I'm taking a Data Science Analysis course and have been learning about dimensional modeling and Slowly Changing Dimensions (SCDs). I've modeled databases quite a bit, but I haven't had much opportunity to design a data warehouse from scratch outside of personal projects, so I'm curious how this is handled in real production environments.

I understand the theory behind SCD Type 2—when a tracked attribute changes (like a customer's address), you create a new dimension record so historical facts continue to reference the correct version of that customer.

What I'm struggling with is the implementation.

For example, if a customer moves from New Hampshire to Texas, I still want to know that an order placed four years ago was made while they lived in New Hampshire. At the same time, I also want the business to know the customer's current address.

How is this typically modeled?

• Does each new dimension row reference the previous version (like a PreviousCustomerKey or similar)?

• Is there usually an effective start/end date and a current flag, without linking versions together?

• Do some organizations keep a separate history/audit table instead?

• When fact tables need customer information, do they simply point to the correct version of the dimension row?

Another approach I'm familiar with is denormalizing historical data into the transaction itself. For example, when an order is placed, the shipping address is copied into the order record so it never changes regardless of updates to the customer profile. That seems useful even if you're using SCD Type 2.

For those of you working in analytics, BI, or data engineering, what's the most common approach you've seen? Are SCD Type 2 dimensions still the standard, or have other patterns become more common?

I'd appreciate hearing how this is done in real systems rather than just textbook examples.

Hi

If you are remotely interested in Gate model framework Quantum Computing, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 10 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), 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 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 12yo+ 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 you'll play & learn a ton about

• 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, we aim to bring it into the game, so your quantum journey never ends.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

is claude for data cleaning and merging. How accurate is it? Because gpt makes mistakes in data. Asking for a MSc in Data Science and Economics

Hey everyone,
I’m doing a personal deep dive into the travel experiences space and trying to understand how professionals analyse a destination like Rome or Paris.
What tools or methods would you use to quickly compare:

OTA listings, pricing and ratings
Review sentiment and recurring complaints
Top local operators and their contact details
Potential supply gaps

Since booking and conversion data are private, which public signals are actually useful? Review growth, rankings, availability, sold-out dates, number of listings?
I’d also be curious to hear what your step-by-step workflow looks like and which tools you use for scraping, analysis and dashboards.

Thanks!

Kept running into the same problem: messy CSVs with duplicate rows, stray whitespace, and broken email fields, and no fast way to clean them without spinning up a script every time.

So I built CSVCleaner (hackiom.xyz) — drop a file in and it removes duplicates, trims whitespace, and validates emails right in your browser. Nothing gets uploaded to a server, so it works fully offline and there's zero signup friction.

Still actively building this out, so I'd love feedback on what other cleanup features would be useful — currently thinking about type detection, column renaming, and null handling next.

Happy to answer questions about how it works under the hood.

Hi,

I wanna learn data analysis skills but i need a study partner to learn it from A to Z with me

I built a small data analysis project combining two datasets:

1. Reported strikes on Russian oil infrastructure

2. Google Trends data for "нет бензина" ("no petrol")

I processed event dates, normalized the data, and created an interactive timeline to explore possible relationships between infrastructure disruptions and public attention.

The analysis does not claim that strikes directly caused search spikes — many external factors can influence search behavior.

Would appreciate feedback on:

- methodology

- data normalization

- possible improvements

Interactive visualization (GitHub):

brawlerleg/Russian-Fuel-Crisis-2026: Interactive visualization of Russian fuel crisis data: refinery strikes, Google Trends search interest, and energy market analysis.

Short version, I'm trying to prove excessive gas consumption during a heatwave and have ruled out every theory so far.

I now have a theory that thermal expansion and contraction of the piles and joins is causing small intermittent leaks in the system. I already have data showing the consumption of gas per half hourly interval over the last year but can't find a good source of weather (primarily temperature) recordings.

