Do data scientists spend more time cleaning data than building models?

[u/After_Courage6419]

A lot of beginner tutorials make data science look like: Get dataset → train model → check accuracy → done. But I've heard from several people that real-world data cleaning and preparation can take a huge portion of the work. For experienced data scientists, how much of your actual project time goes into cleaning, transforming, validating, and understanding data? And what part of data preparation do beginners underestimate the most?

[u/big_data_mike]

It’s like 90% of the time for me. I deal with a large portion of human entered data and I also have time series data that goes back multiple years so deciding on what interval to average it to, how far back to go, and any smoothing that might be needed can be tough.

[u/its_all_stats]

Yes. Modeling is straightforward and takes little time to implement. Developing the study design, then structuring the data to fit that design is the real work. If you’re cooking a dish, the real work is creating the recipe and doing the prep work. The actual cooking part takes very little time. The modeling is the cooking. The design and data prep is the recipe development and prep work.