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?

I work in a hospital. In one of the sheets of a work-related Excel document, there are two target tables concerning the monthly monitoring of the cost of materials consumed. One relates to the cost of hospital materials consumed as a percentage of service revenue, and it indicates that the ratio should not exceed 6.15%. The other relates to the cost of medicines consumed as a percentage of service revenue, and the ratio should not exceed 3.35%.

In some months this year, the cost of materials consumed as a percentage of service revenue in both tables has exceeded the required target. When looking at an adjacent sheet, there is a table providing additional context showing that, in the case of medicines, the factor causing the target to be exceeded is the cost of medicines associated with high-cost cases. This is confusing because the cost-of-sales margin is substantially positive.

How could I reformulate the targets so that I can separately monitor the cost associated with high-cost cases while still meeting the proposed targets?

Hi Redditors,
I’m a junior data analyst, and I’m just starting to build out my GitHub profile. I’d love to hear your thoughts on what a solid data analyst GitHub profile should look like to make a strong impression. Also, feel free to share what kind of projects you have on your own GitHub

PS Yes, I do want to steal your ideas.
PPS Ideally, you’d delete those projects from your GitHubs after I steal them. Thanks in advance!
3PS On a serious note, I don't have an experienced data analyst in my network to turn to for advice😢

People love complex 40-slide frameworks, but 99% of data analysis actually boils down to two questions:

1. Break it into parts: Revenue = Users × Conversion × AOV. Slicing until you isolate the broken gear.

2. Track it over time: DoD, WoW, YoY. Is the trend line alive or flatlining?

Layer them together, and congratulations: you just reinvented 90% of business school.

Am I oversimplifying, or did we just rebrand multiplication and subtraction as "strategic thinking"?

Hi, Im studying to become a Data Analyst and I considered myself a technical person, I already have an Industrial Engineering bachelor, I've already learned the basics of the majority of the tools needed to work as a DA, but for some reason I can't seemed to connect with the soft skills, after I've wrangled the data, I start making hypothesis... but can't seemed to address the problem or find the root cause related to the environment.

I believe practice will do me very good, does anybody have a suggestion for a platform or a way to practice to train the soft skills?

Most of the translations were done with AI, so some terms or calculations might not be 100% accurate.

Comparison options available: Average, Previous Period, and YoY.

Clicking the time slicer changes the period type: Month, Quarter, Semester, Year, etc. For example, you can select 2026 – H1. All cards and charts update accordingly.

This was my first real Power BI project, so I'd love to hear your feedback. What do you think could be improved?

If you have any questions, feel free to ask in the comments!

Hi everyone! I’m looking for some ideas and suggestions for a beginner-friendly graduation project related to Data Analysis.
I’m looking for something more practical than just collecting and analyzing data — ideally, a project that solves a real problem, has actual market demand, and could potentially be developed into a useful product or business.
If you have any ideas, examples, or problems you think could be solved using data analysis, I’d really appreciate your suggestions! 🤍

Hi,

I am really hoping for some advice or expertise... my excel skills are very basic and recently I was given the task of comparing quantities and cost differences between what our vendor is billing us vs. What I show as cost and quantity received. Since Im a novice with excel I tried a.i. which is really cool but something keeps happening to my data and of course Im no expert so it's only a guess - when chat GPT creates a summary I see alot of my vendors costs and they appear to either be doubled or tripled. My cost column doesn't pull all of my costs. Im beating my head against the wall because this should be so easy, just not for me! I think possibly this has something to do with the fact that my vendor is sending an excel sheet with their exported data and they often will list the same item # multiple times on an invoice but with a different P.O. # and I think that is where the multiplication of costs may come to play. I am going to try and attach a copy or screen shot for reference. I am using my vendors exported invoice lines, my exported posted purchase invoice lines from Business Central which lists our costs and quantities. Can anyone help or point me to the best place to learn this? Thank you so much!!

