My personal favorite is Richard McElreath's "Statistical Rethinking", which covers regression and multilevel modeling from a Bayesian approach but doesn't assume too much formal math background.
Yeah I'm familiar with the area (about to move from South Bend to Chicago). There's a lot of effort going into building a startup scene in South Bend, both around the Innovation and Ignition Park developments and at Notre Dame. At least a few of the companies that I'm familiar with (Vennli, Carextech) have gotten traction; the existing economy there is pretty manufacturing-heavy, especially around auto, appliances (Whirlpool) and medical devices (J&J has pretty big presence in Warsaw).
I might try and work through the area colleges (St. Mary's, Notre Dame, IU South Bend) if you're looking to recruit there.
Inter Alia, this article is another piece of evidence that a lot of pretty smart people are stuck in jobs that aren’t very complex. The accumulated cost of this to the rest of us is quite high...
Unfortunately mandatory parking minimums are written into the zoning codes of most American cities, leading to the surfeit of parking in lots of places. Undoing this will take no small amount of work.
It’s interesting to see the America's most prominent libertarian economist state that capital markets aren’t adequately funding risky business ventures...
I tip my cap to all the loyal users of MoviePass out there; I have long waited for the day when investors would be willing to subsidize my hobbies and you've been living in that world for months. Bravo, ladies and gentleman.
I have to wonder, though, why you'd want to go to the movies regularly these days, with all the dreck that Hollywood has been putting out in recent years. Maybe it's time for you guys to hit the $1.99 rentals on YouTube/Amazon/iTunes more often.
Pointless jobs abound in both the public and private sectors, but I think these articles resonate because so many white collar workers are largely occupied with projects that will be cancelled before they're finished or whose end product will eventually be ignored by the people who are intended to use it. Most companies just aren't managed very well...
You're welcome, America. Will anyone really miss Buffalo Wild Wings? Or Golf? I suspect we drink just as much beer, it's just more craft beer than Sam Adams or Corona.
The 'Millennials don't like breasts' angle is a new take. I'd figured that one was a constant.
The 'rise of passive investors' creates more opportunities for activist investors to discover overvalued/undervalued stocks. Index funds aren't affecting the prices of individual stocks. So long as the opportunity exists to make money buy buying/shorting stocks, someone will be buying/shorting stocks.
Moreover, executive compensation is still overwhelmingly set via earnings-per-share targets in a fairly transparent manner. If there's a conspiracy afoot to miss targets to benefit an industry sector, the authors haven't identified it.
I like the Matt Levine treatment of this question ('Are Index Funds Communist?'):
To those of you who hate R, consider investing 1/2 hour to learn the dplyr package. Dplyr is, in my view, Hadley Wickham's real masterpiece and is why I use R for most data analysis nowadays.
As for ggplot, the 'grammar of graphics' approach makes it intuitive to get started with but I often run into trouble with both the inheritance hierarchy and with getting graphics 'the last mile' to presentation-quality.
My favorite ggplot2 graphic? The London Cycle Hires Map:
These calculations seem fishy, for reasons stated above and others (e.g. why 20-year ROI? why exclude graduate degree holders?).
However, it is true that rising education costs are eating much of the ROI of attendance and lack of transparency has made it harder to see where that crossover point is.
But in a world in which the equity and college wage premiums are headed in the directions that they're headed, I would not give this advice to an intelligent 18-year-old.
Writing a full reply since I don't agree with much of the advice given.
I've worked around/in data science teams at a large BigCo and I think that you're far overestimating the bar here. There aren't enough people to who can write data pipeline code (SQL/Shell/etc.), much less implement and intelligently explain statistical/ML models. Also, the average decision maker here does not understand the difference between 'created model in Pandas' and 'created model with Amazon's ML API'.
The modal background of data scientists in industry is closer to 'Econ BA + knows Python' than 'Artificial Intelligence PhD'. Moreover, the former will still enjoy a remunerative career if (s)he's sufficiently savvy about identifying problems and showing off how they can be solved with technology.
There may be a point in time when companies can't get a return by throwing math-savvy programmers at a problem, but that will be long after you and I have passed from the scene.