Potemkin Data Science (2020)
One of the points Andrew Ng repeatedly makes, especially recently, is that we do not focus a lot on the data.
ML is a field of models, not data. That&#x27;s fine, it&#x27;s an academic discipline. However, a data scientist should probably focus on something else.<p>Nevertheless, we currently want to be ML people, more or less. 
It&#x27;s sexy to download __some__ dataset and develop a new model architecture.
It&#x27;s not really sexy to collect good data - not just any data, but good data.<p>Now, coming from academia, I think this is just a normal thing. It happens in every discipline in some way.
In academic fields that are driven by observational data, data collection is usually a difficult and tedious task. Developing theories, methods and, of course, new results is much more interesting. And rewarding!<p>Look at economics and Piketty&#x27;s work: His main contribution was to collect data on wealth. Nobody really cares about his models much. However, for some reason, that data simply did not exist before. And it&#x27;s now valuable for hundreds of different projects.<p>As a data scientist, one needs to think about data, first and foremost. One needs to spend most time on collection, on measurement errors, on selection effects, on identification, censoring and all that stuff that nobody likes to think about.<p>Fitting the next best function approximator to the data is the most fun, but probably not the most valuable thing a data scientist can do.<p>From that standpoint, I think data scientist is a proper job that is probably needed in every company in the future.
However, no coursera course on the latest ML package will make you a valuable data scientists.<p>I think data scientists who are expert statisticians, survey and experiment designers, causal inference gurus and combine that with proper CS techniques will be valuable.