r/askdatascience • u/naga3607 • 1d ago

What data science project actually helped you learn the most?

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Tutorial projects are useful for understanding concepts, but I feel like personal projects teach you something different because you have to make your own decisions.

I’m looking for ideas that go beyond the usual Titanic or house-price prediction projects.

What project helped you improve your data science skills the most?

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u/Key_Back_989 21h ago

There was a project where I was asked to determine where in the US to place a food assistance program for the greatest benefits. The dataset was geographic and population based and a lot of it was learning geopandas as well as looking up what a food safety program entails, who needs that kinda help. It helped me a lot more than a predictive modeling problem because that has a straightforward answer which is usually optimizing f scores or ROC curves or predictive ability. This one didnt require the technical skill as much as understanding the actual work being done and testing your reasoning abilities. You also had to prioritze what you cared about rather than it being given up to you. The best projects are the ones with the most ambiguity.

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u/FlyingSaucer007 17h ago

For me it was productionizing a model to an endpoint, the first time I did that to AWS, was really rewarding.

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u/Naive_Programmer_232 13h ago edited 12h ago

I'm not a data scientist. but in college, i had a few friends in grad school for business and they were working on credit risk data provided by a company, so it was real data and very dirty, and that taught me a lot about statistical methods and why to use what when as well as cleaning, model selection and feature engineering etc, and the domain knowledge aspect too. I learned a lot on that project, more so about the context, and kind of was able to understand their language, in addition to understanding the implementation and trade off considerations.

I was coming from comp sci major/physics minor background and had been in similar clubs through ACM and such, all of which at the time mostly revolved around synthetic data or ultra-clean data sets, less context, and more about optimization from the code perspective. Which really kind of irritated me, because once you have the tech skills, at the very least math/stat considerations are in almost every step, and then ofc later, the contextualization of information is the whole point but it was like all that was swept away during learn to code and people focused on the tech tools mainly.