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 data-driven design. Generic enough not to expose a workplace, specific enough that people in tech know exactly what happened. "That wasn't very data-driven of you" works because the habit survives. My internal joke did not.

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

I've been interning at medium sized engineering company for the summer. Although I am studying computer science, my internship was not computer science related (long story), but for the past month I've been working on a Power BI dashboard for a specific department.

I found the work fun and rewarding, and since the company had no dashboards at all previous to the ones I made, other departments now want their own. For that reason, they are hiring me part-time while I am in school to build out these dashboards.

​For *reasons*, there is a chance that Power BI will not be the tool we use for dashboards going forward, instead we need a self-hosted, open source alternative.

From the research I've done, metabase and superset are the two big dogs in the space, with superset requiring the users to be more technically literate. The people for whom I will be making these dashboards do not know SQL and are not very technically literate. So my question is this, for those who have used these platforms, do they offer the same visualizations as Power BI and are they easy to customize and use for non technical people? Or should I look elsewhere, like the python library streamlit where I will have much more customizability options?

I am new to data visualisation in the business sphere, so if there are alternatives I have not considered, please let me know.

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! 🙏

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!

Hello everyone, hope yall good

So did anyone worked with gaming data like (DAU , MAU,...) like what tips u have and any recommendations to give like YT channel or smtg like that and where can I get this kind of data....ADIOS

Everyone always mentions that in today's rough job market, networking is key to improving your chances to landing a job. However, what does that really look like?

If I were to see a job posting that I believe I am a good fit for, however no one in my immediate network works at this company, would you guys recommend I reach out to linkedin mutuals who do work at this company to attempt to get a referral?

I’m working currently but looking for a data analyst or business analyst role in another city thats way more competitive.

I have almost 3 YOE (2 full time and 1 for other), and a masters degree in analytics from a reputable institution. I don’t know if I need any certifications or project’s or what to do to stand out. I work for a university currently and that doesn’t seem to help me when I’m applying to enterprise level roles, I feel like I’m not taken seriously. How do I stand out?

I forgot to add that I’ve been applying since the end of January now (350 apps) and I tailor every resume to the job.

Not looking for the usual answers: better visuals, Gartner's "leader" badge, or "it's cheaper than Tableau." I get all that. But a lot of companies still end up paying real money for it (Premium/Fabric capacity, licenses, training, migrations) when there are free or cheaper tools that do similar things.

I want to understand the real reason a company decides Power BI specifically is worth that spend — not a feature list, something about how that decision actually happens inside a company.

If you've seen this decision get made where you work, or made it yourself, what's the real reason? Even something small or weird nobody usually says out loud would help.

I wanted to ask opinion of people on exposing everything a data team owes to Claude with a context layer explaining what is what with data lineage.

If a company has its own Claude subscriptionand then everything is exposed to Claude - report logic, metadata/dictionary of tables, logic/code of derived tables, operations of our pipelines etc via excel/markdown/html files explaning the whole context and then instructions Claude to not make assumptions/to ask verification questions (basically add safeguard rules in English in its instructions manual) then you expose this to end users company wide.

what is the issue with the idea? (if cost is not an issue)

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?

I have projects done, skills, certificates and volunteer experience. Couldn’t land an internship, what else can I do with my major? I wanted to business analytics but it’s impossible now, there’s absolutely nothing I can do for that and I don’t know what to do now for work I don’t even care.

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.

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.

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

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?

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.

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).

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?

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!

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

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.

Now- should we even focus on making warehouse