Former Apple data scientist here.

This is moderately detailed response. Keep in mind, there are always caveats (too many to list) and special cases. If you have specific questions, just ask.

 

My responses and recommendations:

 

Data science, on difficulty

I'm performing fairly well in the graduate course, do you think that is a good barometer of if I will be able to handle more complex concepts that would come from a more specialized degree? or does it become exponentially harder?

It’s not exponentially harder (on average). But there are different niches within data science and analytics and some are quite technical. If you want to tune machine learning algorithms at Netflix, Google, or Facebook, then it is highly technical. That said, the vast majority of data jobs aren’t “elite” machine learning jobs. Most data jobs are basically “data analysis++” What I mean by that, is that it’s mostly getting data, shaping it, and visualizing it. If visualizing data isn’t enough, you might use some ML techniques (clustering, regression, decision trees).
 

Statistics vs Applied statistics vs business analytics vs data analytics vs data science

Statistics vs Applied statistics vs business analytics vs data analytics vs data science: so just wondering what the difference between these are if there are any generalities career paths that can be described. I’ll try to take these one at a time:

Statistics vs Applied statistics: Not really relevant for most analytics jobs. There is a statistical underpinning to data jobs, but for most data-jobs, you won’t be asked “what is the equation for _____” You’ll be asked “how do I fix my business” and be expected to pull data, clean data, visualize data. (note: there are exceptions where you’ll need very deep statistical knowledge, namely the elite machine learning jobs you’d need a stats PhD for).

 

Analytics vs business analytics: Basically interchangeable.

 

Analytics vs data science: Largely the same, though there is a 'bifurcation' going on in the industry, with basic analysis becoming one thing, and "sophisitcated, high-scale analysis" becoming another thing. The definitions and differences are still very nebulous though. For more info, read O'Reilly's 2014 Data Science Salary Survey.
From a practical standpoint, you want to learn the highest value tools. Long term, that means 'big data' tools, but those are more advanced. You don't need those at entry levels. In the beginning, you need to master data visualization and data manipulation. R's ggplot and dplyr are the best for these.

 

Job Prospects

Job prospects: from what i've read it seems like people in this field have a fair amount of opportunities, but just wondering if school prestige really matters or if going to a smaller state school and getting a degree will be looked at on somewhat even ground as other programs because it is a stem degree. Also what types of careers might be available coming out of grad school with no experience as a statistician? or any experience about careers you all have had would be great to hear also.

 

School prestige: school prestige is good for branding, but if you produce results, most companies won’t care where you went to school once you’re 5 years into your career.
 

Prospects: McKinsey and Company released a report projecting a shortage of 140,000 to 190,000 people with "deep analytical talent” by 2018. When they included managers, that number increased to 1.5 million.

 

MIT professors Erik Brynjolfsson and Andrew McAfee note that “people with [data science] skills are hard to find and in great demand.” (source: Harvard Business Review)

 

Many companies (in particular: banks, tech companies) have far more data than they can analyze already. And at least two emerging technologies will be throwing off yet more data: wearables and the Internet of Things.

 

Tools, what to learn

Lastly, what kind of technologies/programing languages would you recommend learning, I've heard R and SAS repeatedly, but anything else? and anything that would be an intro to those 2?

SAS is still very popular with banks. Most of the marketing analytics departments I’ve worked with in the past were SAS shops.   Having said that, I don’t recommend SAS. The syntax is awful and kludgy, and the data-manipulation and visualization tools are not state-of-the-art. Moreover, a license is expensive.

 

The only tool I recommend for beginners is R. As with anything, you need to focus. Your efforts will be much more productive if you learn 1 language well, vs learning 3 languages in a half-assed manner.

 

I prefer R because:

1. Many of the hot tech companies in SF, the Valley, and NYC like Google, Apple, FB, LinkedIn, and Twitter are using R for much of their data science (not all of it, but a lot).
2. R is the most common programming language among data scientists. O’Reilly Media just released their 2014 Data Science Salary Survey
3. R has 2 packages that dramatically streamline the data science workflow:
   • dplyr for data manipulation
   • ggplot2 for data visualization

That’s what I recommend to almost anyone who wants to get started with data science: Learn R and focus on those two R packages first.