From the limited knowledge I have of this field, literature I have read suggests it may be a sought after and lucrative profession now and in upcoming years.

Can anyone in the know shed some light on this? Is it true? What geographical area is "booming" with this right now (in the US)?

I am interested in careers in Orange County or the Silicon Valley. I presume there may be Data Science jobs in the Silicon Valley. What about Orange County?

Lastly I only hold a Bachelor's in Stats from U.C. Berkeley. Worked briefly as a s/w engineer (15 years ago) using C/C++...so little and so long ago that it basically doesn't count. I am fairly adept at logic puzzles, such as those Google is notorious for asking interviewers, but I don't know any programming languages, or SQL, or anything else. I can solve problems relatively well though. Based on my educational level how long would it take me to become work capable in Data Science? Data Science is an unusual discipline as it's not as straight forward as just earning a BS or MS in mechanical engineering, for example. I'm not even sure how to go about acquiring the proper education. There are some online Master's programs I have seen advertised, but I really don't know how effective they are. I will say learning from home (online) would be a massive preference if that's possible. Some disciplines can be done online, some can't (such as ones with physical labs, or anything hands on related).

Would appreciate some insight from anyone(s) who knows...

Thanks in advanced

I'm curious, what did you between now and 15 years ago when you worked as a software engineer.

As you probably know, there are two major software platforms that statisticians work with: R and SAS. Typically one has to pay for a SAS license, but right now, SAS is letting people use SAS Studio for free, which seems like a good opportunity to learn the 'language'. I mention this as SAS has a certification program, which I would imagine could potentially help in landing a job. I think if you maybe find or invent a decent project to work on, it would show a sincere effort in this kind of work, as well as showing resourcefulness.

"I am interested in careers in Orange County or the Silicon Valley. I presume there may be Data Science jobs in the Silicon Valley. What about Orange County?" -- Have you done searches on sites such as LinkedIn and Ziprecruiter?

Online grinder. MTTs initially, then NL cash games after a while.


I have not. I don't even have a LinkedIn acct. It's obv not really geared for outliers like myself. May I ask why you ask? Sorry, not at all trying to be antagonistic. Just fishing for info...

All the data scientists I know have PhD's from MIT.

I imagine it will be very tough to get a real data science job without that level of education, and very easy if you do.

It's hard because 'data scientist' is such an over used term these days. But as I mentioned in the other thread many (and Id argue most) data science jobs are more about applying data science concepts to specific use cases then they are about cutting edge research.

There are a lot of PhDs looking for DS jobs, but many of them don't have the skills/experience for working with real life data (aka messy, large volume, and in different data systems) from companies.

I think in many ways it's analgous to programming and true computer science. You don't need a PhD in CS to do most 'computer science' jobs. And the people that have PhDs often aren't the best candidates for most of those jobs.

I would suggest learning how to program. That will open a lot more doors and options for you. Most data scientists have PhD, although the best ones I know have only a masters or less so it is possible.

I've been taking a self-paced Web Dev course through Code Academy. I'd need to begin a programming career at an entry-level.

Can you recommend any career sites to search for US jobs for entry-level types like me? I have nothing to show for it when I'm applying--just a B.A. in Stats 17 years ago, and too many years of grinding online, plus one informal online self-study Web Dev course. I imagine other applicants will have degrees in CS and/or industry experience making my resume look like a joke. So I'm not sure how to get my foot in the door. I interview well, however, fwiw. But getting the interview is likely to be the big hurdle. This is an aspect I really appreciate about poker. No red tape. Game's gotten too tough though and profits are diminishing so I'd like to reboot and start anew in a totally different occupation. Kinda need a change too. Grinding takes its toll.

The nice thing about tech jobs is that there's lots of easy ways to show off your skills. There's nothing stopping you from building something interesting and open source projects are like unpaid internships you don't have to apply for.

The 'red tape' of tech is pretty limited if you've shown you can do stuff.

I have been looking at moving into Data Science as well, for mostly the same reasons. We are coming from different directions, however. I have many years of experience in C++ SW development, but no statistics background nor anything on my resume to suggest my suitability for a data science position (other than a brief bit of poker on my resume where I "talked up" data-driven decisions).

The feedback from the internet seems to be to 1) Get on GitHub and work on something related, 2) Get on kraggle and partake in data science contests, 3) Get a nanodegree in data science from Udacity. By the way, that list is in the order of importance, not a timeline.

Relevant degrees are only good for a foot in the door, no one cares beyond that, and notable open source work is an acceptable replacement for a degree.

i didn't have a background in CS but got my foot in the door going to a bootcamp.

i think you can do the same for data science:

http://www.zipfianacademy.com/

It's honestly not that hard to get an interview for an entry level CS job. Just be honest on your resume and tell them what you've been doing. They'll then give you a phone interview in which they ask you basic C.S. principles. If you pass that then the you'll get an in person interview where you'll have to write a sample program in the language that your job uses.

It'll also help for you to get some of those online certifications in a few different languages.

Like justjaidai said above. It'd help wonders if you create a project to work on. Maybe to get practice learning a few languages write a poker equity calculator (with no interface or GUI) so you can be able to show that project and say "hey ive always been interested in programming while i was playing poker all of this time." For any job you do they're going to want assurance that you are serious about programming and are going to be at the job for awhile.

I thought this was more detrimental than useful.

"Data Science" is a wishywashy term. People who spend a weekend learning Google Analytics consider themselves Data Scientists.

1)Learn to program
and
2)Learn SAS

the shortest route to getting an entry level job is studying your algorithms (there are 3-4 famous interview prep books) for a few months. Know how to solve interview questions is more than 50% of getting those jobs. Spend the rest of the time learning one language well enough to solve algorithms problems. Also read hacker news so you have some ideas what's going on in tech

Which books are those?

