Realistically there is no way to do news without a bias nowadays. A Trump supporter told me there was no war between Russia and Ukraine. I said “OK, conflict”. His reply was “mainstream media has you brainwashed”.

To cover Jan 6 do we have to say that maybe it was Trump supporters who peacefully went to the Capitol or maybe it was Antifa who stormed it - we have to treat all possible scenarios as equally likely?

As a hiring manager in the data science/ml world in healthcare, I generally think of degree programs in "data science", "machine learning", "artificial intelligence", "deep learning", etc as being less valuable than degree programs in the corresponding fields that aren't as buzz wordy. I tend to prefer candidates from backgrounds like computer science, math, applied math, statistics, or something domain specific (coming from healthcare) like epidemiology (or variation like computational epi) or bio- or biomedical informatics.

Those programs tend to show me that you're interested in and have done the more boring but foundational coursework that is often cut to make the sexy degree programs. That means that hopefully you won't be upset that 100% of your job isn't deep learning, and that you'll be better suited to pick the right tool for the job.

At one of my last jobs, there was a machine learning engineering team (all boys) and a data science team (all girls and gays) who had the same ML chops. The DS team ended up getting more models into production and more research published than the ML team because they had more "soft" skills to navigate the problems the org was facing. When someone in leadership would say "we're having issues booking appointments", the ML team would set off building some fancy deep learning model while the DS team would generate hypotheses with stakeholders, do some exploratory analysis, run a few prospective studies, and then use those results to inform some regression models that would end up in production. It wasn't as sexy as some deep learning model, but the leadership team wanted full interpretability of their model so deep learning was never going to be acceptable. I generally think of these kinds of skills being taught more the stats, applied math, or epi programs than in the designer ML programs. ymmv

I’m no mathematician, so I only took basic school math, but I hated every moment of it. Mostly because overwhelmingly there was never any context or justification for learning any of it. Why does this exist? What actual real world problems does it solve? How did folks come up with and it, prove it works and start using it? Crickets. Just learn this formula, then that. The first time I heard the ancients calculated the distance to and size of the moon with trigonometry I was floored. Oh ok so that’s the kind of cool shit they came up with it for. Now I’m listening.

data engineer here, offtopic, but am i the only guy tired of databricks shilling their tools as the end-all, be-all solutions for all things data engineering?

So I am not alone! There seem so few people who hold this view these days.