Ask HN: What are the best data science bootcamps?
I'd like to move into data science by taking a ~3 month bootcamp. Which bootcamps are most respected by employers? I have a PhD in Computer Science. Obvious bias, but myself and a number of people I know have obtained great value from the Insight Fellows Program [1]. From what I can tell, nothing else really comes close. For their Data Science program, I believe a PhD is still a requirement, but it seems you have one. The bigger caveat is that it's not really a "bootcamp" per se, in that there are no real lessons or assignments. Applicants to the program are expected to be nearly ready for entry into the job market already. The program is rather for developing soft skills and networking. Although the program centers around a project, the project is really more of a conversation piece for interviews. Pre-COVID, this program was free, which was one of the best things about it. Now I believe there is an ISA instead. If you have to have a PhD this isn't a bootcamp, it's just a pay for play networking funnel. It's literally the thinnest wedge you can fit a business in in this space. Convince people they won't find a job without you and then charge then for the privilege of being a part of it. And charge companies a finders fee. A PhD could walk into a job without this 7 week whatever. Vulgar and predatory. Reading some of that site I get the impression that corporate delusions of grandeur must be reaching epic levels. 5 years (under)graduate and 3-5 years PhD and apparently you're still not good enough.. what a farce. Sure I'll pay 20k to participate in <stock photo session> so employers more leniently bestow upon me their blessing of <job>. Just waiting for the day even a PhD is made to be (!) considered worthless on its own. I mean it was free for years. As a former DS hiring manager who phone screened over 200 candidates, Insight is the only DS bootcamp that I've found to be positively correlated with capability.The other programs had candidates that were weaker than the generic new grad pipeline. Eventually we just made a PhD a hard requirement to filter down the ridiculous application volume. I can second this. Exact same policy for our data science program's recruitment pipeline; a PhD is a hard requirement and Insight was the only program we noted when reviewing applications. Still, it would be a toss-up between the PhD/Insight candidate versus someone 6 years their junior with 2-3 years of "real world" data science under their belt in the private sector. > Exact same policy for our data science programs recruitment pipeline; a PhD is a hard requirement > Still, it would be a toss-up between the PhD/Insight candidate versus someone 6 years their junior with 2-3 years of "real world" data science under their belt in the private sector. Aren't these contradicting each other? It sounds like you don't even consider the latter candidates. Or do you mean from your prior experience? Probably meant that a PhD is a hard requirement for new grads. As a 2019 Insight alum, I can't say more good things about the program. As someone who has now been on the other side of hiring, I can say that the fellows from the program really differentiate it from every other 'bootcamp' (I wouldn't call Insight a bootcamp as there are no real tests/homework/etc, but of course lots of expectations set by the program directors). Unfortunately, Insight kind of imploded like most other talent recruiting did during the pandemic, laying off basically the entire staff and not having a spring 2021 cohort (and I have no clue what ended up happening with the fall 2020 cohort). I believe they are trying to revive it, albeit at a smaller size and likely with the ISA. I truly hope they succeed as it was a fantastic program. I also hope they stay small. > I can't say more good things about the program. what were some of the top benefits for you in your career from attending this program? I would say i) having a relatable project which I came up with/collected data for/built out/productionized, so I could talk about a real data science project during interviews that didn't require a ton of background ii) having a cohort of very motivated people to learning with/from iii) interview prep with experts, both technical and not. Agreed. I’ve worked with and hired boot camp, masters program, and traditionally educated data scientists. Insight is the only non-traditional program that’s positively correlated with performance. It seems to (a) screen tolerably well, and (b) do a good job of helping the demographic of “quantitative and technical PhD but no industry exposure” translate the skills and background well enough to speak the same language we do. I think one of the comments said vulgar and predatory. I second this. You don't need a bootcamp to break into Data Science, or Data Engineering, or Data anything. Wow, $24k for the program? It’s a filter that says “After a PhD, I still have 24k to toss at this”. Not exactly. It was free for years but due to the program struggling during the pandemic, they implemented an ISA. In other words, you only pay if it works out for you. Thank you, fantod. I have not attended any bootcamps myself but r/datascience and other similar forums seem to indicate that bootcamps are useless.
