Can someone explain how you can train Data Scientists in 6 months? What jobs do these grads walk into?
The tech prerequisite is: "You should feel extremely comfortable with how a computer works including: touch-typing, web browsers, search engines, and basic computer programs."
The Curriculum:
Stats, Linear Algebra AND Data Wrangling: 4 Weeks
Modeling: 3 Weeks
Data Engineering, Databases, SQL, Productionisation: 3 Weeks
Machine Learning, NLP, Neural Networks: 3 Weeks
Python, OOP, Algorithms & General CS: 3 Weeks
Project work: 3 Weeks
At that pace over so many subject is anyone walking out able to remember what a dot product or a t-test is from week 1?
You assign precourse work that rejects 98% of applicants. That helps select students with existing experience, sometimes even STEM degrees.
According to the example contract on their site, their income share agreements last up to seven years. Even if they fail to teach a student anything, the student can spend years studying on their own (or even get a degree), and still owe money.
>You assign precourse work that rejects 98% of applicants.
I think a good model for this is the Insight Fellowship. By selecting mostly STEM Ph.D. graduates, the pool of students already has some relevant background/foundation. The focus can then be on the tools and methods for applying DS in a corporate/business setting, which is actually relevant for jobs.
Insight grads, in my experience, have been a notorious joke. Nothing has diminished my respect for a PhD from a top-tier institution more than the people I have worked with coming out of that program.
Interesting, I've had the opposite experience. One of the best managers I've ever reported to and worked with came from the project. I've also worked with a few good engineers and scientists from there, too.
I suppose this is a reminder of The Law of Small Numbers and sample size importance!
if you have a STEM degree, what value can you derive from short introductory classes on linear algebra, Python and relational databases?
Countless truly excellent and free/cheap resources on these topics exist and you don't have to give a way a significant percentage of your yearly salary.
I think the value is similar to the value of a personal trainer. There's no rocket science involved in maintaining fitness, but many people find value in the added accountability and planning support.
serious question: Why isn't there a service "personal trainer for coding"? Not so much for juniors looking to get into the industry, but... I'm sure there's a continuous pipeline of high-value people who would pay $$$ to keep them accountable to doing say three coding exercises a week while they are getting ready to switch jobs or are between jobs.
Perhaps because one of the "three virtues of great programmers" is hubris - which Larry Wall optimistically defined as "The quality that makes you write (and maintain) programs that other people won't want to say bad things about." but which also/really means "Excessive pride or self-confidence."
I suspect many/most coders think a "personal trainer for coding" is a great idea - for other people (especially their idiot co workers). But they obviously don't need one themselves, and would never need to pay for new...
Hubris drives you to re-build things you shouldn't... which is how you learn why they work the way they do. And maybe to try things you "shouldn't", which end up working out OK fairly often.
This actually exists. I would link to a specific example, but it's run by an individual and I'd feel weird doing that. "Software coaching" might be a useful keyword?
There are programming tutors on wyzant and other services. I suspect a lot of people learning to code who already have a coding friend/person in their life do this informally.
I really like the idea of a talented tutor helping with subjects I struggle with. Even if it costs thousands of dollars this can be a great investment. The offer would have to be focused, customized and beyond the basics. Given the materials that are out there (MIT open courseware comes to mind) it is really beyond me why people spend so much.
You can procrastinate away your entire life with "someday I'll surely take advantages of all the free resources on X that are out there and finally learn X and maybe even shift careers!"
Just think of all the things you never bother to learn despite having a latent interest even though you can open a new Youtube.com tab right now and fire away. It's hard to start, and then it's overwhelming when you do, and then you feel like a failure when you don't stick with it.
Isn't it worth quite a lot to snap out of it and finally do it? It doesn't surprise me that people find this to be worth thousands of dollars. When you consider how fatally expensive it is to procrastinate your goals forever, maybe these schools are a bargain.
This is literally me, although today I finally found the motivation to actually put together two of the parts I 3d printed a while ago. Maybe some day I'll finish the robot.
I've thought about the Lambda School model, sure the funding model is good, however the problem is what that funding is being used for. If you could work on the material on your own and hire a TA paid by the hour to grade your assignments, give feedback and provide assistance, basically only pay for the services that you actually need then the cost for a degree would go down massively.
What Lambda School is trying to do right now is just fish for good students and hope they graduate as quickly as possible so they can collect as many ISAs as possible for as little cost as possible.
