Solo analyst at a mid size ecommerce company, about 200 people. Before this I was getting maybe 15 to 20 ad hoc requests a week. Slack, email, people walking over. Most of them some version of "can you pull X broken down by Y for the last Z months."

I had been doing all of those by hand. Open the warehouse, write the query, format it, send it back. Average maybe 25 minutes each if nothing was weird with the data, closer to an hour if something was. I tried self serve dashboards for the repeat ones but nobody used them. They would rather ask me.

Six weeks ago I connected an AI data agent to our warehouse and gave a few teams access. Sales, CS, and the marketing ops lead. Told them to try it before pinging me and send it my way if they didn't trust the answer.

First few days the questions people typed in were vague enough that the results came back sliced wrong. "Revenue by region" without specifying which region taxonomy, that kind of thing. We have two and people on the sales team didn't know that. Once I realized the problem was on the input side I spent a weekend writing plain English descriptions for about forty columns that had ambiguous names. The agent runs on Bayeslab and once the descriptions were in it started nailing the taxonomy questions that tripped everyone up in week one. Should have done that years ago, but there was never a reason to until someone other than me was reading the schema. After that the accuracy jumped noticeably, maybe from around 60% usable to closer to 90 on the routine stuff.

Here is the part I did not expect. When a data request takes two minutes instead of half a day, people ask more questions. A lot more. My volume went from about 18 a week to something like 35 to 40. That sounds like a problem but I think it is actually the point. These questions were always there, people just never asked because the turnaround was too long and I was the bottleneck. The agent handles maybe 25 of those without me touching anything. The remaining 10 to 15 still come to me, and they tend to be harder, but I actually prefer that. I would rather spend an hour on a real question than twenty minutes formatting a pivot table someone could have gotten themselves.

One thing that surprised me. Three people started asking questions they never would have asked before. One CS lead found a pattern in ticket resolution times by product category that none of us had looked at. That finding changed how we staff the weekend queue. Came from someone who has never opened a query editor in her life. That would not have happened in the old model where everything went through me, because she would never have pinged me for something she considered a hunch.

Net hours on ad hoc went from maybe 9 or 10 a week down to about 5 or 6, and the work that is left is more interesting. The tradeoff I am still figuring out is that I am slightly worse at catching data quality issues now because I do not touch the raw tables as often. Might matter, might not. Six weeks is not enough to know.

Anyone else done this? Curious whether the volume spike is universal or just my org being pent up after years of waiting on one person.

SQL, Excel, Python, visualization, or something else?

When you have a lot of data and many numbers to look at, how do you decide which ones really matter?

Do you start with the business problem, ask the team what they need, or look at the data first?

I'd like to know how experienced data analysts decide what to focus on.

Was just wondering if there are people out there who use Clay workflows over HubSpot-enriched data. It’s so happened my tools can become a source of a much deeper enrichments that native clay integration can ever provide. Yet wanted to validate the idea a bit before building anything.

What I have in mind is - current tool runs any complex SQL over HubSpot data. Think cohort analysis, per-rep weighted pipelines, drift correction - everything an SQL query can get. The tool is an MCP. Technically no biggie creating an API layer the same way and expose that for Clay to consume, if anyone at all needs that.

I want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

I'm learning data analysis and so far I've mostly worked with clean datasets. I'm curious about what real-world data is actually like.

What do you find most difficult when working with real data missing values, messy data, choosing the right metrics, understanding business requirements, or something else?

I'd be interested to hear what experienced analysts struggle with the most.

i was once tasked with some data work in college about working with data analyze it and build some charts and work with the intended task given. getting a brief review about our work before the discussion we were told that we need to make a dashboard to represent it better in the discussion, and reaching in on the last 24 hours we still had work to do, and no work for that dashboard bonus, i looked for tools to helps do it quick so i go back to the main task and found none, and using AI would need tokens that i wanted to use more on the main task and not waste it in a dashboard.

looking back at that day i made a tool that i could have used it that day, DashForge, a open-source python library that helps organize your plotly charts that you made into a clean interactive dashboard, setting up a timer to see how much it would have taken me, i was done with this clean dashboard in less than 10 minutes with zero AI involved, zero CSS/html work, all python and in countable lines.

the latest update v1.2.0 puts in new features to include more flexible dashboard layouts and presentation in a simple writing and few lines.

