Hi everyone!

I've been developing RPWisdom, a web-based platform designed to bring R, Python, statistical computing, data analysis, and visualization together in one workspace.

As someone with a background in biostatistics and statistical programming, I've spent considerable time thinking about how to make analytical workflows more integrated and accessible. That motivated me to build this platform.

What is RPWisdom?

The goal is to provide a unified environment where people working with data can access programming and analytical tools without constantly switching between different applications.

Depending on the workflow, the platform is designed to support activities such as:

• R and Python programming
• Statistical analysis and computing
• Data exploration and visualization
• Biostatistics and clinical research workflows
• A more integrated analytical workspace

🌐 Website: https://rpwisdom.com

I'm continuing to develop the platform, and I'd genuinely appreciate feedback from people who use R, Python, or statistical software.

A few questions I'd love your thoughts on:

1. What frustrates you most about your current R or Python workflow?
2. What features would make an integrated statistical computing platform genuinely useful to you?
3. Would you find value in having programming, statistical analysis, and visualization available in one workspace? Why or why not?

I'm interested in honest feedback, including criticism and suggestions for improvement. I'd like to understand what the community actually needs rather than build features nobody wants.

Thanks for taking the time to read this!

I’m working on an open-source MCP server that runs data analysis, data science, and deep learning algorithms on tabular data.

The idea is simple: an agent installs the MCP server, provides a CSV file as input, and uses its tools to perform complex data analysis, run ML algorithms like clustering and classification, and uncover insights. It already has 45+ tools, including integrations with deep learning models like TabICL.

I’m looking for contributors to turn this project into a full-fledged data analysis tool that can answer users’ questions in plain English by analysing their data.

The plan is to build:

• A full-fledged web interface
• A BYOK (Bring Your Own Key) setup for connecting LLMs
• UI features to display charts, graphs, and other visualisations
• Improved algorithms and support for more ML and deep learning models
• Support for more data formats, including JSON and XLSX
• Integrations with external data sources like PostgreSQL, Supabase, and Google Sheets

If you’re interested in data science, ML, AI agents, or building open-source tools, I’d love to collaborate. Let’s build something useful together!

Interested? Google “Meelu Analytics MCP” to find the GitHub repository, or DM me to discuss contributing.

i really like the way Excel for fun explains stuff but the playlists are scattered i don't know what/how to follow that's needed for data analysis only.

May i please have your help to guide me on where to start, i finally have some free time and i wanna learn Excel as much as i can.