Especially with your PhD, I think an overview course like the one offered by Andrew Ng will get you upto speed. You can add others like AWS certification, Tableau etc.. based on your needs/jobs you're applying to. Bootcamps seem most useful for taking people who are already qualified, prepping them for interviews, and introducing them to companies. Someone with a PhD in Computer Science might actually benefit. I hire Data Scientists. I get A LOT of applications. The name of the bootcamp doesn't matter. If you haven't worked as a Data Scientist before, have a portfolio of code I can look at. If you have, I care about how relevant your experience is to my problems, not about where you learned pandas. I'm curious why you want a Data Scientist to show you a portfolio of code rather than a portfolio of data projects & outcomes. The code seems pretty secondary unless you're in a some specific field looking for code-based solutions to a niche problem. A data project without code is extremely difficult to evaluate. I haven't hired data scientists but I've worked with them, and the best I've seen by far are the ones who can write well engineered code and who know how to use source control and unit tests. My personal opinion is that I want software engineers with good data skills, not expert data people who are terrible at the engineering part. After all, ever experienced data scientist will tell you that 90% of the work is building pipelines and cleaning data. I have hired data scientists, but not in the last two years. I agree that 90% of the work is building a good pipeline and identifying + cleaning data, but disagree that example code is a good way to evaluate either of those skills. I'd much rather see that a candidate knows how to use common tools, understands how to go from real-world data to usable data, and has a strong focus on an end result that's actually business-valuable instead of a toy. Probably a different focus, though. Most of my projects and hires are for large corporate clients, not smaller niche companies/startups. How extensive a code portfolio would someone need for you to consider them? How much experience would they need? I got my PhD about a year ago and have been retraining myself to become a data scientist when postdocs didn't happen, and I'm really not sure how to break into the market. Thank you, throwitawaybaby. I'm going to echo the Insight Data Science Fellowship Program as, by far, the best in that space. Even then, please pick up a copy of Clean Code if you're from another field. That's a chronic issue with data science candidates. I think some of this has Python to blame. It’s very difficult to write idiomatically maintainable code in dynamic, scripting languages. Now also consider that data wrangling is almost always highly coupled to one or more input formats, lots of index-via-literals, string manipulation, type conversions, field copying etc... I think I’d have trouble writing what others would consider Clean code in such a setting - and I’ve programmed for 40 years. Based on your background and some word of mouth, I'd recommend I'd recommend the Insight Data Science Fellowship. It's important to filter out bootcamps targeting no background whatsoever, so at the very least this should be a solid starting point: https://insightfellows.com/data-science Thank you, dwrodri. Depends what your goal is. Name, Learning, Job? The value add from the name of a respected bootcamp isn't as high as you think. Getting a job will come from your effort and creativity in the search process post-bootcamp. Which program you choose depends on your time, money, and energy constraints. The good news is that there are plenty of options out there. I wrote a post [1] outlining the constraint tradeoffs based off my experience in a bootcamp and subsequent career movement through the data world. [1] https://www.dataindependent.com/blog/joining-data-bootcamp/ Edit: Emphasis on the 'name' of the bootcamp (vs the other value adds you earn) Thank you, statmapt. You don't need a bootcamp if you have a PhD in Computer Science, just apply to all major tech companies you'll get atleast a few hits. Thank you, amusedcyclist. Given that you have a PhD, I don't think the bootcamp will be a differentiator. It might even be a red flag for some employers. Unless you really don't feel ready and want to get up to speed with specific tools and get some interview prep - in that case the bootcamp might help but it won't be what gets you the interview. You can probably do much better than you think. Thank you, beforeolives. Why not try for data engineering? I'm looking for data scientist and data analyst jobs and it's been rough, you'll get over 100 apps for most positions because so many scientists like myself pivot out of the ridiculousness of academics. It's very hard to stand out, even with multiple publications, employers are flooded with great applicants. Maybe a data engineer can chime in, but the market certainly seems better for that skillset than for us python, R, sql, stats people who are a dime a dozen. What is the difference between data engineering and data science? The terms frequently seem to be used interchangeably, but apparently they're not synonymous. They aren't synonyms but they sometimes overlap. - data engineering involves more work on data transformation and developing different pipelines - data engineering requires more knowledge of databases, cloud environments or different streaming tools (it gets close to being a backend developer in some places) - data engineering doesn't involve any statistical modeling, data science does - data science is a broader term - depending on the company a data scientist might be doing all the data engineering work (if it isn't too much) + the model work and statistics. Or they might be focused entirely on research, statistics and ML models Thanks, that clarified it. Do you know how people typically get into that role? For us, depends on the seniority of the role, but we've had good luck bringing in people coming from both directions (where I define the "directions" as "software engineering" and "data