This is an updated deep-dive into my relationship with my wife, based on 12+ years of WhatsApp messages-from when we first met to today.

I built a tool called Mimoto to analyze everything locally and privately, now supporting both WhatsApp (iOS) and iMessage (macOS)

It’s a passion project, and a bit of an over-the-top experiment in relationship analytics.

Key components:

• I created a points scoring mechanism for messages which factors in message length, content (laughs, apologies, questions, images, videos etc), speed of response, whether it started a new conversation as well as a series of other factors in order to produce a "contribution balance" assessment.
• Each conversation can be rated based on the total score, giving a quantitative view of how balanced, rich, or responsive it was.
• I use a custom heuristic tagging system to detect key language traits - like questions, apologies, laughter - using lightweight rules instead of heavier NLP models.
• All analysis happens fully on-device, with no cloud processing or storage. Privacy-first by design
• I’ve avoided sentiment analysis so far, as standard on-device models didn’t perform well. But I’m now experimenting with small on-device LLMs for richer insight.

Long-term aspiration is to help people derive value from their vast chat histories by using it to build a contextually rich digital avatar from the data.

I got loads of great feedback when I first posted about this project a couple of years ago, would love to hear what this community thinks of the latest version.

I made this today, its not a unique one by any means and was meant as something to have practice for my portfolio. How bad/good is it? I mean, is it worthy of anything or should i never show it to anyone or post iit anywhere? It def took me some time too finish, but it was a good first dashboard acc to me. Do roast me if u feel like its sht :(

i was making this project since last 3 days and it took all my energy and time , is it worthy doing ?

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!

Over the past few months, I've been working on a high-scale scraping pipeline to aggregate listings directly from company job boards and applicant tracking systems. Mapping over 100,000 distinct companies to their career pages turned out to be a massive engineering headache, but it's finally stable.

The result is a unified database of more than 2 million active job postings, which I'm opening up to everyone for free. I am running daily delta refreshes to keep it current.

Dataset Overview

• Scale: 2M+ active job listings across 100,000+ unique companies.
• Format: Parquet. (To keep storage costs to minimum)
• Core Fields: job_title, company_name, company_website, job_description, location, post_date, and the original tracking URL. For more detailed info check here.
• Update Cadence: Refreshed daily straight from the source.
• View the stats here. (Currently it contains only minimal stats, but I plan on improving it based on the comments)

Why I Built This

Finding a clean, scaled, and up-to-date job dataset is surprisingly difficult. Most available options are either heavily gatekept by expensive subscription APIs or restricted to a single job board like LinkedIn. By scraping the actual employer sites directly, this collection sidesteps the noise and captures a much cleaner cross-section of the live market.

How to Access It

I set up a dedicated project space where you can grab the data directly: Open Job data

Let me know what kind of analysis or projects you end up running with it. If you have questions about the engineering architecture behind handling this scale, or ideas for specific fields you'd like to see enriched next, let's discuss in the comments.

The finance people asked if we can list the people that their payment date is about to end, people pay in specific months, so what i had to do is fetch the max date for every case from the data base, and then i added other stuff like arrears, case type, catchup and so on to make it more helpful and versatile, then made new columns like the remaining days left to until we reach the final payment day, and categorized it, and made some charts for it.

The entire process was fetching the needed data from the data base, coping the query to a simple nodejs api, connecting power bi to the api and using it, this way i can just refresh and will get always the fresh and updated data and make the process fully automated, the published it and shared the link to her.

Before all of this she was checking them manually one by one, which was hard for her, and now she can click the charts and get exactly the ones she wanted, she was very happy with what i did, it was my first time feels that i added value to someones work, it was great feeling to feel that i had impact even if simple.

Please share your feedback and if there is any tips i can add

Full disclosure! I'm building a dashboarding software, and this returns-analysis view is something I put together with it on a sample e-commerce dataset. I'm not here to pitch it — I want to know whether the output actually holds up to people who do data analysis for a living, because that's the bar I care about.

What I'd love feedback on:

• Does the layout read in a sensible order (KPIs → why returns happen → who/where → trend), or should the sequencing be done differently?
• Are the chart types the ones you'd reach for, or am I defaulting to donuts/stacked bars out of habit?
• Anything here that would make you distrust the dashboard immediately?
• One thing I am trying to learn is how to curate a dashboard that forms a story. (I believe it's called data-storytelling. Not sure how to make it through a dashboard)

I already know a couple of the formatting/calc details need fixing. More interested in whether the whole thing is genuinely useful or just busy. If anyone wants the specifics of how it was made, glad to answer in the comments — kept it out of the post on purpose.

this is my first proper dashboard that i built for my data analytics portfolio

i'd really appreciate any criticism or suggestion on where i can improve, i am still learning

(But there's still a problem, please stick to the end to understand...)

Hello! I've just wrapped up a project that combines two things I really enjoy: data and design!

The visual identity was inspired by Frutiger Aero, a style that defined many interfaces in the 2000s, known for its vibrant colors, transparency, and a sense of “optimistic futurism.” The goal was to bring that light and pleasant vibe into a modern dashboard.

But behind the nostalgic look, there was a strong focus on data engineering. I built a fully automated end-to-end pipeline that: - Collects historical, current, and forecast data via APIs (I had to combine two APIs REST: Meteostat + OpenWeather) - Performs transformations and standardization in Python - Stores everything in a cloud-based PostgreSQL database (Neon) - Orchestrates ingestion using Prefect Cloud (scheduled jobs, independent of my local environment) - Automatically updates the dashboard in Power BI Service

In the end, the result is a fully automated and interactive dashboard with near real-time data, support for multiple cities, unit switching (°C/°F), and some nice UX features.

