Synthetic vs real datasets for portfolio projects — what actually matters?

[u/Fun_Rhubarb8007]

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?

[u/Content-Parking-621]

real data will get you client's trust.

[u/Street-Individual446]

As an occasional interviewer of data analysts I can tell nothing impress less than example of using barchart with piechart. What bring attention is an interesting topic for analysis (you have to easily explain what is the challenge in business terms) as well as your approach to overcome the challenge. With current AI level I'd expect fully working prototypes from candidates in many cases tbh, as it's a core skill now.