I'm also a physics grad student (experimental high energy) who is considering industry jobs in addition to postdocs. I've attended several career panels in the past few years. Most recently, a panel was held at Fermilab. One of the panelists started a blog, Science Jobs Headquarters, where you can read about that panel and get other good advice.

A few of my takeaways from the panel: 1) Python is really useful and everyone should learn it. (I need to work on taking this advice. I mostly develop in C++, and my Python is patchy.) 2) Some companies want to hire people with very specific skills and experience, but other companies are just looking for smart people who can learn on the job. The important point here is that few skills are absolutely essential to get a job in data science/private research/consulting/etc. Even if you're not a Python whiz, there are still people looking to hire you. 3) The website Glassdoor was recommended for investigating companies at which you might want to work.

You should act in a way that, if everyone acted that way, things would work out.

— Louis C.K.

Taken, answering all of the questions I was capable of answering. I will be very interested to see the results on some of the new questions. (The shifts on existing questions could also be interesting, but I don't expect much to change.)

This reminds me of the researcher's maxim:

A month in the laboratory can often save an hour in the library.

— Frank Westheimer

Note: The discrepancy in spelling ("ageing" vs. "aging") is in the original.

To indicate this more concisely, you can put [sic] after "Ageing" in the quote.

Where did you get that from? Did you read the primary, or is there some actually decent exposition of this somewhere?

Mouseover is javascript EDIT: or CSS and shows up when you hover your pointer over some trigger area. Alt text is plain HTML and shows up when the image (or whatever it is alt text for) doesn't load.

As a senior in high school, I had the option to take two different computer science courses.

Option 1: AP Computer Science A, taught at my high school. The teacher was one of my school's math teachers who had some programming experience. (My school had not actually offered a comp sci course since I started there, even though Intro to Java was on the books.)

Option 2: An independent study in computer science, taught at the local vocational high school. The teacher had a master's degree in computer science from Brown and had worked for Macromedia/Adobe. (She was also the daughter of my school district's Director of Technology, whom I knew as a student representative to the Technology Committee.)

On the surface, Option 1 looks better for college admission, since it's an AP course. There may also be some perceived bias against vocational schools. However, I chose Option 2. This proved to be the superior choice. I had already taught myself basic programming skills, and the independent nature of the course meant I was able to learn at my own pace and study different topics with a knowledgeable teacher.

When I started college, it turned out that the AP Comp Sci A test wasn't even worth any course credit. Actually, the Computer Science department did not require Computer Science I as a prerequisite for more advanced courses, assuming that if a student could pass Computer Science II, they didn't need to take the previous course. Choosing the better course allowed me to get a jump-start on learning more once I got to college. Although I did not end up completing my intended computer science minor due to too many course conflicts with my physics major, I still found it useful to have an advantage from my high school course. I continue to use the lessons I learned from my high school teacher (who excelled at teaching object-oriented programming and data structures) in my current software/programming-heavy research on the CMS experiment.

Full disclosure: the non-AP course did not contribute to my weighted GPA or class rank because I took it in the last semester of my senior year. The last semester was not counted since rankings had to be decided before the semester ended, both for reporting to colleges and for the purpose of valedictory and salutatory addresses during graduation.

Kevin’s school offers a molecular biology elective during second semester, which is not an honors or AP course. Kevin would like to take the elective during the second semester of his junior year, in addition to his other coursework, but he knows that doing so would lower his GPA, so he decides not to.

In Kevin’s story, the class ranking system was poorly designed: it rewarded some students for achieving less rather than for achieving more. The colleges that Kevin applied to were relying on a faulty measure of quality.

Taking a non-honors or AP course only harms one's GPA (in this ranking system) if it replaces an honors or AP course. There have to be enough honors or AP courses offered to fill a student's entire schedule in order for this to be the case.

Ranking systems which do not weight honors or AP courses can also encourage students to achieve less. This can even happen when honors courses of different difficulties end up with the same weighting.

I think the real lesson to draw from such examples is that creating a measure by taking information which exists in a multidimensional space and projecting it into a single dimension can lead to perverse incentives. (A similar idea is mentioned in another comment in this thread, but I thought it worth pointing out the general principle.)

So you might reason, "I'm doing martial arts for the exercise and self-defense benefits... but I could purchase both of those things for less time investment by jogging to work and carrying Mace." If you listened to your emotional reaction to that proposal, however, you might notice you still feel sad about giving up martial arts even if you were getting the same amount of exercise and self-defense benefits somehow else.

Which probably means you've got other reasons for doing martial arts that you haven't yet explicitly acknowledged -- for example, maybe you just think it's cool. If so, that's important, and deserves a place in your decisionmaking. Listening for those emotional cues that your explicit reasoning has missed something is a crucial step

This is a great example of how human value is complicated. Optimizing for stated or obvious values can miss unstated or subtler values. Before we can figure out how to get what we want, we have to know what we want. I'm glad CFAR is taking this into account.