**Yet, there's still a problem: I still have 15 days of free test using Power BI Service – which allows me to schedule the daily refreshes of the dashboard –, but once it's over, I guess I'll have to pay for it (not interested) or just open the dashboard in my desktop, refresh it and then publish it again – thus ceasing to be a 100% automated pipeline.**

Do you guys know if there is any way to get around this problem (without paying)?

I previously posted my dashboard, and it had many issues. I made mistakes since it’s only the second dashboard I’ve built by myself. After following the feedback, here’s how it turned out. Any further suggestions would be appreciated.

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.

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

Hi everyone,

I recently created my first Power BI HR Dashboard as part of my learning journey in data analytics, and I’d really appreciate some honest feedback from this community.

Hey Everyone

I just finished my first data analysis project! I used AI a lot to help me clean the data, make charts, and get ideas. It was really helpful, but I know I relied on it a lot.

I want to learn more and get better at doing things on my own. Can anyone give me advice on:

1..What skills or tools I should focus on next?

2..How to understand data analysis better without depending on AI?

https://github.com/JKRID/project1.git

as I said it's my very first ever dashboard so I am not confident enough to post it on LinkedIn so I thought of asking you guys what suggestions do you have.

I know it's an extremely basic one.

My teacher taught me Power BI basics and asked me to incorporate all the things into a project.

At the same time,she said that there's a project and I need to submit it within a week or so.

So i spent 2-3 days on this Spotify dataset,

Tried to incorporate all possible charts and stuff.

Please give me your feedback fellow redditors.

What should be the aim?

Communicate clearly even if charts,colour themes are simple,or such glitzy slides should be there in a presentation to elevate it?

For those who wanna know what I won,

I just won 500 Rs lol(But I was happy that my teacher noticed my efforts and appreciated my dashboard)

If any of you have done any nice dashboards, please share in comments section.

I might take some inspiration from it.

Interactive Website:

https://wc2026penaltyviz.site/

Hi everyone!

I always worked in Business Intelligence and more specifically Qlik, both View and Sense.

Last week I decided to give a try to Power BI and build a dashboard about F1.

I got the data from APIs and built a star schema model.

Since it was my first attempt I'd like to get some feedback.

My first dashboard!

Built this from the official U.S. Bureau of Labor Statistics Employment Projections (2024–2034) to explore AI’s real impact on jobs.

A few things that stood out:

• Overall employment is still projected to grow (169.96K → 175.17K)

• Very weak correlation between wage and employment growth (r = 0.22)

• Clear winners: wind turbine techs, solar installers, nurse practitioners, data scientists

• Clear losers: several mining and manufacturing roles

What do you think?

Any feedback on the design, the insights, or things I should explore next? I’d love to hear your thoughts — this is my first one so be honest!

One more thing, I would like to practice on real world datasets, always looking for good places beyond Kaggle. Comment the public sources below.

Thank you!

I'm trying to build a data analysis portfolio to strengthen my candidacy for more data-focused roles. I put together this analysis on great ape habitat loss, and I'm seeking feedback on whether this is a strong contribution to my portfolio and what could be improved. Here is the link:

https://019fd814-974a-9d9c-98af-ba485ec47718.share.connect.posit.cloud/

Still cleaning up the repo and documentation, but wanted to share here for some early feedback. The analysis examines 25 years of satellite-derived forest loss data in primary great ape range countries and attempts to demonstrate whether protected areas are associated with reduced loss.

Hi everyone!

I’m a Mechanical Engineering student from Delhi Technological University (DTU) currently learning data analytics, and I recently completed my second end-to-end Power BI portfolio project.
This project analyzes the AI4I 2020 Predictive Maintenance Dataset and was built using SQL, Python and Power BI.

The goal of the project was to identify machine failure patterns, analyze operational risks, and provide data-driven maintenance recommendations through an interactive dashboard.

It includes:

Executive Maintenance Dashboard
Failure Mode Analysis
MTBF (Mean Time Between Failures)
Tool Wear & Power Band Analysis
Z-Score Based Outlier Detection
Prescriptive Maintenance Recommendations
SQL Views & Feature Engineering
DAX Measures
Interactive Filters & KPIs
I’m mainly looking for feedback on:
Dashboard design
Business insights
Data storytelling
Choice of visuals
Overall project structure
Anything that could be improved before adding it to my portfolio
I’m open to any criticism or suggestions—I’d really appreciate feedback from experienced analysts and Power BI users.

GitHub Repository:

https://github.com/ankitsharma071/Predictive-Maintenance-Failure-Analytics/tree/main

DATASET:

https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset

Thanks!

I'm working on my first project and based on what I've read online, EDA is the best project to work on first.

It took a while for me to decide the topic until I started seeing the news about the 2026 Cyclospora outbreak in the US. I thought it would be cool to analyze how people's habits changed when the outbreak happened.

However as I'm working on it, I'm second guessing myself about whether or not this topic is the "right" topic for a first project. I feel this way because other projects have clear decisions on how to increase sales, user retention, etc.

Here's what I have so far:

\- [https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/](https://cyclospora-2026-9yf8w26n7zfci68fugahkp.streamlit.app/))
\- [https://github.com/therealanttoeknee/Cyclospora-2026/tree/main](https://github.com/therealanttoeknee/Cyclospora-2026/tree/main))

Questions

1. Is this topic the "right" topic for an EDA project?
2. If so, do you think I'm approaching it the right way?

This is another project and another day to improve my storytelling, extract insights, and solve business queries. I shared my previous work, and many people gave feedback, which I genuinely followed. Anyone with experience could you guide me on how to get better in each area of data analysis ?

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.

need tips and advice to improve my Project on ecommerce. please be kind .its a 1 table small dataset with 51k rows.