I attended a datascience bootcamp in-person in SF from November 2016 – February 2017. It was a very intense crash course in data science and machine learning. I met a lot of amazing people – many of whom are still my good friends. It cost ~$16k when I attended (although there were scholarships available, one of which I was lucky enough to receive).
Before attending a bootcamp-type program, I think it's crucial to have a relatively good grasp on some kind of analytical programming tool (Python, R, SQL, etc.). It's also important to have a solid math foundation (although some of my math friends may disagree with me about whether or not an engineering education meets this requirement 😂). This takes time for sure, I spent over a year studying part-time before attending Galvanize, but I think being highly prepared really enables you to hit the ground running.
As with any field, gatekeeping inevitably takes hold and the bar for being viewed as "legitimate" rises – driven mainly by people who have already "made" it. I've seen this take many forms: denigrating people who take online courses, requiring graduate degrees even though it's unrelated most of the time, looking down on people who are using tool A vs. tool B, and so on. For many people, obtaining more rarefied certification, like a master's degree is simply not possible due to costs or a myriad of other reasons, so can more accessible avenues like bootcamps make you "legitimate"? My response is yes, it is possible if you're willing to put in work, keep trying, and believe in yourself (as lame as that sounds). Eventually you will find someone who is willing to give you a shot.
Galvanize
I often get asked about my experience at Galvanize specifically by people who are considering making a similar transition. I'll post just the summary post from my old blog.
Finding a job
I want to preface this section with a disclaimer: I received my first offer before I had officially graduated. In light of this, my job hunt was probably not as extensive as many other people's. I would say the number of applications I actually did was maybe around 10. However, I did interview with most of those places. I primarily interviewed for data scientist positions although I also did a few data engineer interviews.
What do data scientists really do?
One of the most important things I learned is that there are many different flavours of data science. I would say the spectrum goes from business intelligence and analytic type work on one end to machine learning engineering on the other. Every company's data science team(s) falls somewhere different on this scale and knowing where you want to be is important.
The points I've listed above are obviously not exhaustive and there are many, many, positions (most actually) that fall somewhere in between. However, I want to highlight what I think are maybe the two extremes. In order to get what you want it's critical to know what you want.
For myself, coding is something I love and find interesting so I tried to lean more towards roles that I felt would be heavier on the software engineering side. Other people who have stronger business backgrounds than me might lean more towards the consulting style data science where you do a variety of shorter projects with perhaps a few longer term ones. The point is that there are a lot of jobs out there all called "data scientist" but they are by no means all the same.
Getting interviews
I got interviews through three avenues:
- Networking/referrals
- Direct contact from recruiter
- Cold applications
The first method is obviously the most effective and I think if my job search had gone on longer I would have probably focused most of my efforts on that.
Networking is a word that has some negative connotations - especially if you are an introverted person. Some people may feel like it is shallow or self-serving but I don't think it should be. The most fruitful thing I did was have one-on-one conversations with people I found on LinkedIn or connected to through friends. I didn't target people because they worked at a company that had active job postings, but because I was genuinely interested in what they were doing. I had several information interviews that didn't lead to any immediate job opportunities, but were very helpful nonetheless. I think talking to a bunch of people is how I got a feel for all the different kinds of data science roles that are out there and which ones I would be interested in.
Getting the job
There are obviously books on this subject. You should definitely study for technical interviews, but I am going make a bold statement and say that a good technical interview can't be "gamed".
What is a good technical interview? I think it is one that doesn't consist of any random brainteasers or questions that you either know or you don't (e.g. XOR swap).
So how can you prepare for a good technical interview? I think spending time researching the company and trying to gain some domain knowledge is always helpful. Time I spent doing this was never wasted while all my attempts to study various topics were usually never actually helpful.
Was it worth it?
In a word: mostly.
I did a lot of research on Galvanize before deciding to come and I think some things are not clear or easily found. Here is the most recent distribution of salaries for graduates (based on September 2016 performance fact sheet).
Take note that the lowest bucket contains anything < 100k. We can see that really the distribution is bimodal meaning most people will end up with something relatively different than median or mean.
Furthermore, international students (such as myself) should note that Galvanize only reports these stats for domestic students and I think these specific breakdowns are actually only for Californian residents as well. Keep this in mind if you are planning on attending the program at a different campus and/or you do not have the unrestricted authorization to work in the USA.
What it is not
Galvanize is not a data science making factory. If you think it's impossible to go from 0-100 in the span of 14 weeks; you are right. My anecdotal evidence tells me that the people who came in with more preparation got jobs quicker than those who did not.
What it is
Galvanize is an amazing and vibrant learning community - just like they say. It was amazing to be surrounded by other passionate people who were all going through the same leap of faith I was in making this transition. Many of the people in my cohort are still my good friends and since I moved here from a diferent country; they were also the seed for new friendships - the value of which cannot be understated, especially as an adult out of college.