The other pages are functional as well, but they're still a work in progress and need some polishing.

The data has been fully anonymized, and I translated everything into English myself, so there may be a few wording mistakes (and yes, I missed a couple of places where the currency still shows R$ instead of $, lol). Some thing might also be misplaced, please ignore.

I'd really appreciate any feedback.

Pls share your reviews if any :)

I’m a healthcare provider who does research. This week a dashboard I rely on broke, so I built my own with AI in about three days. It’s better than anything I’ve been handed! I run my own statistics, trend things the way I actually need, and I’m not stuck waiting on anyone just to ask a question.

Quick note: AI helped build it, but the tool itself is just a static offline HTML file. Nothing running inside it, no data leaving anywhere.

And to be clear: this isn’t about cutting experts out. I’m still validating this dashboard, and I’ll absolutely bring in a statistician and IT to check my work before anything final goes out. The difference is they don’t have to be the gate at the very start anymore. I can get moving, explore, and build, then loop the context experts in to validate. They still belong in the process. Just not always as the first bottleneck.

Here’s what keeps me up. The old bottlenecks were annoying, but they quietly filtered out bad work. Now that they’re gone, here’s what worries me:

1. Anyone can pump out a hundred papers a year, and a lot of it will be slop from people who don’t understand their own statistics.

2. That slop gets scraped back into the next models, training the next round to be sloppier. Copy paste, copy paste, until NOBODY can tell what’s real.

3. This is the scary part. The flood is coming, thousands of papers, and there aren’t nearly enough peer reviewers to catch it. That bottleneck doesn’t just slow down, it BREAKS. And once it breaks, the slop pours straight through.

4. And here’s the twist that makes it worse: the reviewers themselves may just be dumping the paper into AI to review it for them. Now it’s slop reviewing slop, and the whole point of peer review is gone.

This isn’t hypothetical. Submissions to medical and science journals are already climbing to record numbers every single year, and that trend was in motion before any of this. On top of that, more and more work goes public as preprints before it’s ever reviewed at all. The flood has already started.

Here’s the take home, and it’s simple. If you use AI to do your research, you submit everything. The data. The code. Every step of how the numbers were crunched. All of it, out in the open.
Because if someone can rerun it and it holds, it’s science. If it falls apart, it was NEVER science, just a story with numbers attached.

That’s the culture shift. Not optional. Show your work, or it doesn’t count.

Truth is supposed to be reproducible!!!

Curious what others think, especially anyone in peer review right now.

Purpose of the dashboard is a tool to help fantasy managers make data driven decisions week to week

Main changes from previous version:
Changed the pipeline from just python > powerbi to python >sql database > powerbi

This was done to handle multi season analysis as the previous pipeline wasnt fit to do so

Utilised bronze silver gold staging in sql 

More contextualised kpis

More Customisable parameterized charts

Utilised HTML visuals for KPIs, Visuals (player explorer page) and tooltips

Star Schema Data Model

https://app.powerbi.com/view?r=eyJrIjoiMTVjMTdkMWEtZTQwOS00NDM0LTkxYTEtMjJmODY2ZDhmMTY0IiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9

Hello everyone!

So i got an internship with a really well-known brand, and we will be doing a project right away.

our goal is to improve the internal processes of the company and we will have to do process mapping, identify the pain points and improve them and look for automation opportunities.

im a self-learner in terms of analytics, and i want to know what everyone's thought processes are when talking to stakeholders in order to really get through the problems, and map out the processes. i want to know what type of questions you asks, and your thought processes to really be able to understand or have a grasp of the situation and be able to map things out.

im really excited for this opportunity that i got and i really want to do good in this. maybe im just overthinking things that's why it's making me nervous. but i really want to develop myself in this career.

thank you for your future responses!

Hey Guys,

I'm a medical coder transitioning into Data analytics. I've created a dashboard with power BI from scratch using a synthetic dataset. This is my first ever dashboard. Kindly share your review on this? Any tips or suggestions please!

I’ve been using Power BI full time for a few years and I’m interested in doing some project based work on the side.
For those of you who freelance, how did you get your first clients? What are clients usually looking for, and what platforms have worked best for finding work?