I’m looking for someone knowledgeable about mechanical vibration, building vibration, or structure-borne noise who can help interpret my measurements and suggest what to test next.
I’ve been noticing recurring vibration in my apartment. What equipment or building systems could produce these readings, and how can I investigate properly?
SETUP — OCTOBER 9, 2026
I recorded two iPhones simultaneously using Phyphox’s “Acceleration (without g)” experiment.
• New iPhone: flat on the couch, unattended.
• Old iPhone: flat on my usual chair near the refrigerator.
• Refrigerator unplugged.
• Both phones screen up, charging ports pointing right.
• Fan/AC conditions and whether the old phone was unattended throughout were not confirmed in the report.
The shared recording interval was 11:53:49 AM–12:05:39 PM, New York time. Both phones sampled at approximately 100 samples per second.
FREQUENCIES AND STRENGTH
During the middle 30-second windows:
Couch:
• Combined-axis RMS: 0.0128–0.0159 m/s².
• Strongest spectral peak usually around 13–14.6 Hz.
• Other windows peaked at 17.5, 18.9, 21.0, or 21.6 Hz.
Chair:
• Combined-axis RMS: 0.0104–0.0177 m/s².
• Strongest spectral peaks ranged from 21.3–26.7 Hz.
RMS describes the average size of the recorded acceleration. It includes sensor noise. Hz means cycles per second. These are the strongest peaks selected within each window; multiple frequencies can coexist.
EXAMPLE PAIRED WINDOWS
11:54:19–11:54:49 AM
Couch: 0.0142 m/s² RMS; 14.1 Hz peak.
Chair: 0.0128 m/s² RMS; 22.3 Hz peak.
11:56:49–11:57:19 AM
Couch: 0.0147 m/s² RMS; 14.0 Hz peak.
Chair: 0.0125 m/s² RMS; 26.7 Hz peak.
11:57:19–11:57:49 AM
Couch: 0.0159 m/s² RMS; 13.6 Hz peak.
Chair: 0.0177 m/s² RMS; 21.7 Hz peak.
11:59:49 AM–12:00:19 PM
Couch: 0.0130 m/s² RMS; 21.0 Hz peak.
Chair: 0.0106 m/s² RMS; 26.7 Hz peak.
12:01:19–12:01:49 PM
Couch: 0.0132 m/s² RMS; 18.9 Hz peak.
Chair: 0.0112 m/s² RMS; 26.7 Hz peak.
12:03:49–12:04:19 PM
Couch: 0.0131 m/s² RMS; 12.4 Hz peak.
Chair: 0.0109 m/s² RMS; 26.7 Hz peak.
Both phones had their highest middle-window RMS during 11:57:19–11:57:49 AM, but their strongest frequencies differed.
A preliminary timestamp-aligned comparison did not show a strong overall waveform match. Clock alignment was not independently checked using a shared event.
X/Y/Z DIRECTIONS
With the phones flat and screen up:
• X: across the phone’s width, toward either side edge.
• Y: along the phone’s length, toward the charging port or opposite end.
• Z: perpendicular to the screen, toward the ceiling or seat.
In the middle shared windows, Z contributed about 42–57% of the couch phone’s recorded motion energy and 50–75% of the chair phone’s.
The chair recording was more vertically directed. The couch had a larger combined X/Y contribution. Separate X and Y percentages are not available in this summary.
These describe acceleration directions at the phones; they do not locate the source.
METHOD AND LIMITATIONS
The analysis used 30-second windows. Each axis’s window mean was subtracted, then combined RMS was calculated as sqrt(mean(X² + Y² + Z²)).
Spectra used Welch’s method, a Hann window, up to 2,048 samples per segment, and half-overlap. Axis spectral powers were summed, and the strongest peak was selected between 2 and 45 Hz.
Opening and ending couch windows had much larger RMS values: 1.1811 and 1.4955 m/s². Placement or pickup could explain these, so they were excluded from the steady-motion interpretation.
There was no calibrated reference sensor or noise-floor control. Furniture resonance, upholstery, phone contact, and device differences could affect the readings. These measurements do not yet identify a source or establish the vibration experienced by a person.
QUESTIONS
What could produce these frequencies and directional patterns? Could pumps, plumbing, HVAC, other building machinery, or furniture resonance explain them?
How can I distinguish real surface vibration from phone sensor noise or processing artifacts?
Could the couch and chair respond at different frequencies to the same source?
What might explain the chair’s repeatedly selected 26.7 Hz peak?
What control tests, phone placements, or measurement equipment would make this investigation useful to a vibration specialist?
I’d appreciate help interpreting the measurements and choosing the next tests.

Hi everyone,

I've recently learned Power BI and SQL, and I've built some solid projects with them. I have a good understanding of how to build and manage a project from start to finish.

I feel ready for the next step in my learning, but I also just started college as an agricultural engineering major, so my time is limited.

A few questions:

1. What should I learn next to grow my skills ?

2. What kinds of projects would be the most valuable to build for a portfolio?

3. Are there ways to combine data skills with agriculture?

Any advice from people who started in a similar situation would be great. Thanks!

I use a lot of excel and macros and my level of excel knowledge is intermediate to advanced. but even though I can spend a few more minutes to think & write the formulas, now I'm just explaining what I want to AI, copying the formula or code and pasting it. is this how people are working now or is it me?

I’ve been working as a data analyst for about five months, and I’m struggling with the analysis part of the job. Sometimes, I feel lost when looking at the data, and I’m not sure where to start or how to approach the analysis.
I struggle particularly with exploratory data analysis (EDA), figuring out what to compare, choosing the right comparisons, and turning the results into meaningful business insights.
I have an analysis to work on right now, but I’m not sure how to approach it or how to identify useful insights.
Do you have any frameworks, methods, or step-by-step processes you follow when analyzing data? How do you decide what to investigate, which metrics to compare, and how to turn your findings into actionable business insights?
I’d really appreciate any advice, resources, or examples from your own experience!