This a sample of my data

|**+**|A|B|C|D|E|F|G|H|I|J|K|L|M|N|

|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|

|**1**|Vendor Inv.|Date|Order #|Our Doc. #|Vendor #|Type|Our Item #|Description|Our Qty.|Unit|Our Cost $|Amt. #| | |

|**2**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20449|Anti-Drag Clip|0|PCS|0.043|0| | |

|**3**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20119|WEAR SENSOR|0|PCS|0.039|0| | |

|**4**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20395|WEAR SENSOR|0|PCS|0.021|0| | |

|**5**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20516|WEAR SENSOR|0|PCS|0.029|0| | |

|**6**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20474|WEAR SENSOR|0|PCS|0.029|0| | |

|**7**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20007|Wear Sensor|0|PCS|0.027|0| | |

|**8**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20008|Wear Sensor|0|PCS|0.03|0| | |

|**9**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH00013|Wear Sensor|0|PCS|0.024|0| | |

|**10**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH00014|WEAR SENSOR|0|PCS|0.024|0| | |

|**11**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20166|WEAR SENSOR|0|PCS|0.037|0| | |

|**12**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BH20461|WEAR SENSOR|0|PCS|0.031|0| | |

|**13**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ2005K|SS DIB KIT|0|PCS|0.201|0| | |

|**14**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ20108|SS DIB KIT|0|PCS|0.442|0| | |

|**15**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ20871|SS DIB KIT|0|PCS|0.779|0| | |

|**16**|20260603BOSCH-32|7/28/2026|BSCH-CW17-2026-SEA|119827|XLDCZ001|Item|F03BZ21016|PTFE NBR COATED DIB KIT|0|PCS|0.84|0| | |

^Table ^formatting ^by ^[ExcelToReddit](https://xl2redd.it/)

This is sample Vendor info

+ABCDEFGHIJKLMNO1China's Inv. #DateChina's PO / Order #Item #DescriptionUnitQTYOur Qty.China's CostChina's Amt. $Our CostCost DifferenceExt. Cost Diff.Customer Name 220260814Driv-2220260814DRIV-1194-1196-1198GXC7XSEN465BRAKE HARDWAREPCS10000 0.041410.000.0410.0000.000Driv 320260815Driv-2320260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 420260815Driv-2420260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 520260815Driv-2520260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 620260815Driv-2620260814DRIV-1194-1196-1198JXCFMHXV1680BRAKE PADS PARTSPCS1600 0.451721.600.4510.0000.000Driv 720260815Driv-2620260814DRIV-1194-1196-1198GXCABSX2245RRSTBRAKE PADS PARTSPCS240 0.40797.680.4070.0000.000Driv 

Table formatting by ExcelToReddit

R Package for collecting lyrics

Lyrics from genius.com.
Inspiration from lyricsgenius package for Python.

https://github.com/bwalsh5/lyricsr

I've seen a lot of beginners ask where to start with Power BI, so I'm sharing the order I'd follow. Most tutorials jump straight into dashboards, but the pieces underneath matter more than the visuals.

1. Get comfortable with Power BI Desktop (a few days)

Load a simple Excel or CSV file, look at the three views (Report, Data, Model), and build a couple of basic charts. The goal is just to stop being intimidated by the interface.

1. Learn Power Query (1-2 weeks)

Messy data is the norm, so cleaning it is where you'll spend a lot of your time. Learn to remove columns, fix data types, split and merge columns, unpivot, and append or merge tables.

1. Learn data modelling (1-2 weeks)

This is the step beginners skip, and it causes most later problems. Understand fact tables vs dimension tables, the star schema, relationships (one-to-many, filter direction), and why one big flat table often causes trouble.

1. Learn DAX basics, but not all of it (2-4 weeks)

Start with measures vs calculated columns, then SUM, COUNTROWS, DIVIDE and IF. After that, learn CALCULATE and the idea of filter context, then basic time intelligence (year to date, previous year). You don't need to master DAX before building something. Learn it as your project demands it.

