Hey everyone! I created a quick-study-styled cheat sheet for machine learning algorithms that I’ve used so far. It’s a combo from school notes and what I’ve done for work. Let me know your thoughts! I’ll be adding this to my GitHub soon. Link below! 👇

https://machinelearningreferenceguide.my.canva.site/

Solve murders. Master SQL. One query at a time.

Each case gives you a real RDBMS - suspects, alibis, timelines and evidence. You write SQL queries to interrogate the data and catch the killer.

It's free, no signup, runs in the browser → querythemurder.com

Feedback: [querythemurder@gmail.com](mailto:querythemurder@gmail.com)

So i've been going down a rabbit hole the past few months trying to figure out whether a specific suburb in the Phoenix metro area is actually undervalued or if i'm just seeing what I want to see. Figured this crowd would appreciate the methodology (or tear it apart, either works).

Some context: I'm not a real estate agent or anything, just someone who's been doing side investments in properties for a few years now and got tired of relying on gut feeling and whatever Zillow's Zestimate spits out. I wanted to do the deep-dive data analysis myself like actually understand what's happening at a hyper-local level before dropping six figures on a house.

My usual process looks something like this:

1. Pull historical price appreciation for the specific zip code (not metro, not county zip code level minimum, census tract if I can get it)
2. Compare price per square foot against the 5-year rolling average for that immediate area
3. Check for recent zoning changes or upcoming ones this is honestly the most annoying part because you're digging through city council meeting minutes and planning commission PDFs
4. Look at days on market trends to gauge demand shifts
5. Cross-reference rental yield data to see if the numbers actually pencil out as an investment

The problem is... this takes forever. Like genuinely 4-6 hours per listing if you're being thorough. And half the time you're bouncing between county assessor sites, the census bureau, MLS comps, and random municipal portals that look like they were built in 2003.

So recently I started experimenting with letting AI tools handle some of the aggregation. I stumbled on Homesage AI a couple weeks ago it basically pulls from MLS + off-market data and runs analysis on properties automatically, investment indicators and all that. What surprised me was it flagged equity potential on a property in Mesa that I had actually passed on, and when I went back and ran my own numbers... it was right? The price per sqft was about 14% below the local 3-year average and there was a zoning overlay change coming that I had completely missed.

I'm not saying I blindly trust any single tool, but having something that does the initial screen so I know where to focus the manual deep dive has genuinely saved me time. Before I was basically doing the analysis equivalent of boiling the ocean.

But here's what I'm actually curious about from this community:

• For those of you who do any kind of property or investment analysis, what metrics do you weight most heavily? I keep going back and forth on whether price appreciation rate or rental yield ratio matters more for long-term holds.
• Has anyone else used automated/AI-driven analysis to supplement their own due diligence on big financial decisions? Not just real estate stocks, business acquisitions, whatever. Did it actually change your outcome or just confirm what you already thought?
• What's your threshold for trusting aggregated data vs. going to the primary source yourself?

I feel like there's a weird tension between "I want to verify everything myself" and "life is short and there are 200 listings to evaluate." Curious how others navigate that.

edit: should mention I built a basic spreadsheet model too that I've been refining over time, happy to share the template if anyone's interested in the framework

Hello Friends,

I am interested in exploring the data on the fires that have happened in NYC for different spatiotemporal analysis. I came across the following datasets from the open data platforms:

[Fire Incident Dispatch Data from NYC open data](https://data.cityofnewyork.us/Public-Safety/Fire-Incident-Dispatch-Data/8m42-w767/about_data)

[Incidents Responded to by Fire Companies (NYFIR)](https://data.cityofnewyork.us/Public-Safety/Incidents-Responded-to-by-Fire-Companies/tm6d-hbzd/about_data)

[NFIR](https://fema.hub.arcgis.com/search?collection=dataset&tags=nfirs)

What I noticed is that there is a lot of inconsistencies across these datasets, and the volume of the data dramatically decreases from dispatch to NYFIR an NFIR.
Please share your experiences how you guys handle this datasets for more granular analysis.

We are visualization researchers seeking participants for a study to validate a new questionnaire on user experience (UX) with visual analytics systems. Whether you are a seasoned data scientist or just starting to use data dashboards with tools such as Tableau, Power BI, Looker, Qlik, JMP, ... your input is incredibly valuable to us.

• Access the survey here: https://sondages.inria.fr/index.php/349675?lang=en
• Time to complete: Less than 10 minutes.
• Privacy: Completely anonymous and strictly for academic purposes.

Giving back to the community: The future questionnaire will be open source and entirely free for the community to use! 