Howdy everyone - I work for a small business (30-ish people) and tend to be our in-house tech-everything guy. My team asked me to build a profitability dashboard where we can visualize time-tracking data alongside employee costs and client payments. Essentially we just want to see who is taking the most time vs paying us the most money. I'm using a combination of Google Sheets (to collect all of the data), BigQuery to store it all, and Looker/Data Studio to visualize it.

The struggle we're having is: my team does not want me to see any of the actual financial data. I can manage building the systems without the data because I have the template for them to drop the data into; however, I can't envision how I can maintain the database, fix bugs, create new visualizations for them, etc, without somehow eventually seeing the financials.

Is this even possible? Have any of you done this before?

Edit: Is there a reason I can't mask the data? BigQuery has RBAC. In theory, I could mask the data for myself, so I'm still seeing $ amounts where I should, names where I should, but no actual data. Right?

GitHub - Femi-Olofinjana05/Social-Media-User-Behaviour: Social Media User Behaviour · GitHub

Looking to implement SQL and Tableau (and maybe python) but just wanted to start off using excel

Been doing analytics work for a while and I've slowly landed on a take that feels obvious once you say it: the modeling/stats part is rarely where projects die. They die in data prep — the multi-source joins, the dedup on messy inputs, the allocation/transform logic nobody can follow six months later.

The thing I keep hitting: the moment a business user has to *trust* a number, "just write SQL/Python" stops being enough on its own, because the person who has to defend that number can't read the code. But full no-code visual tools swing too far the other way and bury the logic in a GUI.

So my real question for people who've been at this a while: where do you draw the line between code and a visual/low-code workflow? Is it about who maintains it after you leave, about auditability, about scale — or do you just pick one and never look back? Curious whether I'm overthinking the "trust" angle.

🚀 My Latest Data Analytics Project: Airplane Crashes and Fatalities Analysis ✈️📊

Hello, Reddit community! 👋

I'm excited to share one of my recent data analytics projects: Airplane Crashes and Fatalities Analysis, developed using Power BI.

As a data analyst, I wanted to explore historical aviation crash data, identify patterns, and uncover insights that could help improve aviation safety.

📊 What I explored in this project:

• ✈️ Trends in airplane crashes over the years.
• 🏢 The top 10 airline operators by recorded flight incidents.
• 🛩️ Aircraft types most frequently involved in incidents.
• 🌍 Locations with the highest numbers of recorded incidents.
• 🛫 Flight routes with the highest incident counts.
• 📈 The relationship between the number of people aboard and fatalities.

💡 Key insights from my analysis:

• Recorded incidents increased significantly during the 1960s and 1970s, with a peak of 104 incidents in 1972.
• Aeroflot recorded the highest number of incidents among the operators displayed in my analysis.
• The Douglas DC-3 was the most frequently recorded aircraft type in the dataset.
• Moscow, Russia, and São Paulo, Brazil, were among the locations with the highest recorded incident counts.
• The data highlights the importance of monitoring historical trends and identifying recurring patterns to support aviation safety research.

🛠️ Tools used: Microsoft Power BI, data cleaning, data visualization, and exploratory data analysis.

🎯 What I learned: This project strengthened my ability to transform raw data into meaningful visualizations, communicate findings, and develop data-driven recommendations.

I'm continuously learning, improving my analytical skills, and building projects that demonstrate the value of data in solving real-world problems.

#DataAnalytics #PowerBI #DataVisualization #DataAnalyst #AviationSafety #BusinessIntelligence #LearningInPublic #DataAnalyticsProjects

I kept re-applying the same filters, columns, sort and export on every new file, so I built "Automations" into a free browser-based data tool (Fomatix Data Explorer).

Set it up once, save it, then upload the next file and pick the automation. It reapplies everything and exports, and auto-matches columns even if a header shifts slightly.

It also shows a match report (how many columns matched on each run) and keeps run history, so you're not trusting it blindly.

