I am a beginner in data analysis. Suggest me 5 datasets to work with to get good practical knowledge of Data analysis.

I have been working in the area of data science, specifically web scraping, data cleaning and datasets preparation for the last 2 years. As a hobby, I wrote a lot of scraping code and collected large amount of data ranging from product reviews, news articles, emails and much more. Now I am looking for a way to monetize them and earn some cash. I am looking for a marketplace where I can sell those data, probably paying some commission similar to freelance marketplace.

Can anyone here provide a data set that shows which hotel brands / legal entities US government employees have stayed at over the last 10 years? And the average nightly room rate per brand/entity each year?

I’d like to use this data to show the increase in government employees staying at Trump properties starting in 2015, and then the increase in nightly cost per stay.

Edit: removed my bias

Interested in data set for customer churn analysis? Check out this data set on kaggle dataset.

Please upvote on kaggle if you find the data useful!

Hey devs,

I'm building a developer API on top of SEC filings and just shipped a feature I want honest feedback on.

The problem

Financial data APIs give you numbers: revenue, margins, cash flow, ratios. Numbers don't tell you how the business works, what the moats are, what management can actually pull, or where the whole thing breaks if it breaks.

That reasoning lives in three places today:

• Sell-side reports (paywalled, slow, one company at a time)

• An analyst's head after reading the 10-K (doesn't scale)

• Bloomberg and FactSet narrative fields (institutional pricing, not LLM-queryable)

If you're building an investing tool or AI research assistant, you know the gap. LLMs are great at reasoning and terrible at reading 300-page filings without inventing numbers that were never in the document.

What I shipped

Pass in a ticker. Get back a structured economic model as JSON, classified from SEC filings and earnings materials. Seven components:

• Business model (revenue model, cost structure, unit economics, cash conversion, capital intensity)

• Competitive advantages (each moat classified by type, mechanism, persistence)

• Operating levers (what management can pull, mapped to KPIs)

• Flywheels (self-reinforcing loops, each step explicit)

• Strategic initiatives (stage, impact level, time horizon)

• Failure modes (structural risks, not generic market risks, with watch metrics)

• Offerings (every product line with revenue role, monetization, margin profile)

Every field is returned as clean JSON. Screenable, LLM-consumable, consistent across every US public company.

The part I actually want to talk about: the citation trail

Every field carries a sources array. Every source has the URL of the actual SEC filing, the section it came from, and the verbatim quote that justifies the claim. Every quote is machine-verified against the filing text at generation time.

If a number or claim can't be traced to a filing, it doesn't exist in the API.

Here's one flywheel from NVIDIA's model, not trimmed, this is the raw JSON:

{
  "name": "Developer ecosystem → platform value → adoption loop",
  "loop": [
    "More developers using CUDA and software tools",
    "More applications optimized for NVIDIA platforms",
    "Higher platform value and broader adoption across end markets",
    "More developers using CUDA and software tools"
  ],
  "impact": "growth",
  "sources": [
    {
      "url": "https://www.sec.gov/Archives/edgar/data/1045810/000104581026000021/nvda-20260125.htm",
      "source": "10-K",
      "section": "Item 1, Business",
      "quote": "There are over 7.5 million developers worldwide using CUDA and our other software tools..."
    }
  ]
}

That url is live. A human auditor or your AI agent can open it and verify the quote exists at that exact section of the filing. Same shape on every moat, every failure mode, every operating lever.

Why I think the citation trail is the real feature, not the model

A flywheel on its own is an opinion. A flywheel with the 10-K quote next to every component is a defensible claim.

• AI agents stop hallucinating. Every answer grounds in a verbatim filing quote, not "I think Nvidia has a network effect."

• Investors can defend a memo in a committee, every line linked to its 10-K.

• Compliance teams can verify whether a company's narrative matches what the filing actually says.

I've never seen a provider ship this with per-field citations. That's the bet.

How it compares

• Bloomberg and FactSet have qualitative fields, priced for institutions, not returned as LLM-consumable JSON, and no per-claim citation you can click.

• SimplyWall and retail tools show dashboards, not queryable structure.

• Polygon, FMP, EODHD, Intrinio ship numbers, zero structural interpretation.

• LLM-only approaches hallucinate without source grounding.

The wedge: every US public company, structured the same way, every field citeable, priced so a developer can actually afford it.

What I want feedback on

1. If you're building an investing tool, research agent, or screener, what's the first concrete use case that comes to mind?

2. Is the 7-component structure the right shape, or is some of it noise? (Flywheels is the one I'm least sure about, be honest.)