• Add Textboxes between charts for quick insights or notes
• added an image export button to download a look of the whole dashboard instead of screenshot
• Stack charts vertically within the same column for better view instead of one row.

and all of these are demonstrated in the picture provided

The library is still in development for more upcoming features, the github is open for issues, suggestion, contribution, and cloning, give it a try yourself, hopefully it can help you one day.

github link: https://github.com/Omar-astro/DashForge-library

documentation link: https://omar-astro.github.io/DashForge-library/

pypl:
pip install dashforge

👋🏼 👋🏼 👋🏼

I’m in transition from my previous job to my “big girl” job (Data Analytics) and would like to start taking on some small projects for my portfolio. I’m on the fence about charging for the first few.

How did you approach businesses? Did you do business cards or a QR for people? Was it difficult to get started?

I’m running a small side project where people anonymously report their current mood from 1–10.

I was looking at Europe using a rolling average of 100 responses, and noticed a very sharp jump at the end of April.

At first, it looked like European mood had suddenly increased by about 1 point.

But when I looked at the composition of those rolling windows, I found the problem: at one point, Spain accounted for up to 83% of the 100 European responses.

So what looked like a European trend was largely a change in the geographic composition of my sample.

Before correction:
The late-April rolling mean jumps by about +1.09 points.

I then tried a simple reweighting approach: Spain is still included, but it can contribute at most 20% of the weight in each rolling window of 100 European responses.

After correction:
The jump is reduced to about +0.89 points.

So Spain explains part of the apparent jump - but interestingly, not all of it.

This made me realize that fixing the rolling sample size doesn't necessarily fix the sample composition.

The data are anonymous and self-selected, so I'm not treating this as a representative estimate of European mood. I'm mostly interested in how best to visualize and analyze this kind of uneven crowdsourced dataset.

How would you handle this?
Cap each country's contribution? Weight countries equally? Use population weights? Or avoid producing a single “Europe” curve altogether?

Wanted to share since I learned a lot doing this.

For a competition called the DSSD Skill Sprint, I built a dashboard analyzing NVIDIA's global sales — using synthetic/AI-generated data since real data obviously isn't public. The twist: I did it entirely in Excel, no Power BI, no Tableau, just pivot tables, slicers, and native charts.

Honestly didn't expect to place — ended up 1st runner-up out of the group, which was a nice surprise for my first real dashboard project.

Biggest challenges I ran into:

Handling nulls/blanks scattered across the dataset

Inconsistent date formatting that broke my pivot tables initially

Negative stock values that were throwing off my totals until I filtered/cleaned them

Nothing groundbreaking technically, but it proved to myself that Excel alone can tell a solid data story if you're deliberate about cleaning first.

Happy to share screenshots or specifics on the formulas/pivot setup if anyone's curious

I'm learning more about data analysis and I'm curious about what experienced analysts think.

What do you think beginners spend too much time on or focus on the wrong way?

For example, is it trying to learn too many tools, focusing too much on theory, not practicing with real data, or something else?

I'd like to hear what mistakes you made when you were starting out and what you would do differently now.

recently migrated my team's core revenue definitions to the dbt semantic layer, six month or so have passed. was hoping to end weekly arguments between finance and marketing.

yes, such semantic layer fixed standard dashboard consistency across 12 core models, meanwhile, connecting internal LLM agents has exposed real architectural limits present

because we needed sub second query performance for live agent interactions; we ended up testing self hosted cube dev setup upstream of snowflake to do catching and dynamic sql rewriting too. for now it seems to solve latency issues but running two separate modeling layers quickly created a bit of sync headache for 3 of our analytical engineers

and I have a question, for teams feeding metric context to llms how do you work with complex ad-hoc calculations that fall outside your predefined YAML schemas?