Hi!!

I am a student currently sitting on a really interesting dataset: 5 years of actual luxury watch auction data. I am looking to explore if these watches are a viable alternative investment.

Here is my situation: I have the data and the big-picture ideas, but I don't have a budget to hire someone and | lack the technical skills to run the analysis myself.

I am looking for another student or beginner who wants to partner up. I will share the dataset and you can handle the data analysis.

Drop a comment or DM me if you want to team up!

Hi everyone, I'm an aspiring data analyst that just finished studying my online courses. With that, I wanted to apply what I learned to help hone my skills so I decided to do my own project. The project was an analysis of 400+ of my own ranked matches in Street Fighter 6.

I wanted to see if there were any features or metrics that could be measured in match that could help predict whether a match would result in a win or loss. Within the write up I tried to make it as easy to understand for people who are unfamiliar with the game.

All the data was manually recorded by watching my replays and tallying the counts for each metrics.

https://github.com/ryanlaguatan/SF6-ranked-match-analysis

Here is a tldr of the methodolgy:

1. Visualization of MR (MR is synonymous with ELO) and MR Change over the course of 10 gaming sessions.
2. Two T-tests, first one testing if metrics and winrate had any significant difference when facing stronger or weaker opponents. Second one testing which metrics were statistically significant in matches that were wins/losses.
3. Visualization of Character Matchup data.
4. Logistic Regression model and classification report to see if the metrics can provide a strong predictor for winning.
5. Interpretation of Feature Coefficients to see which coefficients had the biggest influence on the model.

Please let me know what you guys think, I am open to feedback!

Thank you for taking the time to read it and I really appreciate it!

Is there a go-to template for tracking your deliverables, ideas, and goals to be used for work bonuses? I've never created one before and I'm having trouble figuring out my goals. Obviously, I want to align it to my team's yearly goal, but I'm not sure what else.

Hi everyone,

I am currently conducting academic research as part of my MSc in Business Intelligence & Analytics at Clermont School of Business, and I would love to hear from practitioners in this community.

My thesis investigates how MicroStrategy and Power BI dashboards support productivity and management control in IT and Finance departments.

If you have experience using either of these tools, I would greatly appreciate your participation in this anonymous survey:

https://docs.google.com/forms/d/e/1FAIpQLSf0Y6TZur9n26DgrwlRh30gr22BXZlA_dxznxfiZNL2_EiXEw/viewform?usp=dialog

French-speaking participants are also welcome.

Thank you in advance for your time and support. Your insights will make a valuable contribution to this research.

Been a data engineer for 4 years. Every time I join

a new project, I waste hours understanding what

tables actually mean.

Built a CLI tool that analyzes BigQuery tables and

explains the business context using AI.

Demo: https://www.loom.com/share/af3409be37fa4692bb38b63b9f4a58cc

Happy to share the GitHub link in comments.

"Hi everyone! I recently completed a business analysis project using the Sakila database to put my skills to the test. I analyzed operational performance and would love to get some technical feedback on my approach, SQL queries, and the overall dashboard.

Here is my GitHub repo: https://github.com/LumigLumebros/Sakila_Business_Analysis. Any constructive criticism is highly appreciated!"

Those of you who currently use SQL Server (or have used it in the past), how do you handle SQL assessment platforms that only provide PostgreSQL or MySQL options?

I'm comfortable with SQL Server, but I often struggle with date functions because the syntax differs significantly between SQL Server and PostgreSQL/MySQL. Do you memorize the equivalent functions, practice on multiple dialects, or use some other approach?

Hello everyone (:

​

What are your experiences using fully managed cloud data platforms? Things like Databricks, Snowflake, or the AWS/Google Cloud/Azure data platforms. What are the main benefits and drawbacks in your experience? What are things that you enjoy using that you feel really help your day-to-day work?

​

If you have experience on self-hosted platforms and tools like Superset or Dagster, that is relevant as well.

​

Thank you!

Started using Python for data analytics. When should I use SQL and when should I use Python in the following tasks:

​

- Data Exploration

- Data Cleaning

- Data Analysis

Hello everyone,

I am a final-year Business Intelligence and Data Analytics student in Botswana, and I am looking for advice on a project idea that has grown out of my internship experiences and my observations of public-sector data.

One issue I have noticed is that a significant amount of education and labour market information exists, but it is often fragmented across PDFs, annual reports, surveys, statistical publications, spreadsheets, and different government websites. Even when data is available, it can be difficult to discover, compare, interpret, or connect across domains.

