BUSINESS

To expand a bit on my comment on @KateGregory’s answer, the problem you face with using your own computer is “who owns what?”. How is a line drawn between things that you do on that computer for the company vs. for yourself? If you work on other projects off the clock, can the company make any claim to those? Does the computer have to comply with all corporate standards & policies, including antivirus, web filtering, remote scanning for unapproved software, etc.? Who owns the software you’re using? Who’s responsible for the licensing?

You are going to have to wait a couple weeks before you can make any kind of pitch to get a usable computer. Otherwise, you come across as the new guy who wants everything “just so”, and that will not earn you any points.

Among the most common job titles you’ll see:

Web Developer: A person whose primary responsibilities include the programming and development work involved in creating a working website. Though this is largely a technical role, a certain amount of business knowledge and communications skill is important to doing the job successfully. the average Web developer’s salary ranges from $36,000 to $80,000, depending where they live and how much experience they have. The median is about $54,000, but recruiters say demand for job candidates is pushing pay higher. Senior Web developers’ pay ranges from $52,000 to $107,000, says PayScale, and experience with technologies like C# and Java can result in even more money.

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.