It is just shifting away from being the only major server-side scripting language to one of many, that is all.

It is true that it became out of favor for high volume sites and large-scale commercial infrastructures lately but it still remains really popular for small to mederate sized applications.

If Facebook was about to be developed today, probably it would not be developed in PHP, but that does not make the language irrelevant at all.

Fun

All of the above doesn’t leave much time for fun. To keep my sanity, I squeeze every bit of free time out of life to ensure that I’m not missing out on opportunities. Of course spending time with my family is fun, but for the sake of separation I’ll be excluding that time from this section (I feel I did the Family section justice). As part of my free time at home I enjoy building side projects. I love creating beautiful web applications that could benefit the lives of others. My passion is creating things for others through programming. I love the feeling I get when someone enjoys something I’ve made. I can’t get enough of it. I’ve started hundreds of side projects, “finished” a few (is a project ever really finished?) and am working on a promising one right now. I love it and can’t get enough, but I pace myself so that I don’t squander precious time with my family.

The biggest piece of advice I could give is to take a course in microeconometrics/labour econometrics as a part of your course. If your course coordinator won’t let you, beg. If they still won’t let you, then go off-line for a week or two and properly digest Mostly Harmless Econometrics (or if your stats isn’t too good yet, Mastering Metrics). If you want to go and work in health analytics, then replace what I just wrote with the equivalent for research design.

Why learn microemet? Basically, many of the big questions in business are of the form “what will happen if we do x”. Predictive models that aren’t informed by causal reasoning do *terribly* at this question–they answer the question “what do we see happening to y when we see x”. Inferring what will happen to y when you fiddle with x is a difficult task when all your data come from a world in which you did not fiddle with x. Too often we come across people with great technical chops who aren’t even aware they’re making mistakes when answering these questions. Don’t be one of these people.

The second biggest piece of advice would be to not become too enamoured by the sexy end of data science (especially predictive algorithms), but *do spend the time learning this stuff in depth*. Often the simple stuff done well is far more useful to real-world decisionmaking.

Third: read very widely.