science/analysts") Analysts and junior data-science types can often make the transition well if they can beef up their engineering skills (i.e. learn to write tests, make stuff that will be maintained for years) Software engineers are often a good fit too if they can pick up some of the data skills (get really good at SQL). Probably really depends a lot on the specifics of the position, sometimes "data engineer" means "write sql queries to apply business rules" and sometimes it means "maintain our interesting in-house ETL applications which were written in Java 8 years ago" I'd also value the soft skills a lot if I were to be hiring data engineers - so much of the job tends (at least where I am) to be correctly interpreting business rules/needs and anticipating potential future use-cases. Hmm, so it might be out of reach for me. I have a PhD in pure math, and no experience as a software engineer. I've coded for research, but never for production. You can move over from being a software engineer to a data engineer pretty easily. Or you can be a data scientist who had some exposure to that kind of work and move over to data engineering quite easily too. I think you're right, they're pretty wishy-washy, but I'd define data engineer as someone who builds systems that make quilty, useable data available (i.e. anything from building ETL pipelines to productionizing models), vs a "data scientist" which I'd probably describe as doing more one-off in-house research type work. I suspect a lot of "data scientists" end up being "people who write tableau reports for other people" and/or "people who manage an ugly pile of python data processing scripts to make the data-spice flow" In my experience the plumbing is a lot more work [requires more man-hours] than the interesting visualizations, and I think some organizations do a good job of supporting a few scientists with a robust engineering staff, while others hire the scientists because they want the fruit, but forgot to plant the fruit tree. I don't think you need a bootcamp. Foundational DS concepts can be self-tough given the right resources (such as Manning's "Data Science Bookcamp" https://www.manning.com/books/data-science-bookcamp) Thank you, the_decider. Have a look at the courses offered by DeepLearning: Thank you, lobo_tuerto If have a PhD and you're motivated enough to self-study, you probably don't need a bootcamp. There is likely a mentor on SharpestMinds who can help you break into the industry. Thanks, russ_poll. We've had good experience with Insights are a previous company. That said, generic Data Science is somewhat saturated right now in my experience. With a CS degree you should probably look more into the ML Engineering side of it. Thank you, marcinzm. Insight is good for PhDs. Thank you, danbrooks. AFAIK Insight is the only one laser focused on your persona (PhD in a math-y field) Thank you, skadamat. As someone who's both gone through a DS bootcamp (not Insight but similar) and also been on the hiring/interviewing side, I've thought a fair bit about this. First off, have you tried applying for DS jobs yet? What were the results? As others have alluded to, a bootcamp is not a mandatory prerequisite for a DS job, and is no substitute for on-the-job experience. If you're finding that you can generally get callbacks from recruiters/hiring managers based on the strength of your current resume (or have friends in the industry who can give you referrals), then you probably ought to just keep up with that approach. You can refine your resume and interviewing technique based on the parts of the interview process you struggle most with, and eventually the pseudorandom number generator that is interviewing will work out for you. If you're submitting your resume to lots of companies and never hearing anything back, then a bootcamp might make sense. My general advice for a bootcamp is to look for one that meets your needs and has incentives aligned with yours. In general I think that is probably a better way to choose a bootcamp than trying to figure out which ones employers respect the most. There's no one right answer to that question, and honestly that answer can change over time: you can see in some of the other comments that it looks like the Insight fellowship has changed significantly in response to the pandemic, and I know that the bootcamp I did changed fairly substantially in the years after my cohort. IMO none of the existing bootcamps have the history or pedigree at this point for their name to mean a whole lot on its own. Generally in my experience bootcamps tend to be split into two groups: 6-8 week project-based ones, mostly focused on polishing candidates that are already close to being ready to get DS jobs; and 3-6 month training-focused ones, designed to upskill people who have a minimum baseline set of skills but are not particularly close to being competitive DS candidates. If you have already been doing some reading on the side and mostly need an introduction to hiring companies (and maybe a project to talk about), shorter fellowships make more sense. If you think you need more training in core DS concepts, then a longer program may be better. Prefer programs that only make money when you get a job (either via as your recruiter or via an income share) versus programs that charge an upfront tuition, although note that the former tend to be harder to get into and may actually exclude you from some jobs immediately after graduation (if they work as your recruiters, large companies with their own recruiting arms may not be willing to pay the extra recruiter fees). Finally, if you're in a bootcamp, make sure that you're doing something to differentiate you from your peers. The first time I saw a bootcamp candidate talk about their model to find ideal jogging routes based on RunKeeper data hosted in a Flask app using the Google Maps API it was super impressive; the third time I saw a candidate present this same basic project was much less so — it was obvious that to save time all the bootcamp participants had been taught the same basic stack and given a lot of hints for what kinds of projects they could do.