r/data

If you could go back to the beginning of your Data Science journey, what would you learn first?

Would it be:

Python

SQL

Statistics

Machine Learning

Data Visualization

Git

Cloud platforms

Many beginners jump straight into AI without building strong fundamentals.

What skill saved you the most time later in your career?

Code for creating it is also there.

I've spent the last few months digging through annual reports, earnings call transcripts, investor presentations, and exchange announcements from Indian listed companies.

One thing became obvious...

Everything is technically "public," but almost none of it is actually usable.

Want to know every company talking about AI adoption?

Good luck.

Want every management commentary about data centers over the last 5 years?

You'll be opening hundreds of PDFs.

Want to compare CapEx guidance across an entire sector?

Hope you have an entire weekend free.

That got me thinking...

What if someone built a structured database instead of just storing documents?

Imagine being able to ask questions like:

"Show every company that mentioned data centers in the last 8 quarters."

"Which companies warned about margin pressure before their stock fell?"

"Find all management teams increasing CapEx while guiding higher earnings."

"Which pharma companies mentioned USFDA inspections this quarter?"

Not AI hallucinations.

Not another stock screener.

Just structured, searchable intelligence built from public company disclosures.

I'm genuinely curious...

Would this actually be useful to anyone?

If you're a:

Developer

Quant

Analyst

Wealth manager

Fintech founder

Researcher

Investor

Would you or your company pay for something like this?

Or is this one of those ideas that sounds amazing until you ask real people?

I'd love brutally honest feedback.

If you think it's useless, tell me why.

If you think it's valuable, I'd love to know:

What would you use it for?

Which data would be most valuable?

What would you expect to pay for something like this?

Hi everyone,

I'm working on a recognition-based community for people in data.

The idea is simple, we want to highlight the work data professionals do, feature their stories, and help them connect with others in the industry.

Would you be interested in joining something like this? If yes, I'll dm you the link!

I am building a Supply Chain Disruption Monitoring and Risk Analysis using Graph based Agentic AI. I need a supply chain network dataset in the semiconductor industry.

Currently my only option is to manually go through filings and earning calls to create a network big enough to propose my system. Creation of network is out of scope of my project and I'd appreciate it if I could get a dataset that would reduce this load.

I really do not know who to ask. I have been scouting google trends, and wanted to see how popular the game "Granny" is. The result was finding a sudden, huge spike in the term's popularity in late 2005 to early 2006 (roughly december 15th 2005 to january 18th 2006).

blue - \"Granny', red \"Granny porn\"

Here is what i was able to deduct myself:
-There exists a weekend effect, around saturadays and sundays
-There has been no significant cultural or political effect that could have caused this trend
- The spike was exactly january 1st 2006
-The trend was international in both english speaking and non english speaking countries
-The rise of "Granny" roughly correlates with the rise of "Granny porn"
-There was also a sudden rise in the term "porn", which had happened in November 2005
-a similar spike does not occur for the search "Grandma" or "Grandmother"
-The internet was far more often young males than other demograhic, which could potentially support the porn hypothesis

Please help me I am going insane

Final year CS student here, targeting data science and analytics roles for campus placements.

Been struggling with this question while building my portfolio: does it matter whether your project uses real messy data vs synthetic/clean data?

Real datasets from Kaggle feel either too cleaned already or the same recycled projects everyone does. But synthetic data feels hollow because the hard part — cleaning, feature engineering, deriving meaningful columns from raw data — is already done for you. You're basically just visualizing something someone else already solved.

Specifically for BI/dashboard projects — if you use synthetic data, the dashboard looks clean and professional but there's no real discovery or insight because the data was designed to be dashboarded. Nothing surprising comes out of it.

Also practically — if an interviewer asks "where did you get this dataset?" what's the right answer? Saying "I generated it synthetically" feels like admitting you took the easy route. But lying about the source is obviously wrong. Is there a way to frame synthetic data usage that doesn't sound like you avoided the hard part?

At the same time I've heard people say interviewers care more about what you built on top of the data than where it came from. But isn't handling bad data literally the core skill in DS?

For people who've interviewed at analytics/DS companies or done hiring — how much does data source actually matter? Is a well-executed project on synthetic data better than a mediocre project on real messy data? Or does using synthetic data automatically signal you avoided the hard part?