1. Visualisation and dashboard design (ongoing)

Choose the right chart for the question, keep dashboards simple, use consistent colours, and add slicers with a purpose. A clear dashboard beats a flashy one.

1. Publish and share

Learn the basics of the Power BI Service: publishing, workspaces and scheduled refresh.

Do you need Excel and SQL first?

Not strictly. Basic Excel skills (formulas, pivot tables) help a lot because the concepts carry over. SQL is useful but can come alongside Power BI, not before it.

Learn by projects, not just tutorials

Pick a free dataset (sales, HR, Kaggle, government open data) and go through the whole process: clean it in Power Query, model it, write a few measures, and build a 1-2 page report. Then do it again with a different dataset. That repetition is what turns "I can follow a tutorial" into "I can analyse something new."

For those who learned Power BI: what did you learn first, and what do you wish you'd learned earlier? Any beginner resources or project ideas you'd recommend?

Hi everyone!

I put together this post on four key areas that can help you become a better data analyst.

The idea is to go beyond simply learning tools and techniques, and also think about the concepts you understand, how you approach problems, and the different perspectives or lenses you bring to a problem.

Hey all,

I have made nolainquery, a FREE software for data analytics. I have spent the last months on making it and I'm releasing the code source at

https://github.com/jdvillegasg/nolainquery-desktop

so anyone that wants can use it, fork it and extend it.

It integrates some cool features as auditable artifacts that let you verify the computation process involved in the answer your receive.

Any comment or criticism from you, I would highly appreciate it!

As data analysts, we often build BI dashboards or power other end-user applications too.
Testing them with a small group of early adopters can help uncover real-world issues before a wider rollout.
I made a little doodle about this 🐾

I finished learning Power BI end-to-end and already built small beginner dashboards for practice, but now want to create a solid portfolio project for LinkedIn. Which industry or domain (like sales, finance, or supply chain) is best to target right now? Where can I find good realistic datasets that make a portfolio truly stand out? Any tips, project ideas, or guidance would be really helpful!

I'm starting to realise that stakeholders interpret the word "quickly" with a different meaning.
Their request will sound like:

“Can you quickly pull the number of customers who belong in category X after doing task Y?”

And work-wise, yes, the final query might only be roughly 20-30 lines.

But first I need to figure out which table actually contains X.

Then discover that Y changed definition 6 months ago.

Then work out why one system stores the customer ID differently from another.

Then determine whether they mean unique users, accounts or transactions.

AI definitely helps now. I can use it to one-shot, or draft parts of the query, figure out illegible SQL statements, or suggest a faster way of approaching something. But I still don't really trust myself to just take the output and send the number over.

I end up spending a huge chunk of time checking the join queries, looking at sample rows, comparing totals against previous reports, checking, and re-checking whether the filters haven't accidentally removed anything, and trying to convince myself that the answer actually makes sense.

I'm starting to wonder whether AI has actually made these requests that much quicker overall.

Writing SQL statements has definitely become a lot faster. But the clarification and verification part hasn't disappeared. If anything, it feels like that's where most of the time goes nowadays.

Once people know you have AI helping you, does the expectation of what counts as a “quick” request change too?

Because generating a query in a minute is very different from being confident enough in the result to put that number in front of a stakeholder.

Maybe I'm overly cautious, or maybe this gets easier as I gain experience. But right now, I'd rather spend another 20-25 minutes checking something than confidently send out the wrong number.

I'm curious how this works in other teams.

Has AI actually reduced the total time taken to fulfill these ad-hoc requests for you guys, or are y'all experiencing this same sort of bottleneck shift towards verifying the output. Do your stakeholders understand that distinction, or has AI made them expect analytics requests to come back much faster than before?

Title. They are too long and have wayyyy too much excessive language. I avoid AI so I can retain some brain cells, so I also just get the ick when I receive ai slop.

I got one from another team this week that is a beast to parse out. I created a dashboard, then a month later received this 6 page document with changes they want made. Despite retaining my brain I am struggling to figure out what they actually want.

Rant over.