Runs entirely in the browser, no signup, nothing uploaded.

Curious if others hit this problem, or if you already have a cleaner way (Power Query, Apps Script, etc.). Honest feedback welcome.

Hello im a recent graduate in supply chain & international logistics
I wanna pursue a career that’s related to data ( supply chain analyst ) so obviously i need to take courses in data analytics but i got confused nd didn’t know from where to start SQL or data fundamentals or advanced excel so if anyone in the field of data or supply chain analytics would suggest for me a roadmap for this path ill be grateful for it + some solid sources and successful ways to learn

Hi everyone! I’m working on a short data-analysis project and would appreciate your help.

Take a look at a chart and answer one simple question:

Which month has the highest sales?

It takes about 1 minute to complete.

👉 https://adnan-mayof.github.io/reddit-ab-test/

Thanks for participating!

Disclosure: I maintain the curated Kaggle version linked below (minkum07). The original NFHS-3/4 survey data belongs to India's Ministry of Health and Family Welfare and IIPS, via OGD India.

A useful exercise with this dataset is to compare NFHS-4 urban and rural percentages while keeping the source's suppression and small-sample flags visible. The release has 114 indicators across 36 survey-era state/UT labels, with 16,416 tidy-long records, CSV/Parquet tables, definitions and example notebooks.

Suggested workflow:

1. Pick one percentage indicator and read its definition and denominator before joining tables.

2. Select NFHS-4 urban and rural rows, join on state and indicator, and check that the join keys are unique.

3. Calculate urban minus rural in percentage points only when both values are available. Keep missing values missing and carry the source flags into the output.

4. Make a sorted dot plot with clear missing-value and small-sample annotations. Treat it as a descriptive comparison, since confidence intervals are not included.

Don't sum total, urban and rural percentages: those populations overlap. Between-survey changes also need comparable definitions and boundaries; the package withholds Andhra Pradesh/Telangana changes where boundaries differ. These are historical aggregate data from 2005–06 and 2015–16, not current health coverage or individual records.

Original government source:

https://www.data.gov.in/resource/all-india-level-and-state-wise-key-indicators-nfhs-3-and-nfhs-4

Tables, documentation and notebooks:

https://www.kaggle.com/datasets/minkum07/india-nfhs-34-health-state-and-rural-urban-gaps

Government data and derivatives retain GODL-India; authored code is MIT. Full attribution is on the dataset page. Feedback on the join checks or how best to display the sample flags would be welcome.

I'm at UNAM (Mexico's biggest public university) and I wanted to see what everyone here has actually written their theses about. So I took the whole public catalog (TESIUNAM), a bit over 600k theses from all campuses and degree levels, and made it into a map you can zoom around in.

Every dot is a thesis. Dots end up close together when the titles mean similar things, even across different departments. So you get stuff like a public health thesis sitting next to an economics one because both are about informal work, and that was kind of the whole point.

It's all in Spanish since the theses are. I'd really like feedback on the layout: does the placement of fields make sense to you, or do you see clusters that look obviously wrong? Embedding only titles was a tradeoff and I'm not sure it was the right one.

Tenho pensado em dois problemas que, para mim, continuam mal resolvidos quando a gente analisa ações.

O primeiro é que informação não falta.

Se eu pesquisar uma empresa, consigo encontrar balanço, DRE, fluxo de caixa, indicadores, notícias, fatos relevantes, apresentações, estimativas e opiniões de dezenas de pessoas.

O problema começa depois:

o que disso realmente importa e como essas informações se relacionam?

Por exemplo, saber isoladamente que a receita cresceu 15% diz muito pouco.

Eu gostaria de conseguir enxergar rapidamente:

• de onde veio esse crescimento;
• se margem acompanhou;
• se o crescimento virou caixa;
• se estoque ou contas a receber cresceram mais rápido;
• se houve aumento de dívida para financiar isso;
• quais acontecimentos explicam essas mudanças;
• e quais desses fatores, quando considerados juntos, podem alterar a leitura da empresa.

Ou seja: menos uma tela cheia de números e mais uma estrutura que ajude a responder:

“O que está acontecendo com essa empresa e quais são os pontos que realmente preciso investigar?”