Exactly. Some people need a structured environment to get that kind of work done at a decent pace. It also helps to have feedback from a teacher, just like a personal trainer helps you with form.
I have seen another approach (also with STEM PhDs), where it isn't an income share, but rather that the incubator gets a referral fee if the student is hired--similar to a recruiter. Now, why do this? The math behind most of data science is rather basic for a number of PhD physicists--I would say the greatest weakness on the math front is that many will know probability, but not Bayesian statistics. However, the level of coding will be mixed with many not knowing about source control and a number coding in say Matlab rather than Python. Now, some have said that people can learn this on their own, which comes to the next reason. Physics graduate students and postdocs can put in extreme hours and their supervisors can expect those hours. 100+ hour weeks happen (especially for experimentalists--a number enter industry and it takes readjustment to figure out what to do with spare time). By formally leaving and joining an incubator, they give themselves space/time to learn--it's also a pretty intentional act where they have to decide that they are leaving the field (which can be psychologically difficult). The next is job interviews--the typical physicist has no idea what a data science interview will be like and coaching will help them a lot. Finally, there is networking, where the incubator may have connections with hiring managers where they can at least get people into interviews. I have seen people do either the incubator route or the DIY route and for those who have gone with the incubator (where they don't pay and the business model isn't a percentage of their salary coming from their pocket, but rather a recruiting fee going to the incubator from the company they get a job at), then they seem fairly satisfied with their experience.
Exactly this. The only Bootcamp grass I know that were successful in their post grad jobs were already developers but with a different skill set. The entire concept of turning non technical people into developers is a farce.
> Can someone explain how you can train Data Scientists in 6 months?
Doesn't it ring alarm bells that society attempts to herd 100% of students through 4 year programs? That sounds far from optimal, no?
It seems highly likely to me that if you get someone who excels on the steep parts of learning curves, 6 months studying the right things would be just as effective (if not more) than 4 years doing mostly busy work.
> What jobs do these grads walk into?
I think they walk into entry level jobs. I know I much prefer working with people that love to live on steep learning curve slopes over those who prefer to go at the pace of the system.
> remember what a dot product or a t-test is from week 1?
I have been in data science for many years and most of the terms I was taught college (like t test) never come up. Maybe once or twice a year I have to spend 60 seconds to refresh on dot product algo.
>It seems highly likely to me that if you get someone who excels on the steep parts of learning curves, 6 months studying the right things would be just as effective (if not more) than 4 years doing mostly busy work.
I don't know what you were doing in college but anyone who can learn four years of material in six months probably doesn't need Lambda School in the first place.
If you cut out the commute, obsession with socializing, 1x speed inefficient lectures, impersonal general purpose homework, breadth first class catalog that try to get you well rounded - the necessary material for most professionals is not four years worth. "I never use what we learned at school, professional life is very different" is a very old meme.
Where I live, colleges prepare you to be able to choose to be many things - continue academia, or be part of the workforce under diverging titles. They assume you don't know what you want to be yet - and it is mostly true. But if you are at a point in life where you know what you want to be, you can skip most of it (or even do it live when needed). The "keep your options open" attitude of colleges have a lot of overhead. What most people need from a 4 year college can be easily condensed into a year. It WON'T be 4 years worth of material. But if you know what you want (not keeping your options open) you don't need most of it.
I mean, I’ll be the last person to say you need four years of college to be an effective programmer, but that’s not really what I take issue with. I dropped out of my BS after freshman year and got my first job in the industry instead at 20. The rest of school isn’t busy work, I just prefer teaching myself, as I had been already for eleven years.
I saw countless people at my cow college who were utterly unqualified to be there, but because "It's free money!" and Mommy and Daddy expect them to go, they went.
For entirely too many of the young women, it was Four Year Husband Selection Tryouts, and for too many of the young men, it was Random Sexual Encounter Weekend. A couple of folks were there because we had the most powerful supercomputer in American academia, or for our excellent agricultural and engineering programs, but they were the exception rather than the norm.
Going back at age 28 after a six year stint in the Navy gave me a very different perspective though, so I may not be the best example.
Statistics of enrollment percentage doesn’t really describe the culture surrounding college in the United States, though. Plenty of people don’t end up going to college but that certainly hasn’t stopped it being considered a panacea for employability in this country.
This is still being pushed by politicians. It’s still what pretty much every parent hopes for their children as well. On this website I’ve seen people derisively describe people as not college educated.
