From the course: Data Science Foundations: Fundamentals
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Alternatives to programming: Low-code, no-code, and AutoML
From the course: Data Science Foundations: Fundamentals
Alternatives to programming: Low-code, no-code, and AutoML
- [Instructor] Working with data can be challenging under the best of circumstances, and there's a lot of thankless work that goes into it. For example, there's the common saying that 80% of the time on any data project is spent getting the data prepared. And that certainly matches with my own experience. I mean, in terms of data preparation, that includes things like converting categorical features and dealing with missing data and maybe rescaling data or feature engineering extraction and selection to name some of the tasks. And then there's the issue of hyperparameters in your model. Now, for linear regression, that might just be the alpha rate or the false positive rate in hypothesis testing. For K nearest neighbors, it's the number of neighbors to consider. But for algorithms like deep learning, there can be many, many more. Like the number of hidden layers, the number of units per layer, the learning rate, the dropout rate, the number of epics and so on. You have a lot of work…
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