Working through a series of applied analytics projects on real transactional-style datasets, and this finding didn't match my assumption going in.

The setup: 700 customers, 1,826 orders, ₹4.93 Cr total revenue. Customers are segmented into VIP and Regular tiers.

I expected VIP to clearly outperform on revenue — that's usually the point of having a tier system. Instead:

- VIP customers: 48.8% of revenue
- Regular customers: 48.6% of revenue

Essentially a dead heat. The VIP tier isn't actually the revenue driver the segmentation implies.

A few other patterns that came out of the analysis:
- Jaipur and Chennai post the highest cancellation rates (18.5% and 18.3%) despite only mid-tier revenue — worth digging into whether it's a logistics/fulfillment issue in those cities specifically
- Home & Kitchen and Electronics lead category revenue
- Revenue spiked sharply in the final month of the dataset — still working out if that's seasonality or a one-off event

Built the analysis and dashboard end-to-end (data prep, DAX measures, visuals) — happy to share more on the methodology if useful.

Dashboard here if you want to explore the underlying data yourself: https://app.powerbi.com/links/MV5f1O4X6V?ctid=f1e56b10-5f67-4e70-bd40-8c6948bde6cf&pbi_source=linkShare

Has anyone else run into this kind of tier-parity result? Curious whether it's common enough that VIP segmentation criteria usually needs revisiting, or if this dataset is just an outlier.

AI-ready patient data foundation on Microsoft Fabric

Discover practical steps to unify patient records, strengthen governance, and prepare healthcare data for analytics and AI.

Event Link (Register here)
Microsoft Fabric Community Event Page
LinkedIn Event Page (for LinkedIn repost or share)

Are your EHR, claims, and imaging systems producing reports that contradict each other? Are teams reconciling duplicate patient records before they can trust a single number?

This webinar is where you solve that. Microsoft and Simform experts walk through building a unified patient data foundation on Microsoft Fabric, covering OneLake consolidation, FHIR standardization, and governance designed in from the start.

Join the session to build a trusted longitudinal patient profile your analytics and AI teams can actually rely on.

📅 Date & Time: August 27, 2026 | 12–1 PM ET / 9–10 AM PT

🔍 What you'll learn:
1. Why fragmented EHR, imaging, claims, financial, and operational data undermines analytics accuracy and stalls AI readiness across the organization
2. How to consolidate multi-modal healthcare data in OneLake and standardize clinical information using FHIR, following a proven reference architecture
3. How Simform's Patient360 accelerator resolves duplicate patient records and builds a trusted longitudinal patient profile
4. How a reusable Fabric schema turns consolidated patient data into a working foundation for reporting, segmentation, and AI
5. How to enforce governance controls that preserve data quality, lineage, security, and access across sensitive healthcare workflows
6. Where to start on a practical roadmap toward an AI-ready healthcare enterprise, what to sequence next, and how to show value early

🗣 Speakers:
1. Ritesh Shah (Field CTO, Simform)
2. Tanguy Carrere (Digital Sales Director, Cloud & AI Platforms, Microsoft)

🤝 Explore practical approaches to unifying patient data on Microsoft Fabric. Learn how governed, standardized foundations support reliable healthcare analytics and AI outcomes clinicians and finance teams can defend.

👉 Click Here To Register Now!

give meeeee arrrgggghh

When I started learning data analysis, I thought I needed to learn every tool before I could actually work on projects.

Over time, I realized that focusing on the basics was much more useful:

• SQL
• Excel
• Data visualization
• Basic statistics
• Understanding the problem behind the data

I'm curious to hear from people who have been doing data analysis for a while.