Cracking the Coding Interview is the most popular one, and you can look in the "Customers Also Bought" section on Amazon to find more.

"but I don't know any programming languages, or SQL, or anything else"

You should learn SQL. If you know SQL and can remember enough statistics to do experimental design/statistical tests, you'll be able to get a job somewhere.

Doing a "bootcamp" is probably a good idea if you have the time and money (or live near a place that runs them); you'll learn the minimum needed to get hired somewhere. (That's basically their business model.)

"Data Scientist" is not a very specific term these days but web development is not a closely related skill.

It is highly sought after. But of course anything can become obsolete with time. NoCal, SoCal, Seattle, Austin, Boston, DC, and NYC are probably the best markets, but there's DS jobs to be had all over. I can't comment on OC specifically. I work from home in Ohio.

Just learn SQL. It won't take that long whatsoever. Just Google around and learn it. It will be useful to you.

How long will it take to learn? Idk. How much time do you have a week to learn it? I'd advise you to Nike it up and Just Do It. Take some data science MOOCs. Read up/practice on the required math/stats. Download R and RStudio, type "install.packages("swirl")" followed by "library(swirl)" and go from there. swirl is an R package designed to interactively teach the basics of R programming.

I'm currently working on an NBA related side project with a couple of 2p2ers. Mostly just data merging and cleaning and the like so far but about to get into the analysis itself. Feel free to join us. Happy to PM the GitHub url.

I got this job in a very roundabout way and sometimes feel like a phony: http://www.hanselman.com/blog/ImAPhonyAreYou.aspx However, I've become better and better with time. I first encountered data analysis in my sociology grad school program. I eventually landed at a tech company as a consultant. I developed my skills very minimally, saw an internal job posting for a data scientist, and said **** it and applied because I knew it's what I wanted to do even though I didn't feel remotely ready or qualified. Got it mainly because the dude in charge has no clue. Nice guy, great manager, but still has no clue about the topic itself apart from being a skilled programmer. And he blogs on DS on a well known tech site now. Ha. But anyway, it's worked out. I probably couldn't get a DS job at Facebook, but would feel confident interviewing with say, IBM.

Feel free to PM me if you want to join the project, especially if you like basketball, or just to ask any further questions.

Definitely do not need a PhD. I imagine PhDs would be more common in the public sector and in a market like Boston, which I believe is the most highly educated metro.

This is very accurate. Not only that, but I'd imagine it is far more interesting to most people to solve real problems than develop new methods of analysis.

#1 is good advice. I would advise against #2. Kaggle competitions often involve hardcore optimization that isn't practiced in most job settings because users have long amounts of time to fine-tune their solutions whereas this would be considered inefficient in most work contexts. I can see it being a good platform to demonstrate your worth, but it's not best for developing your skills. #3 is decent advice too. Get involved in some data science MOOCs.

Data science is not well defined. It is so young, includes a broad range of possibly valuable skills, and there is a huge range in the amount of talent among those who hold the title.

However, and I saw this in other posts too, I wouldn't advise learning SAS. Stick with R and/or Python. They are free, they are better, and they are more widely used. SAS may be more widely used in the public sector, but that's probably it.

https://lagunita.stanford.edu/course...uction_to_sql/

free sql class, enjoy

i think they use sqlite, but i'm told if you know the basic syntax for sql in general, moving between the various flavors isn't particularly hard. At least, from a data manipulation level.

Also interested in Data Science for a possible career change.

Currently running through the Johns Hopkins set of courses on Coursera, but primarily just doing the swirl lessons. I'm terrible at holding myself accountable for watching all the videos, etc. Contemplating doing a bootcamp or putting money towards an online program but it's hard to commit to dropping a couple grand when I feel like I'm probably capable of teaching myself given how many resources there are out there. Just need to man up and commit the time.

Since data science means too many things, you need to figure out what it is and if you want to do it. Does it mean "statistician who can code" or something else? Expert in mapreduce and Hadoop ("big data")? This depends on the job.

I think trying to contact people who do "data science" and talking to them is another approach that wasn't mentioned. Looking at job postings is flawed except in some cases, because most postings take the shotgun approach and have unrealistic requirements.

You're going to need to start networking no matter what you do. Just learning how to code and hoping good jobs come your way isn't going to yield the best results (speaking from experience here). See if there are Meetups related to your interests.

You can look into community college classes. Cheap and at least it's something you can put on a resume. It's a weird situation since you do have some past experience, you might be above 101 level. See if there is anything for R, SQL, or something else that will show you're still competent.

I'd also keep in mind that trends can shift quickly. While data science might still be in demand in 5 years, the bar might be higher, and many of the entry level positions available now might be automated into non-existence. Some jobs might disappear once companies realize they don't need a staff of several full-fledged data scientists, or it isn't yielding the magical results they expected.

Has anyone done any of the various bootcamps or online programs?

http://www.pce.uw.edu/certificates/data-science.html
https://www.udacity.com/course/data-...odegree--nd002
https://generalassemb.ly/education/data-science

Would be interested in hearing experiences.

What does a data scientist do a daily basis?

It is like writing SQL queries, and other scripts to search for useful information? I could see why poker players would be very good at this... unfortunately HEM or PokerTracker data analysis is so category specific, but probably good to mention.

Basically collecting, interpreting, finding relationships between, and communicating relevant information about large (probably somewhat disparate) sets of data.

Datacamp.Com is R focused, but its blog has some maybe useful (if very simplified) infographics on "what a data scientist does," "types of data scientists," etc.