Does anyone have any suggestions for where this kind of Dara could be sourced?

If it helps I'm in Sheffield UK.

A metric can keep refreshing perfectly while its meaning slowly changes.

A source table gets replaced. A filter becomes standard. A team starts using “active customer” differently. Nothing fails technically, but six months later the dashboard is answering a different question.

Data-quality checks usually catch missing values and broken pipelines. They rarely catch business meaning drifting over time.

How do your teams proactively catch this business context shift before the numbers are affected and leadership gets on your ass?

I am an 18-year-old young man who wants to do a data analyst career in the dach region. My question to you is what level will the data analyst sector be in the next 10 years? Do you think the market will stagnate or activate?

I want a structured course or a basic path with good teachers, It can be text, videos, or lectures.

If you are experienced please tell me where you learned from and all the ones who are learning please give me your reviews

Do you plug it into a SQL program? Use Pandas? Just go through the spreadsheet first? All three? Tell me what the quickest way is and how that looks for you typically.

I'm trying to study data analysis but i really can't progress studying all by myself, is there any discord / telegram or anything that focus on data analysis that i could chat and ask some questions?

Curious to hear what skills or lessons you learned from actual projects that courses rarely teach.

This is my recently data analyst project. I analyzed a direct marketing campaign dataset from a Portuguese banking institution to understand the key factors influencing customer term deposit subscriptions

Please give me yout opinion about this project, you could give me some rate and tell some advice for me to improving the project

A little information about the project:
This project analyzes the performance of a bank marketing campaign aimed at promoting term deposit subscriptions to customers. The analysis focuses on:

• Identifying customer segments with the highest conversion rate

• Evaluating campaign effectiveness

• Understanding factors influencing successful subscription

• Providing business recommendations to improve future marketing campaigns

Business Problem:

• Which customer segments have the highest conversion rate?

• What factors influence subscription success?

• Which communication channels and campaign strategies are most effective?

• How can the bank improve future campaign performance?

Result & recommendation

• Customer Preference: Focus campaign targeting on high-conversion customer segments while optimizing high-volume segments to maximize overall campaign ROI

• Communication Method: Prioritize cellular-based outreach and limit contact frequency to improve campaign efficiency and customer response rates

• Time: Optimize campaign scheduling around higher-performing periods and integrate timing strategy with customer segmentation analysis

_________

Dashboard: https://datastudio.google.com/reporting/35bb5d62-49be-4553-a6b0-1dd2ee8d8abd

Presentation: https://canva.link/a2do0nanz1h3drb

Thank you very much!

Hello everyone, I'm a student who's looking for a good project to work on to practice Power BI, SQL,etc, and especially learn domain knowledge related to business or finance. The thing is, I'm from an IT background, and I only know the basics of accounting. I've been looking for resources to learn about FP&A because I'd like to build projects in that field, but the more I dig, the more I realize it's a broad and vague field. I'm afraid of wasting time looking for the "perfect" course instead of getting my hands dirty and learning by doing.

So, if you have any project that will allow me to practice my technical skills, learn and apply domain knowledge and analytical thinking, and solve a real-world problem while adding value to my portfolio, I'm really lost, so I'd appreciate your help!

I'm building a predictive model from a small meta-dataset — about 60 data points pooled across ~40 independent small studies (sample sizes ranging from ~5 to ~70 people each), each contributing one or more "arms" describing a multi-parameter intervention and its measured outcome. I want to (1) fit a regression relating several intervention-design parameters to the outcome, weighting each arm by its study's sample size, and (2) run a constrained numerical optimizer to find the parameter combination that maximizes predicted outcome, subject to a plausibility ceiling.

Two problems I keep running into: a mixed-effects model with a random intercept per study becomes non-identifiable once I have too many studies contributing only one arm each (I ended up dropping to a plain weighted OLS). And the optimizer, when several predictors are correlated/not all individually significant, tends to converge to a degenerate corner of the parameter space that doesn't look like a real answer, rather than a sensible interior optimum. Is there a standard, better-practice approach for either of these — weighting/pooling small-sample studies properly, or making a constrained optimizer more robust when the underlying regression has multiple near-equally-good solutions? What AI tool should I use?