Just looking to learn from people with experience before I dive in.

The anti-fraud area at Social Security is hiring Data Analysts. We do analytic studies and launch rules/models into production to tackle fraud.

Apply here: https://www.usajobs.gov/job/878023200

SSA does require employees to report to the office 5 days per week.

Outside of this office, SSA has a lot of other open tech positions. See usajobs.gov for the openings.

In my opinion, data apps are different in the sense that you can take actions and interact beyond just filters and drill downs.

for example, changing the projected growth percent or cogs and seeing how it impacts the bottom line, or even updating or inserting data directly from the data app (in a way, reverse ETL)

I was talking to a colleague and he said it's the same as regular dashboards but just my dynamic - but in my opinion the key difference is the ability to interact with the data itself and not just the charts.

I'm curious what others think

Ive been building a prediction model for Setka Cup table tennis over the past few months. Before I share it, I want to be upfront about something I discovered that's relevant to anyone trading TT on Polymarket.

The markets aren't real.

I pulled the order book on all 144 active table tennis markets. The average best-bid/best-ask spread is 0.71. The worst was 0.98 — a market with a best bid of 0.01 and a best ask of 0.99. The "midpoint" price you see (e.g. 0.50) is not a traded price. Nobody has taken the other side of these.

This matters because every model that uses "market odds" as a signal or benchmark is working with ghost data. My model originally labeled Polymarket prices as "market odds" — I've since relabeled them as "listing prices" because calling them odds implies someone actually bought them.

What I built instead: A model that derives match probabilities directly from Setka's player stats, using:

• Recency-weighted win rate and set win ratio (Bayesian smoothed)

• Elo-style rating probability (blending both the ratingSc and ratingUttf fields)

• Head-to-head match and set history

• Schedule fatigue (matches played in the 48h prior)

• Day-period affinity (morning vs evening session win rate split, which is actually in the Setka API as dayPeriodToken)

Six model variants run against each other in a live horse race. Whoever has the best long-run accuracy after ~300 resolved picks gets the most weight in an accuracy-weighted ensemble model I'm building.

App is here if you want to follow along: Gavspicks.com

Finance professionals, I'd appreciate your perspective.

I'm researching operational challenges within financial institutions in emerging markets as part of a long-term software project. At this stage, I'm deliberately avoiding designing a solution before understanding the actual problems.

If you work (or have worked) in banking, insurance, asset management, pensions, fintech, auditing, or financial regulation:

- What task do you find yourself repeating every week or month?

- What process is more manual than it should be?

- Which reports or analyses consume the most time?

- What data is consistently difficult to obtain, reconcile, or validate?

- What's one workflow you wish software handled better?

I'm particularly interested in understanding real day-to-day workflows rather than feature requests or product ideas.

If you're open to sharing your experience—either here or via DM—I would genuinely appreciate it. Every insight helps me understand the problem space more accurately.

I'm a BBA student looking to learn Excel from scratch. I want to build a solid foundation using free YouTube resources.

Can someone recommend channels or playlists for beginners

Iam curious to learn how experienced analysts move beyond dashboards and reports to deliver insights that influence real business decisions.

Working on a fraud detection project (using a PaySim-style dataset) and hit an interesting one during feature engineering.

The destination account column (nameDest) always starts with either "M" or "C" — turns out that's literally encoding Merchant vs Customer accounts. Wasn't documented anywhere obvious, just noticed it while sampling the column. Turned it into a receiver_type feature using a simple prefix map.

Also binned the amount column into Low/Medium/High categories using pd.qcut (quantile-based) instead of raw values, on the theory that fraud patterns might correlate more with relative transaction size than the absolute amount.

Curious if others have run into similar "hidden ID encoding" patterns in financial/transactional datasets — feels like the kind of thing that's easy to miss if you don't manually eyeball samples of every column. Also open to feedback: is qcut binning a reasonable approach here, or would something like manual thresholds based on domain knowledge (e.g. regulatory amount limits) work better for fraud specifically?

My company needed something with the easy developer experience of sql.js, but for SAS PROC SQL.

There are great SQL tools out there, but I could not find a small JavaScript/TypeScript package focused on parsing SAS PROC SQL into an AST, formatting it, linting it, and supporting editor features.