> On this website I’ve seen people derisively describe people as not college educated.
This is unfortunate, because I've met quite a few very smart people who never went to college, or who started but had life circumstances that pulled them away before they were able to complete a degree.
College is not a panacea and the sad but honest truth is that most jobs do not require a college degree. Most of the jobs in America can easily be done with on-the-job training; we've just seen companies shift to a model where they want to offload those costs to the American worker by letting them spend $50,000 to $250,000 and four years of their lives "proving" they can get an education that many of them don't really need, and don't really want in the first place.
We also allowed well-meaning people to pass laws preventing companies from conducting IQ tests and thereby sorting people as was done many years ago. I know this is true because years ago I used to eat lunch with the Director of Human Talent Acquisition at my company almost every day, and we would discuss this. He was a very engaging gentleman in his mid-70s with an almost encyclopaedic knowledge of current and past HR topics, philosophies, and controversies. He had worked for a well-known Fortune 50 company for almost 35 years before "retiring" to my company, a mere Fortune 1000 player. He can recall using IQ tests to sort applicants into positions for which we now demand bachelor's degrees, or in some cases, even master's degrees.
A bit sad, if you ask me. Our culture should be progressing, not regressing...
It should but it is likely that the alarm will not be raised enough. We are ALL taught from grade 1 to "prepare for college, so it can turn us into adults" as a life philosophy.
It probably does not help that every single one of our teachers was forced through that system in order to be granted the "privilege" of teaching us.
The signal to noise ratio is so awful these days that it is a serious liability to understand what you're talking about in data science interviews.
I have seen been on hiring side of multiple larger organizational interviews where the "answers" to the questions asked during interviews were not even correct. Candidates that knew what they were talking about would be rejected because clueless interviewer didn't know what they were talking about.
Whenever I have to interview I have to play guessing games with interviewers trying to understand what they think is the correct answer. I've had to explain that, yes you can do statistical inference with a linear model, that Poisson regression and Logistic regression are in fact only different in terms of a link function, and of course also had to explain why a Poisson random variable might help in modeling number of purchases. In all these cases the correct answer was "use XGBoost".
I think most people who would be considering bootcamps are comparing it to a traditional 4-year college curriculum -- at least I did. I enrolled in the full-time web program a few years back and this was the back-of-the-napkin math I used to make my decision:
Total Hours of College:
4 years, of which 2 years are "core" coursework and 2 years are general education.
2 Years * 2 semesters per year * 15 weeks per semester * 15 credit hours per week = 900 hours for a degree.
Lambda School:
6 months * 4 weeks per month * 40 hours per week = 960.
Obviously this doesn't account for a number of variables such as 0-credit "mandatory" labs, out-of-class work/projects/studying, but a Lambda student who spends an extra 5-10 hours per week during the program would end up with 1000-1200 total hours.
But this still ignores the main advantage of a program like Lambda, which is the ISA. An education that would be inaccessible to students at the margin -- think people who can't afford to move to a university, qualify for loans, or take 2+ years off of work -- can get in if they're committed enough. Even if Lambda were a total scam, at worst you're out 6 months and $0. If Local State University doesn't get you a job, you're out 4 years and $50k+.
Looks like you made a calculation error or misunderstood your units. A credit hour is 3 hours of work, as a credit hour is "(1) One hour of classroom or direct faculty instruction and a minimum of two hours of out of class student work each week for approximately fifteen weeks for one semester or trimester hour of credit, or ten to twelve weeks for one quarter hour of credit, or the equivalent amount of work over a different amount of time"
So it's 2,700 hours for a 4-year degree, versus 960 hours in Lambda School, just using your method of calculation. It's also not counting the internships or summer programs that students in 4-year schools usually partake in. And it does not count extracurriculars during the schoolyear, like hackathons, interview preparation, programming competitions, student group projects, etc. Finally, you're assuming that an entire half of a college degree is geneds, which is really not the case. It's more like 1/4 geneds, 1/2 required major/concentration courses, 1/4 electives which many students opt to take technical courses in. So probably more like 3,200 (minimum) to 5,000 hours in a 4-year college.
General education courses do not take up half the course work. Moreover, you are suppose to spend 2-3 hours working out of class for every hour in class. You realize you have to study and do homework outside of class... No one considers a bootcamp to be comparable to an degree at a good university.