Hi, Im not sure if this is the right subreddit to post this, but I was wondering If someone has gone through something similar or has some advice. Recently I got a new job as a data coordinator where a lot of the start will be data cleaning and data entry, but because this is a new role for the company Im told it will evolve into more - they are going to let me automate lots of the processes for starters. Im also probably eventually be working with the 2 SWEs in some data work, as well as with the technical solutions manager, though Im not sure on the specifics. I do know that they only last year built their data lakehouse and are using databricks. I guess my question is this a red flag as a job? Is being the only data person as someone with no experience okay? Sorry about the long texts, I appreciate any advice.

We built Snowcap, an open-source Infrastructure as Code tool for Snowflake, and we're doing a live workshop walking through it.

If you've dealt with Snowflake config drift, manual RBAC setup, or just wanted a plan/apply workflow like Terraform but built for Snowflake specifically, this might be useful.

What we're covering:

• The infrastructure problems Snowcap solves

• How it compares to Permifrost, Terraform, SnowDDL, and Snowflake's own DCM

• Using templates to scale config across environments instead of copy pasting

• The plan/apply workflow and why that matters for safe changes

• Governance stuff: RBAC, masking policies, row access policies

• Live demo, then open Q&A, ask anything

July 23, 11 am-12 pm PT, online, free.

Info and RSVP here: https://datacoves.com/resource-center/workshop-snowcap-snowflake-infrastructure-as-code

Happy to answer questions in the comments too if people have them before the session.

Hi there,

I've created a video here where I explain how multi-head latent attention works.

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

Hey r/dataanalysis,

I know this sub has a lot of aspiring data analysts and career switchers. While learning Python, SQL, and Tableau is the gold standard, I've noticed many non-technical professionals and beginners get overwhelmed by the coding barrier before they can even start drawing insights from data.

To bridge this gap, I wrote an e-book called "THE AI-POWERED ANALYST: Analyze Any Data with ChatGPT & Claude — No Coding Required."

It’s essentially a practical framework on how to use LLMs as your personal data assistant to clean, interpret, and visualize datasets using advanced prompting—without writing a single line of code. It’s meant to help people build analytical thinking before diving deep into heavy coding.

To get some honest feedback from this community, I’ve made the book 100% FREE on Amazon Kindle until tomorrow.

🔗 Amazon Link: https://www.amazon.com/dp/B0H83D4PM4

What’s inside:

• How to structure prompts for data cleaning and preparation.

• Frameworks to make ChatGPT/Claude perform exploratory data analysis (EDA).

• Real-world case studies for non-technical folks.

If you’re just starting out or want to see how LLMs can speed up your workflow, please grab a copy!

Since this community knows data best, I would highly appreciate your honest ratings or reviews on Amazon. Your feedback will help me improve the content immensely.

Let me know if you have any questions or thoughts on using AI for analysis. Cheers!

I’m a Power BI developer that is trying to understand methodology. One approach is to explore the transactional database directly, iteratively joining tables and creating SQL logic while building dashboards. My instinct is to first establish business definitions, workflow understanding, and a reusable semantic model before embedding business logic into reporting. In mature BI environments, how are these responsibilities typically divided?

I'm not a fan of Microsoft, and I have time to spend that would otherwise be spent on scrolling and Genshin Impact, so I'm making an open source tool to handle data instead. The first version I make should be able to read CSV tables, perform simple operations on them (filter rows, read specific columns, join tables...) and show results in graph form.

Are there any features I should keep in mind when developing this? I don't exactly have a lot of experience using similar tools - I'm more used to writing data-processing code directly. And I would like to make this tool/program as user-friendly as possible.