our clients a while ago. Nothing looked wrong at first - normal format, numbers seemed reasonable.

While going through the different product lines, I noticed something weird. One of our smaller lines had almost the same inventory level as one of our best-selling ones.

That immediately felt wrong because I knew the difference in sales between them was pretty big

So I checked the client's purchase history and compared it with previous inventory reports. The numbers didn't add up. Turned out to be a counting error on their side, which they confirmed and fixed.

What I found interesting is that the number itself didn't really look "wrong." If I didn't know the business, I probably would've just looked at it and moved on.

The only reason I caught it was because I had a rough idea of how those two product lines compared.

One thing that helped me build this kind of context was keeping a personal onboarding doc with random things I learned about the business. Over time, some of them just became reference points in my head.

Curious how other analysts approach this. Do you have a method for building this kind of business intuition, or does it mostly come with time?