I have trouble getting students to concentrate on difficult material for anywhere near a whole class even if it's their only class of the day (and I'm considered a good and entertaining professor, honest). No WAY I could get anyone to concentrate on difficult material for 40 hours per week. I mean really no way, even if I were a teaching genius.
Learning difficult material means taking breaks and digesting stuff subconsciously.
Therefore, learning anything difficult takes a certain minimum of calendar time as well as hours in the classroom.
Enough buzzword to pass an interview. Unless someone already has a pretty solid degree, it's impossible to learn the fundamentals that quickly. And if they do, they should take grad classes on data science, not go into a bootcamp...
They have. There are companies in the UK that hire Oxbridge grads from the humanities with no tech experience whatsoever and train them. In the States, IBM has a similar apprenticeship programme that lasts for 1 year.
In the mainframe era (e.g. up until the late 1990s maybe?) the big US consulting firms did the same thing. Andersen Consulting would hire smart history and english majors and teach them COBOL in 6 weeks and then dispatch them to a client site to work 60 hours/week. Granted their approaches, designs, and code were highly standardized, but the work got done.
I did this. I started in QA with very little experience. I had some linux experience from personal projects I'd worked on and 3 months of a pretty mediocre python developer bootcamp. The company I started at mostly hired less experienced people and had them work QA for a year to get them integrated. It worked really well. You were on an integrated team with QA/Dev/Ops and as QA you sat with other team members and did manual testing while you figured it out. They had a great mentorship program and were serious about training and promoting from within.
I think one of the reasons bootcamps get away with this kind of thing is that, for at least many people, traditional schooling also fails to teach this material. So if you're going to just teach people enough to get a job, but not actually to do it, better to do it for "cheap" in 6 months than, say, expensively in a 2 year masters program.
I think the problem above is probably somewhat worse for data science than software engineering in general, but it applies broadly to quantitative jobs in the tech industry, in my experience.
Data science bootcamps don't make much sense to me unless the student is coming from a STEM background. If someone has studied engineering and already had courses in linear algebra,stats, basic programming, then it shouldn't be hard to learn the more practical data science stuff
I kind of suspect it's a bit of a light-weight Pareto principle: you can gain 70-80% high-level knowledge (note that I said knowledge, not understanding), which is enough to get started with some real-world applications with sufficient handholding and mentoring.
I don't think this is necessarily a bad thing, but expectations should be set appropriately.
If you've pre-screened to select for candidates who would be able to take that and run with it, I'm not suprised that you could achieve a sufficient amount of decent outcomes.
I think this is correct, re: the Pareto principle.
I'm a Lambda grad (Full stack web, not DS) and at the end of the program, I was knowledgeable about a lot more than I understood; and that also includes being knowledgeable about a lot of my shortcomings that I was painfully unaware of before Lambda.
A year after Lambda working as a SWE and I am now finally starting to feel a sense of domain... I hesitate to call it expertise, so lets say domain 'comfort' when it comes to building and maintaining react or react-like FE applications and node BE apis. I've had a mentor at my company since I started and mentorship is necessary for anyone coming out of a bootcamp with no prior industry experience, IMO.
The expectations set within the bootcamp are reasonable, IMO. Instructors never made it seem like we were going to fully internalize everything we were being bombarded with. Practicing what you learn is crucial to turning any of the things taught into a skillset, and that is made very clear.
I imagine the outcomes are pretty good for those who get through the program, as well. They were great for me and most of my cohort peers that I keep in contact with. The ISA model, IMO, makes a lot of sense and I'd be surprised if Lambda couldn't make it work in the long run.
A friend of mine got a data scientist job at a well-known tech company after a summer-long bootcamp. He had previous experience, though, doing some academic-track work in a scientific field. So the training was giving him a more solid background in an area that he was kind of informally nudging into in the first place.
Technically the prerequisites are very loose but the best success stories I hear are usually more like, someone got a STEM degree but actually not all of those degrees translate into well-paying jobs, after this boot camp they were able to shift over to a data science career.
That seems like an odd course setup for a DS. I'm not a DS but kind of learn about it as a hobby, but I think if you cut the course down to just the first 2 topics people would have a better foundation.
All the DB related stuff seems like a bonus, ML should be easy to pick up if you have good LinAlg and stats knowledge and Python you can pick up along the way.
I also agree on your last point. I don't understand how someone would be able to remember a fraction of this knowledge when you're changing topics so often.
The last point is important. When you're in a university you take different courses over the years but they often reuse or are based on some of the material covered in earlier courses. You learn by repetition and as you go into more advanced topics you can often understand better what you learned before - because you learn connections between things.