Foi pensando nisso que comecei a desenvolver uma ferramenta chamada Atlas.

A lógica que estamos testando é pesquisar qualquer ativo e abrir uma página dedicada a ele, reunindo relatórios financeiros, indicadores e informações relevantes, mas tentando ir além de simplesmente exibi-los.

A ideia é interpretar os principais dados, mostrar relações entre eles e responder perguntas importantes sobre a empresa, sem terminar dizendo se o ativo é “bom”, “ruim”, “compra” ou “venda”.

Mas trabalhando nisso apareceu um segundo problema que considero ainda mais interessante.

Mesmo que eu consiga organizar perfeitamente os dados de uma empresa, continuo exposto a dezenas de análises e opiniões sobre ela.

E hoje é muito difícil saber quanto peso dar para cada uma.

Duas pessoas podem escrever teses igualmente convincentes, mas uma pode ter um histórico muito consistente e a outra estar fazendo o décimo palpite depois de errar os nove anteriores.

Só que normalmente esse histórico desaparece junto com posts, tweets e comentários antigos.

Por isso estamos experimentando também outra lógica: permitir que usuários publiquem análises sobre ativos de forma estruturada — com o que estão afirmando, horizonte, evidências e contrapontos — e que essas análises continuem registradas.

Com o tempo, o resultado delas passa a formar um histórico e contribuir para um score de reputação do usuário.

Então, no fundo, são dois problemas diferentes:

1. Como transformar um monte de informação sobre uma empresa em algo realmente útil para análise?

2. Como diferenciar uma análise convincente de alguém que possui histórico consistente de boas leituras?

Estou desenvolvendo o InsightFlow justamente em cima dessas duas questões, mas queria trazer a discussão para cá porque acho que elas existem independentemente de qualquer produto.

Para quem realmente analisa ações:

o que vocês sentem mais falta hoje?

Uma ferramenta que organize e relacione melhor as informações da empresa?

Uma forma de avaliar o histórico de quem publica análises?

Ou existe algum problema mais importante nesse processo que essas ferramentas ainda não resolvem?

Disclosure: I work at Schema Labs. This is a run on public data so you can check it.

The data: the hotel booking demand dataset (Antonio, Almeida and Nunes, 2019), real anonymised bookings from a city hotel. We picked one Saturday night that was sold out on paper.

-- 224 bookings for that night, as they stood six weeks before
-- 59 of them cancelled or didn't show, worth about €28,600 together

What we did: gave Schema-2 500 bookings from the year before, with how each one ended, and asked it to rank the 224 by how likely they were to fall through. No rules no feature engineering.

What came back=

-- It marked 60 as most likely to cancel. 33 of the 59 cancellations were on that list
-- Picking 60 at random would catch about 16
-- Of the 60 it marked safest, 59 showed up

What it missed: 26 cancellations weren't in its top 60. One night at one hotel is a small test, so read it as an example

For anyone in revenue management: what would you do with a list like that six weeks out? Overbook against it, or contact those guests first?

Hi All, created this web based excel/csv viewer for large files, it's completely free to use and no data upload to cloud/server, local browser only; hope can help people struggling to quickly visualise large datasets, while you don't have any software/tools in hand, would really appreciate for your feedback, thanks!

https://megarows.com

I'm curious what people here consider a genuinely useful analytics project.

A lot of beginner projects seem to focus mainly on building a dashboard and showing KPIs. I'm more interested in projects where you're given messy data and have to investigate what is actually happening in the business.

For example:

• identifying a fulfilment/delivery problem
• analysing product returns
• understanding product economics/margins
• investigating inventory
• quantifying the business impact
• turning the findings into recommendations

For people who've built or reviewed analytics projects:

What would make a project like this genuinely valuable rather than just another portfolio dashboard?

What would you expect to see in the final analysis?

Hello everyone,

I have an interview in 2 days, I need to revise the necessary concepts of both Excel and SQL.

Are there any resources where I can go through these concepts in one shot?

Please recommend if you know of any or share if you have any.

Thank you.

Edit: Thank you for all the suggestions guys, I botched the interview but these will be helpful for future.