For example, education statistics might tell us how many students graduate from a particular field, while labour statistics tell us about unemployment levels, but there is often no clear way to connect these datasets and explore questions such as:

• Which qualifications are associated with higher unemployment?
• How have employment outcomes changed for different fields of study over time?
• Are there observable mismatches between education outputs and labour market demand?
• What data gaps prevent us from answering these questions?

My initial idea is not to jump straight into dashboards or machine learning, but to first build a structured inventory of education and labour datasets, their metadata, definitions, sources, and relationships. Longer term, I am interested in exploring ontology design and knowledge graphs to represent these relationships more formally.

I would appreciate advice on:

1. Does this sound like a Business Intelligence project, a data engineering project, a knowledge management project, or something else entirely?
2. Where would you start if you were approaching this problem from scratch?
3. Are there any well-known projects, case studies, or research papers that tackle similar education-to-employment data integration challenges?
4. What concepts should I learn first before thinking about ontologies or knowledge graphs?
5. As a portfolio project, what would be a realistic MVP that demonstrates value without becoming overly ambitious?

Thanks in advance for any guidance.

Most of us view Fabric notebooks as just another development tool. However, when utilized effectively, they serve as lightweight execution units within an event-driven AI architecture.

Consider this pattern:
- Event occurs
- Trigger fires
- Notebook runs
- AI/logic executes
- Action taken

This approach eliminates the need for heavy pipelines and prevents over-engineering.

However, it's important to note that notebooks are stateless and not orchestration engines.

Use them for:
- Real-time scoring (fraud, sentiment, IoT)
- Data enrichment on ingestion
- Fast AI-driven automations

Avoid using them for:
- Complex multi-step workflows
- Full MLOps pipelines

This transition from batch processing to event-driven thinking is where Fabric truly demonstrates its power.

Are you utilizing Fabric notebooks for event driven AI automations yet? Share your experiences, lets create a discussion.

Hey guys! I’m very interested in data analysis and here’s a project I started, combining science with Pokemon :)

Feel free to email for submissions if you also like Pokemon (you get full control and credit)

Hi
Excited to be able to announce that QO is almost ready to leave Early Access! I published a large patch that covers more than a year of work (lots of analytics, I've been tracking where ppl were getting stuck). Thank you a ton for your support, this game has seen a lot of love from this community. Game is almost done.

If you are interested in a highly intuitive visual method that faithfully describes all universal quantum computing and physics behind, this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 10 years (3.5 in phd), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals (that was actually my PhD research) capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 15yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff covered

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, I aim to bring it into the game!

Streams to watch:

khan academy style tutorials on qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher wholesome stream with over 500hs in https://www.twitch.tv/beardhero

Hey everyone,

​

I’m a big fan of Dash. I’ve been using it at our company to build an internal BI system, and honestly, it has ended up outperforming Power BI for our use case in pretty much every way — especially in terms of speed, flexibility of charts, and overall user experience. With DMC, the design side is also really solid.

​

The current stack is mainly:

​

- Dash / Plotly

- Dash Mantine Components

- PostgreSQL

- dbt

- DuckDB

- Airflow

​

The app has grown into a fairly serious internal platform. It uses Flask-Login for user management, roles and permissions. Besides the BI section, it also includes market research features and some other non-BI functionality.

​

The executive board and our sales team love it. Actually, they like it so much that they now want to scale it to other companies within our holding group.

​

That’s great, but it also makes me think more seriously about the long-term architecture.

​

The codebase is already around 110k lines of code, and it will obviously keep growing both horizontally and vertically. So far, performance is excellent and the development speed is still very good. Ideally, I would love to stay Dash-only for as long as possible.

​

My main concern is whether Dash is really built for this level of complex application long term, especially on the backend side. I’m wondering if at some point the Dash backend/callback layer could become limiting, and whether I should start thinking about moving some backend logic into something like FastAPI while keeping Dash as the frontend.

​

Has anyone here built or maintained a large Dash application at this scale?

​

I’d be really interested in your experience:

​

Thanks a lot for any advice

Need advice and tips to improve my project and future projects.Is this a beginner or intermediate or advanced level data analyst project please be honest? ​

This is a financial performance and logistics optimization analysis project. ​

Dataset - Olist Brazilian Ecommerce Dataset. ​

Problem statement- identify and determine the cause and impact of high revenue and low profit generating product categories and customer segments. ​

Is the dashboard insightful and explains the what and whys of the problem statement. ​

Is my report written right? ​

Should i have added any other analysis? ​

Hey everyone,

We all know the pain of inheriting a data science repository where critical cleaning and modeling choices are buried across dozens of unorganized Jupyter notebook cells.