But when you need to learn a lot of things in a short period of time you don't have time to reflect and think how it all works together.
I find this often in my programming projects. Each sub-project requires a lot of focus and concentration. That seems to make it easy to forget about the previous (sub-)project almost totally. Putting in lots of new stuff into your brain means some older stuff gets pushed out of there.
The more you learn the more you forget. The only way to fight this is to try to understand the connections between the sub-topics, but if you are in a hurry you don't have time for that.
I don't know data science topics, but I can tell you a friend of mine was working on a Computer Science BS while I was in Lambda Schools CS section. He had a semester each on Algorithms and Data Structures. I had 1 month on those 2 + Graphs. At the end of my month, I was teaching him these things because his Data Structures class was basically SQL.
I can't directly answer your question, but I'm sure the answer has something to do with figuring out and teaching the base requirements of the topic, methodically, with well experienced industry professionals.
> Stats, Linear Algebra AND Data Wrangling: 4 Weeks
Ridiculous. Introductory statistics and linear algebra are two separate undergraduate level courses. And if you want a real grasp of statistics, you need to learn probability theory first, which is another semester long course.
To say nothing of "data wrangling", whatever the fuck it means.
Your intro stats and Linear Algebra classes are 4 hours a week. These classes are 40 hours a week. Every week is essentially a quarters worth of work. You can take undergrad courses like that in the summer.
Pretty sure "data wrangling" boils down to god-tier regex knowledge - which is less theoretical heavy-lifting than an acquired art that comes with practice.
It is more than that and it is what Data Scientists spend a significant amount of time doing. Real world data is extremely messy and a lot of time is often spent understanding what exactly the data is, changing it from wide to long, deciding how to deal with missing values etc.
This highly cited paper is a good introduction to the subject:
There’s decent science on why this isn’t possible. Basically you need to space things out to give the brain time to work out what to put into long term memory
It's possible, but for very few people. I went to MIT and there were stories of people there taking ~12-16 classes a semester which theoretically averages out to a course a week (though they ran concurrently and it's a crazy course load for the average person). To be fair, the people who could do that were way on the right of the distribution and it's hard to convey just how fast they could pick things up (think of them as the equivalent of an NBA player vs the average MIT student as a D1 athlete).
I react well to that approach if I'm really excited about the material, and usually do well in the coursework. It does take a few months for everything to "really" sink in though, and having a lot of friends or acquaintances from MIT and Stanford that did that I think it's rather the norm.
Approving coursework doesn't mean that the concepts sunk in. It just means you have new tools to further your understanding of everything, but it still takes a long time for it to sink in. During that time maybe you just need to do nothing, just let it sink in, but in my experience everyone ends up needing some time.
You simply can't take 12-16 classes a semester, the day doesn't have enough hours for that. They _might_ have gotten permission from the professors to skip classes and just show up to the exams, which is _not_ learning.
Not sure why that's not learning - if they understand the material at the time of the exams, who cares if they show up to class? Most people forget most of the stuff they "learned" in college 10 years out anyway - most of the economic worth is in the credentialing / signaling versus the actual education (since getting into the school is the hardest part)
I meant that it is not learning in the context of the discussion, which was whether people can assimilate that amount of information in a semester. If you show up to the exam and pass then you did all the learning prior to that.
Depends. Mathematics? Chemistry? There's some room for compression there, but not much. If you ditched all of the absolutely useless "general education"[1] classes, and kept class sizes small enough to allow time for individual coaching, I think most STEM undergraduate degrees could fit nicely within two to three years.
[1] Students are welcome to "expand their horizons" when they aren't paying thousands of dollars for the privilege of being forced to do so.
As a Data Science student that just recently graduated from the program, it prepares you for a data analyst position, at best.... which essentially means any job where your primary role is to wrangle data.
One issue is that if you take a top CS program grad with “proper” academic training and even some internships, they still need to look up references or take a quick review to remembers the things in this list. What really made them competent at work is the ability to learn quickly and solve problems not in book.
That is the main concern I have regarding these bootcamps is that they focus way too much on surface knowledge.
There a wide range of data scientist roles. I’m a senior data scientist and couldn’t tell you how to do a dot product or a t test
A lot of my role is just analyzing data for patterns , implementing rules into our product, building models using very basic libraries and putting them into production, monitoring models in production.