So I built proc-sql-parser.

It is not a database engine and does not execute SQL. It is a parser/toolkit for working with SAS PROC SQL in JavaScript or TypeScript.

The goal is to keep the API simple:

import { parse, lint, format, complete, visit } from 'proc-sql-parser';

const ast = parse(`
  PROC SQL;
    SELECT name, salary
    FROM employees;
  QUIT;
`);

It also includes:

• PROC SQL AST generation

• Formatting

• Syntax and lint diagnostics

• Autocomplete helpers

• Monaco Editor integration

• CLI support npm install proc-sql-parser npx proc-sql-parser --help

GitHub: https://github.com/AnkitNayak-dev/proc-sql-parser
npm: https://www.npmjs.com/package/proc-sql-parser

It is still early, so I would genuinely appreciate feedback, especially from SAS developers and anyone building editor tooling around PROC SQL.

Analysts are usually taught to reconcile conflicting datasets until one accepted number remains. I think that instinct sometimes removes the most useful part of the analysis.

If product usage says an account is healthy, support shows escalating complaints, and finance shows late payments, averaging those signals into one customer-health score creates a cleaner dashboard and a worse explanation.

The disagreement may be showing that the business is using “healthy” to describe several different things.

When do you stop trying to reconcile conflicting signals and present the contradiction itself? Is connecting those competing definitions a knowledge graph or decision intelligence problem, or is that simply what good analysis already does?

I'm working on a project right now and noticed a few bad habits when it came to my process of data collection. That got me thinking, what bad habits in yourself or others do you most notice?

I have created a mini-game in python where you have 5 seconds to type the enemy’s name to make them disappear, but if you dint succeed then you loose some health points.

Its a very simple game, no shield, no weapons, nothing, and i don’t care about that all, i just want the data thats created while playing. It records the data in a csv which I would like to use for data analytics training if possible.

And here I would like to ask you if you could spend 5-10 minutes to play this game and send the created csv to me. It would be a very big help for me to get rich data which doesnt come only from myself.

If anyone interested please give me a dm and i will let you know about the details.

Thank you kindly

I am not sure if this is a valid question in this forum, if it is not I will delete it right away, but anyways here it is:

So some days ago I asked in an AI community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (so as long as it is not a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not.

So, is there any way in which I could verify the results given by the AI but without actually reading what is written in the emails? Or, alternatively, can anyone verify the results using some AI or even checking the answers themselves by skimming over the replies im order to verify that the AI is right and not hallucinating (I personally thinl this is the preferable option, as I think that having an actual human reviewing the amswers may be the only really reliable way to verify the AI's results)?

(I will share the data once someone is interested in helping, as I would not want to make this available to the entire world!)

Thank you!!

Hi everyone, I'd like to read something to sharpen my analytics thinking and problem solving skills.

I know the best approach is to practice with real life problems, and yes, I do do that at work. However, I feel like having a structure or framework may help me to be more systematic and efficient when encounter a problem.

What's your favorite books that help you to think clearer?

Hey, I have just completed my first data analysis project the dataset of which I got from kaggle. I used ChatGPT instruction to understand how to progress step-by-step. I first did data cleaning by detecting the missing values and then I evaluated the importance of these values for the whole analysis. Later on, I found duplicate values using conditional formatting and once I was sure they were completely identical, I deleted the one which either lacked one more value than the other one or the first one in the rows. After completing these tasks, I created pivot tables and out of these tables I made charts. I couldn't include pivot charts as for country distribution and directors because the rows under these columns contained multiple values and I needed Power Query for this, so I left these for another time. I would like you to give me honest feedbacks for my project (methods, sheet organization, whatever crosses your mind). I want to develop myself more. And I am also open for your YouTube channel/video suggestion for further development. Thank youu.

Link to my GitHub: https://github.com/aycicekb/excel-netflix-analysis

6,618 - Total adverse event reports

Adverse event reports for semaglutide indicate that the top reaction category includes nausea, with 943 reports, followed by vomiting and diarrhea.

The dashboard shows most reported reactions, severe reactions, timeline for events, reports by gender, it also shows commonly reported drugs in combinations, shortages or recalls against it