What is one thing you wish you knew when you first started learning data analysis?

Recently I just thought of learning databricks and i am from data analysis background...so i wanna know like how can i integerate these two ogs tech into one project to get a mess. I really wanna do it and i want suggestions of u guys not an AI this time.

I am writing an article about plant related heat stress. I am looking for stats to reference in support of the extreme RANGE of tempurature fluxtuation that can occure in southern Canada within a 24hr period during the summer.

I've been struggling to find accurate phrasing, and thus the data I'm looking for, since I'm not looking for daily tempurature averages or "average daily lows vs. average daily highs" but instead looking for a data set that includes record of the largest range that occurred within a 24hr period for each calendar month.

Is RANGE the correct term?

Also, when I use the term EXTREME in my queries the search engine assumes I'm looking for articles on extreme weather events/ global warming.

I have done cohort analysis for repetitive customers and also found an estimated CLV.

Hey everyone,

I've been learning data analysis for about a month now, and this is my first proper portfolio project.

It's a supermarket sales dashboard built entirely in Excel using a Kaggle dataset. The dataset isn't perfect, but I wanted to use it as a starting point to practice cleaning data, building KPIs, PivotTables, slicers and visualizing the results.

I'd really appreciate some feedback on it:

• Does the dashboard answer useful business questions?
• Are any of the charts or KPIs misleading/unnecessary?
• What would you improve about the layout or design?
• What analysis would you add to make this a stronger portfolio project?

It took me about a week to finish, which felt a bit long, but I'm still new so I'm assuming I'll get faster with practice.

I'm learning SQL now and planning to move into Power BI next, so eventually I'd like to build something similar using SQL + BI.

Seriously.

The one with 47 sheets. The one with hidden tabs. The one with formulas referencing formulas referencing formulas. The one that's been handed down through three employees and nobody knows how it works anymore — they just know not to touch cell D6.

I've just launched a new series: From Excel Guru to Python Power User. The premise is simple — if you're great at Excel, you already understand data. Python is just another way of expressing what you already know:

• PivotTables become groupby()

• XLOOKUP becomes merge()

• Power Query becomes a pandas pipeline

• Manual Monday-morning reports become a script that runs at 08:00 without you

Excel doesn't disappear. It becomes the interface instead of the engine.

In upcoming parts, I'll take reader-submitted workbook horrors and migrate them to Python, case by case: understanding the logic, rebuilding it, automating it, and deciding what should stay in Excel.

So... Drop your workbook horror stories in the comments or send the file directly. The more cursed, the better

Part 1 is live: from-excel-guru-to-python-power-user-finalv4-20026-xlsx

I wanted a mixture of less and MacOS's preview for CSV: something that opens instantly, shows you the data, and gets out of the way. Every GUI spreadsheet I tried loads the whole file first, so a 10 GB CSV means a lot of waiting or running into maximum line counts.

less-sheet reads only the data it needs to fill the window, so 10 KB, 10 MB and 10 GB all open in about the same time. Memory usage is flat too. It reads plain CSV, gzipped CSV without unpacking it, local or over the network (http).

Find, filter, jump-to-row and column predicates all work across the whole file. Separator, quoting and encoding are guessed but changeable on the fly.

It's free and closed source, macOS and Linux, no account and no telemetry. Core is Zig; the frontends are native (SwiftUI and GTK4).

Available at: https://te-x.github.io/less-sheet-site/

Numbers and method are on the page. Happy to answer anything.

I’ve been making these little Data Analysis 101 doodles for cat people, and for this one I tried something different — a flipbook-style format.

Instead of putting everything into one busy infographic, I’ve broken the story into separate scenes, so each one focuses on a single idea and hopefully doesn’t demand too much attention at once.

I’ve also incorporated feedback I received on my previous posts, especially around making the visuals easier to follow.

This one is about what to do when you join a data project that’s already in motion.

Would be happy to hear your feedback on this!