Data quality is another real-world problem. There can be typos, deliberate biases, omissions that need to be guesstimated, sudden structural changes (e.g. a sto

Data quality is another real-world problem. There can be typos, deliberate biases, omissions that need to be guesstimated, sudden structural changes (e.g. a stock split event or a change in reporting cycles) etc. There's also heterogeneous data sources to aggregate and consolidate, each with their own way of measuring things. e.g. You can see how different states and countries are tracking Covid related stats, they all have their own metrics and interpretations. Some even change the way they report overnight. Companies will similarly report their data in different ways. Data cleansing is its own science and art for this reason, quite separately from developing any algos on it. It's a practical problem that's easy to overlook when you're just looking at ML transformations from input to output data sets.