Is Data Science Your Next Career?

I, for one, am glad that IEEE is making an effort to get people excited about a formal education in data science. My education was a disjoint combination of CS and Statistics (my degree is formally in statistics), with no union between the two except what I made of it. In neither CS nor Statistics did my education formally cover problems associated with having too much data to fit in memory or store on one hard disk.

My biggest issue with the teams of statisticians I've worked with before is that they lack a basic understanding of computer science. My biggest complaint dealing with the software developers on analytics projects is they don't understand statistics. I heard a great quote for which I don't remember the source (I paraphrase here): "A data scientist is someone who knows more computer science than a statistician, and more statistics than a computer scientist." The nature of the analytics world right now suggests that this type of specialty is sorely needed in many places.

I like the new Data Science term, because it suggests people might be more open to paying money for math. Coming from an operations research background, I know it can be a challenge to convince people that this is worthwhile.

But really, the term to me more or less means "mathematically literate." I know, there are some techniques that seem to be specifically associated with data science, like the analysis of large scale datasets, but many engineering and mathematical disciplines do deal with this already.

There's a reason these jobs want someone who has a degree in... math, statistics, computer science, operations research, physics, engineering, hell, let's just say "or related field" and be done with it.

It's partly because these fields contribute to intersection called "data science", but my real guess is that a degree in any of these fields means that you're probably mathematically literate. You've done one of those majors that requires you to take calculus of several variables, linear algebra, some differential equations, some kind of probability and statistics, probably write a computer program or two, and then focus on some more specific branch in depth where you learn to model things mathematically.

A good humanities curriculum will impart knowledge, sure, but it also trains you to read dense material, make sense of it, and express some kind of insight about it. A good "stem" curriculum does the same thing, except with numbers and data.

There was a time when someone could get a job by being highly literate. I see this as a similar situation - if you're mathematically literate at a reasonably high level, you're probably employable.

I don't know.

When I think of big data, the first thing that pops into my head is Insurance Actuarial tables, and that's not interesting to me. Are statistics suddenly the hottest and most interesting thing in the world because we can run experiments over larger datasets? Maybe, but I think that most engineers capable of doing the kinds of analysis these firms want would be better suited to harder problems.

Don't get me wrong, data analysis is important, I just wonder if the IEEE has a duty to encourage organizations like this or if they should be trying to influence kids back towards the "hard" engineering practices.

To be honest, as long as people are doing something that makes them happy, I'm not one to judge, but I do think there's something to be said for attacking things that are harder than statistics.

To me, this looks like a fad. Let me explain.

Five years ago, talk of Business Intelligence was all the rage. It was the 'hot' new thing that companies were pouring millions into. You needed the Analytical and statistics skills required to interpret large data-sets efficiently whilst having enough vision to clearly cut through the noise to deliver meaningful metrics. Technical knowledge of manipulating data using multi-dimensional cubes and datasets is also required.

Now it seems that 'Data Science' is set to pick up where BI left off. The fields appear very similar.

To avoid it being an oxymoron, I would clearly define the boundrys and goals relative to similar fields... BI / Data Warehousing / Data Analyst / Database Architecture

Disclosure: I've made a VERY good living since graduating working for Investment Banks in BI/Data analytics. I know from experience that money in these fields is more down to the industry you apply it to. Number crunching payroll or scientific data, low salary. Number crunching bank regulatory or trading data, massive money (regardless of what you call yourself).

I don't think a lot of people know about the Insight Fellows program yet, but it's highly relevant here: http://insightdatascience.com/

They're taking people in STEM fields who are over-qualified and under paid, and helping them transition into new careers as data scientists at top technology companies (Google, Facebook, Square, LinkedIn, etc.). It's a really interesting model because they're filling a big hole that universities have right now in that there's no degree for data science. Close to 100% of their Fellows make the transition successfully and I think the idea is something that others are going to try to copy in the near future because it's clear there's a supply-demand mismatch right now.

Fwiw, the company is a YC alumnus (a hard pivot from their original idea).

So, universities are going to start trying to teach a person to be a programmer (in several languages), a sys/ops admin, a statistician, a business/systems analyst, and a DBA all at once? Good luck with that...

I feel obligated to post, hopefully it's not unwelcome. While not in recruiting, we're always looking for more great engineers at Inflection(Inflection.com) - We're a big-data company and are crunching billions of records. If you're at all interested feel free to shoot me an email (tjbiddle at the-website-i-mentioned-above).

My take on data science, and what makes it distinct from other fields, is that it combines a knowledge of the business logic (i.e. software engineering) with knowledge of statistics.

For example, a statistician might wonder exactly what a particular ID referred to. Does it mean a person, an IP address, a single "session". They could, of course, find this out, but the data scientist would already know this.

Similarly, a software engineer might wonder what information they need to be collecting from the user. The data scientist knows what analysis will ultimately be done, and so knows what information must be collected.

So data science combines statistics and software engineering, and this is useful because it allows a holistic view of the data analysis process, from the collection of data, to the statistical analysis of the processed data.

I think that the word "Science" deserves some attention. I completely respect the data structure side of a data scientist but more often than I'd like I find people addressing themselves as data scientists being very good as software engineers, but not as good as statisticians or model builders.

I may be wrong but I disagree with who says that the difference from a data analyst and a data scientist is that the data scientist is a software engineer.

I would say instead that the difference between a software engineer and a data scientist is that the data scientist is a scientist that has a strong CV in data structures and algorithms, as well as in (maybe pure) science, with experience in statistics, math, or physics, and that knows very well how to work with models, test hypotheses, spot patterns, anomalies etc...

Data science seems, for now, to be what software engineering was supposed to be: a career where you choose your own tools and problems (with some constraints) and that gives you the freedom to move about in different industries instead of being stuck to one in the way that most programmers now are.

In many organizations, data scientists are full-time programmers but who get the dibs on the most interesting projects. I identify as a data scientist as code-word for "no-hire if the work's not interesting". There's plenty of hard engineering (in addition to traditional data science, where statistical intuition is more important) in data science. There are plenty of data scientists working on OS hacks, compilers, and other "hard engineering" topics. The difference and advantage for a data scientist is that your boss doesn't think he could do your job if he wanted to. If your title shows that you actually know math, you're not "just a code monkey".

What major differences are there between the new data science careers and what developer's have been doing in university research departments for years now? Is it simply a matter of scope? Transitioning from relatively small clinical trial sets to marketing data. Is credentialing needed because there is greater responsibility to formulate mathematical models in this path? If so how is that different than creating domain specific algorithms... Basically I'm trying to understand why this is seen as a unique career path as opposed to just another pivot developers may have to adapt to if they want to stay relevant or be on the cutting edge.

"I worry that the Data Scientist role is like the mythical 'webmaster' of the 90s: master of all trades" - Aaron Kimball, CTO at Wibidata

http://blogs.msdn.com/b/microsoftenterpriseinsight/archive/2...

What really differentiates data science from econometrics, other than (I guess) the type of data being analysed?

Can anyone suggest a structured approach[books, tutorials, free online courses] to learn data science?