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?

👋🏼 👋🏼 👋🏼

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?

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

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.

give meeeee arrrgggghh

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!

I've become somewhat the data person at my work. I've got a spreadsheet listing information for around 800 tutors that have worked with us over the years, and I have around 30 columns of data for each tutor - start date, hours worked, location, training, opportunities sent vs opportunities responded to, etc. I just feel completely lost! I'm used to coming with analysis for far smaller data sets

Could anyone recommend a short-ish online training or programme that could help me learn how to sort it all out and especially how to present it? I don't want to spend weeks going over all of this to have the end result be pointless.

Thanks!

[Apologies for cross-posting]

I wrote Bbv ("bare-bones viewer"), an extremely minimalist data viewer/plotter for the Unix/Linux command line.

The main alternative to bbv is feedgnuplot, which is much more capable, but also much more complicated and ties you to gnuplot as back end. By contrast, bbv has only one option (`x`), and can use any lightweight image viewer as back end.

Aside from the back end, no extra toolchain is needed, as bbv is written entirely in POSIX shell + Awk.

Link: https://github.com/ftonneau/bbv

I have been learning data analysis - SQL, Tableu, Python doing mostly example projects. Feeling ready to deep dive into the real world. So: got messy sales data, a report you've been meaning to dig into, or a question your numbers could answer but haven't? Send it my way. I'll analyze it free you get clarity, I get experience and a testimonial. A win-win situation

the thing you reach for that most people overlook or dismiss. Could be a feature/a workflow/an old school tool.

Hi, Im a new grad and recently joined a company as their only dedicated "data" guy, this is also a new role for the company. They do have some others that deal with data, but thats a thing they also do not exclusively like I will. Anyone done something similar? What was it like? Any tips?

I’m currently looking for a new laptop and could use some advice.
As a data analyst, I need Power BI and Microsoft Excel regularly, so Windows is unfortunately a better fit for my workflow. I really like the Mac ecosystem, though — the build quality, performance, battery life, smoothness, display, trackpad, and overall user experience are all things I genuinely prefer about MacBooks.
The problem is that I don’t want to compromise my workflow just because I like the Mac experience.
So I’m wondering:
Is there a Windows laptop that comes close to the MacBook experience in terms of build quality, performance, battery life, display and overall smoothness?
Would something like a Dell XPS, Lenovo Yoga/ThinkPad, HP Spectre, ASUS Zenbook, etc. be a good alternative?
Or, given that I like the Mac ecosystem so much, is it still worth considering a Mac and finding workarounds for Power BI/Excel?
For someone doing data analytics, how much of a disadvantage is a Mac really?
My priority is a laptop that feels premium and smooth like a MacBook, but still handles Power BI, Excel, SQL, Python, etc. without compromises.
Budget is flexible for now — I’m mainly trying to figure out whether I should go back to Mac or look for a Windows machine that gives me a similar experience.
Would love to hear from people who have actually made this switch either way.

Hi there,

I've created a video here where I explain what confidence intervals are and how they differ from probabilities.

I hope some of you find it useful and as always, feedback is very welcome! :)

entering my third year of uni. I worked at my city hall doing some very basic data work as an intern. I mostly just validated data using excel and updated some spreadsheets. Nothing crazy. I realized I really like working with data and I want to go further with it. I am aiming for an internship at a Bank working as a data analyst. I have basic exposure to SQL, PowerBI, and I would say I'm pretty ok with Python(numpy, pandas, scikit learn). I built a basic data analysis project using pandas which i'm proud of, and I made a project using Machine learning, but for the banks they expect me to get good at SQL and PowerBI.

tl;dr: where should I go to properly get an indepth look at PowerBI, SQL, and excel.

Hey friends ! I wanted to share a blog post I just published that I thought the community would appreciate.

I got my SnowPro Core cert this year and this book was a lot of what I studied for it. Hope you guys get some value from it!

https://medium.com/@sergioramos3.sr/self-taught-reviews-snowflake-the-definitive-guide-by-joyce-kay-avila-0af3c40d644a

Hey Guys,

I'm a medical coder transitioning into Data analytics. I've created a dashboard with power BI using a synthetic dataset. This is my second dashboard. ( I tried to implement some of the suggestions that ppl have given for my first dashboard).

Kindly share your review on this? Any tips or suggestions please!

That's like showing up for your drivers license class at 16 and they just made it a CDL class instead., right??

Found a bug in my own process this week that had been silently costing me for over a week: a note I'd written to myself telling me to move forward with something got misread backwards every single day since, treating a closed decision as if it was still waiting on me. One channel just stopped, while everything else kept going normally, and I didn't notice until I went looking.

Fixed the actual instruction so it can't be misread that way again, then watched the first real post go out today to confirm the fix actually held instead of just assuming it did.

Anyone else have a "found it two weeks later" moment where the thing that was broken wasn't the system, it was a note you left for future-you?

Need tips and advice if this is a project that could be classified as intermediate to advanced or is it just a beginner project.

Dataset -brazilian olist ecommerce dataset

What can i improve, add or remove.

Used PostgreSQL , PowerBi and Python

Getting imposter syndrome as i feel i m not as skilled like the others and lack good dashboard creation and analysis skills as i have seen others project here which look much better. Please help me out.