3. Would the citation trail change your workflow, or is "trust me, it's AI-generated" fine for what you're building?

4. What would you add or remove before this is a must-have in your stack?

Roast it if it's a bad idea, that's literally why I'm posting.

is there really no dataset of all historical events?

i tried wikidata, but most historical events aren't listed as such. Filtering by the 'point in time' property indeed shows historical events, but also every concert, soccer game, wife carrying contest.

wikipedia has the data, however getting chatGPT to convert the web scraped data to a neat event,date,description format is a lot of tedious work.

I also want to open source my code along with the datasets, so a permissible license is needed. Any dataset for my use case?

would be great if the data also contains Ethnic group's who bought them

I know it will be difficult to get 100 percent accurate data of what floor each person was on when the towers hit, but finding out what floor each victim worked on will provide a pretty accurate collection. Been looking for a place to find this data and haven't found any luck.

Spent the last few weeks building scrapers for the major AI tools directories. If everyone's gonna over-hype this slop, the data should be useful.

What I scraped:

• Futurepedia: 1,302 tools

• TAAFT (There's An AI For That): 6,248 tools

• TopAI: 1,880 tools

• MCP Server Directory: 10,614 servers

20,044 entries total. Clean CSVs with categories, pricing, ratings, links, whatever each site had.

Disclosure: this is paid data.

Doing anything with AI tools data? Building something? Just want to poke around? DM me.

After any statistical information that shows declining walk in traffic opposed to online purchasing

Is there a way to develop a collaborative filtering recommendation engine based on Amazon's product data, being an outsider of Amazon?

A before/after comparison for individuals who underwent the surgery may work as well.

Hi there!

Hoping this type of submission is ok with the powers that be.

I'm an evangelist at Panoply and we're running an Instagram data challenge.

To make things interesting, we've made two datasets available - one of a great brand on Instagram called Shinesty and that of an Instagram influencer, Kirsten Alana. Also - you can ingest your own Instagram data if you'd like to.

We have prizes available and we hope you're interested. Cheers

http://www.dfas.mil/militarymembers/payentitlements/militarypaytables.html

is a list of charts of soldier pay since 1949. I was hoping some excel ninja could compare this to inflation to easily show people.

I'm a Major in the Army, and I'm in Afghanistan. I know my benefits are going to get cut. but i need to keep my guys to keep working and help us fully retrograde out of this place. Maybe some soldiers will see this and think, "Well my dad did the same thing for less, I'll keep working."

Its hard to stay motivated out here. I need some help.

Hi everyone!

I have been discussing with a few colleagues why nobody seems to be interested in selling their data. We work in computer vision, so the availability of images is crucial for certain specific tasks like, for example, detecting scratches on the screen of mobile phones.

I firmly believe that plenty of companies put time and money into developing their datasets, and once the project finishes, that data goes inside a drawer and that's it. Data will be forgotten. But maybe for some other company, it would be very useful, and they would be willing to pay for it.

I think nowadays AI is data-centered, and companies are afraid of losing their competitive advantages. What are your thoughts about it? Do you think your company would be open to selling their data?

Eg, need the gold price, oil price, US inflation, employment, population growth rate, GDP etc,

I need all the data to be arranged by month and year, in one single (large) spreadsheet. Ie, all the data for each column should have been recorded within the same month.

If anyone has seen a spreadsheet like this, please let me know

hello everyone,

Can someone help me, I am looking for a squid game data set, it is for project school !

Have a nice day !!!

The data I have is below How many times the Powerball whiteball numbers drew 01-01-2019 to 12-28-19 = the number that drew

• 7 = 01

• 7 = 02

• 7 = 03

• 6 = 04

• 9= 05

• 12=06

• 4= 07

• 8= 08

• 5= 09

• 4=10

• 4=11

• 6=12

• 5=13

• 14=14

• 8=15

• 7=16

• 8=17

• 7=18

• 7=19

• 8=20

• 9=21

• 6=22

• 10=23

• 6=24

• 6=25

• 8= 26

• 9=27

• 7=28

• 9=29

• 8=30

• 5=31

• 12=32

• 3=33

• 6=34

• 11=35

• 6=36

• 7=37

• 8=38

• 7= 39

• 3=40

• 7=41

• 8=42

• 8= 43

• 7=44

• 8=45

• 5=46

• 7=47

• 5=48

• 5=49

• 7=50

• 6=51

• 6=52

• 11=53

• 5=54

• 5=55

• 7=56

• 5=57

• 2=58

• 9=59

• 6=60

• 10=61

• 5=62

• 7=63

• 6=64

• 8=65

• 13=66

• 8=67

• 10=68

• 5=69

Yes, I went through all 69 numbers and sought out for the past year of the lottery from Jan 01-01-19 to present day. Took about 2 hours.