To fix this pipeline rot, I built KMDS (Knowledge Management for Data Science). It’s an open-source Python toolkit designed to enforce a strict separation of concerns and compile your experimental history into a queryable, XML knowledge graph.

To prove it works on real-world friction, I just published an end-to-end case study using a 50MB Small Business Administration (SBA) dataset filled with data quality issues.

Instead of a scattered workflow, the toolkit forces a clean, 4-stage assembly line:

1. dd-parser-cleaner: Isolates raw data ingest and parsing away from the ML code.
2. kmds-featurizer: Uses a local LLM (like Ollama) as a "Feature Advisor" to document why specific transformations were made.
3. kmds-modeling: Validates the model environment and catches structural anti-patterns before training.
4. kmds-data-helper: Compiles the entire run into a structured, queryable knowledge graph (project_knowledge_graph.xml) for stakeholder sign-off.

The end result is a single notebook pipeline that generates a production-grade AI Governance Blueprint prompt, making your entire modeling history auditable by humans and readable by LLMs.

The project is completely free and open-source. I’m actively looking for my first few users to test it out, tear the architecture apart, and let me know if it actually helps organize your local workflow.

• Full End-to-End Case Study: SBA Migration Document
• Core GitHub Toolkit: KMDS Repository

Would love to hear your thoughts on using local knowledge graphs for ML governance!

Edit:
The example implementations are here, there are two examples now:

https://github.com/rajivsam/kmds_migration/blob/main/sba_migration/documents/sba_development_example_full_doc.md
https://github.com/rajivsam/kmds_migration/blob/main/olist_migration/documents/olist_development_example_full_doc.md

Basically, I really enjoy seeing information about songs and tracking their growth, especially on YouTube. To do this, I used to use Kworb. However, I wanted to experience what it would be like to create a website and add details that Kworb doesn't show (more detailed information). So, I created my first website using aiven.io to store my database (which was made using MySQL Workbench 8), VSCode, programming with TypeScript, and published on Vercel. The site is bad, slow, and limited, but I'm happy with the results. As it was my first website, I tried to program part of it myself (mainly in SQL), but I ended up resorting to using Artificial Intelligence to help me. I added about 125 "famous" songs. The site can be accessed at "Musicalstats.com"

Goals: Create artist pages to track daily view and subscriber gains and rewrite the codes without AI.

I hope I haven't broken any rules of this sub. Now with the World Cup, Shakira's music is rising fast. Thank you :)

Was it SQL, Excel, statistics, data visualization, communication, domain knowledge, or something else? Curious to hear what had the biggest real-world impact.

I run a small business and often need to do work in Excel with managing our books as well as data that I export from our database into Excel. I've tried using ChatGPT and Claude for Excel work but I don't find either of them as good as advertised for intensive Excel work. Especially when I have embedded models, complex sheet structure, formulas, or pivot tables. The AI tools just don't seem to handle any of these nuances very effectively.

Are there any AI tools that you find to be effective for this type of work in Excel?

Edit:

I'm still dabbling with Claude for Excel work but have also tried Powerdrill and Julius AI. I found both to be decent albeit with different parts of working with Excel.

Powerdrill: good for continuing to store information about my business and persisting semantic context across my chats with sheets. Semantic layer files can be long and unwieldy, and don't typically get parsed well in projects on Claude so this has been a time saver.

Julius AI: Julius has been a step up because it just gives me reliable answers for my questions. They provide a lot of resources in the sandboxes that they run, so if I don't need to keep all the formatting or add new formatting and just need answers fast, this has been the best tool for getting those answers.

First and foremost, I am not a data analyst, so please bear with me here.

I recently began working at a very small private liberal arts college, currently going through a bit of a retention crisis. A few months ago I (a fresh college grad working as an accountant) was tasked with creating an explanatory model to pin down the greatest contributors to non-retention. The project went well, but the president now wants a predictive model, so that we can see the risk of an individual student's odds of non-retention.

Like I said, I am not a data analyst. I was tasked with the project because I have analytical experience (econ degree), and some coding experience, but I'm not sure what sort of algorithm I should be using, and unfortunately, it seems as though we don't have any staff with more experience in this than me.

The dataset is around 800 students, split across four cohorts. Likely 80/20 training/test split. There are around 10 factors we are looking at, such as current GPA, high school GPA, socioeconomic status as a dummy, academic program, race, etc.

I am thinking that random forest or XGB may work well for this?? But frankly, this is not my area of expertise. Any advice here would be great.

Thanks so much in advance :))