It isn't as though 4 year degrees teach purely useful information. If you stripped my computer engineering degree to courses that I use for software engineering, it would fit in two semesters. If you tossed out all the non interview required theory, it could fit in one.
It is a bit like playing tennis. Playing against your 75 years old limping neighbor is easy, playing against competent people is hard, playing on the tour is for the very talented only.
The levels of inhumanity and doublespeak in here are staggering. This post attempts to euphemize 65 people losing their incomes, and probably healthcare, to the point that it reads almost as farce. Not to mention what the impact will be on those remaining, students and staff alike.
"...they believe it's impossible to make this model work.
We strongly disagree." This is wildly insensitive to those involved. I find it hard to imagine how the 65 people laid off today are going to make that make sense when trying to look for new jobs.
"...today's restructuring and right-sizing..." Is this to say that the 65 people who lost their jobs today were somehow wrong? Again, it staggers me how lacking in empathy these words about people losing their jobs, and probably healthcare, indefinitely.
"For employers looking to hire senior product, engineering, design, community management, or instructional staff, we'll be sharing a list of interested staff looking for their next opportunity on social media" And good luck current students; these people will take all the jobs you're aiming for.
I get what you’re saying here, but think you’re being a little too harsh. I’m not sure what else to call a reduction in force for PR purposes, I don’t imaging just saying “fired 65 people” is productive.
In this comment:
“ "For employers looking to hire senior product, engineering, design, community management, or instructional staff, we'll be sharing a list of interested staff looking for their next opportunity on social media" And good luck current students; these people will take all the jobs you're aiming for.”
Sounds like Lambda School grads are pretty junior and they’re giving people that are senior a good recommendation. I don’t think there’s much overlap between LS boot camp grads and senior people leaving LS as employees. Maybe I’m wrong?
I think the critique is for the corporate need to propagandize every act such that it paints all decisions, and by extension the leadership, in the best way possible. It's revolting anti-truth.
Marketing and propaganda are among the most disgusting inventions of human-kind.
Austin seems to take corp-speak to another level. See his response when they settled with the California Department of Financial Protection and Innovation:
I am an outsider, but clearly looks to me like they chose to not fight that battle, which was just legal thuggery. Colleges in the U.S. are a racket that make the mafia look like a bunch of amateurs. The Bankruptcy Exception for student loans is one of the most corrupt scams going on in the USA today.
Lambda lied to their students for years about loan dischargeability. They were told to stop by the regulator, so Lambda did. The regulator did nothing to penalize them.
Didn't they lie by wrongly saying the loan can't be discharged through bankruptcy? Surely that would make candidates less likely to join Lambda?
Why is it even in their interest to say that? It's not like them saying it makes it true. And if it were true, it would mean that Lambda is a bigger risk for the student who therefore may be less likely to apply.
Lambda was securitizing the ISAs and selling them to investors on the backend, obviously with a discount based on risk.
Some people treat this like a bad thing, but it's really pretty basic risk-mitigation and cash-flow stabilization. This is kind of like basic factoring, or "modern" solutions like Pipe (https://www.pipe.com/).
There is an argument to be made that it disaligns Lambda and student incentives ("outcome it doesn't matter because Lambda already got paid") but long term, defaulting students would definitely decrease the sales values of the ISAs.
Part of the argument here is that calling these ISAs non-dischargable would lower the risk to the investor, which would make those ISAs worth significantly more.
> calling these ISAs non-dischargable would lower the risk to the investor, which would make those ISAs worth significantly more
I wonder how true this is. Declaring bankruptcy comes at a huge cost. It's not something people do lightly. Most people, if they have the money to pay back the loan, would probably do so. Once they're in that position (presumably with a stable job), they have little incentive to declare bankruptcy.
But that goes hand-in-hand with the aligned outcomes; it's best for Lambda if these people get good jobs afterwards, because they're more likely to pay the ISA in full.
If they don't, they're less likely, the risk is higher, and the ISA is worth less (to the investor).
The potential for bankrupcy is also a risk, which lowers the yield. Removing the risk of bankrupcy heightens it.
We could argue about the magnitute of these risks all day; I'm sure people significantly better at math than I am have done a lot of work here.
I've been critical of some of their comms before [1], but this statement seems pretty good. You seem to be working hard to read it in the worst possible light.
I don’t know, man. Hearing someone refer to layoffs as “right-sizing” leaves a really bad taste in my mouth, and I don’t think that’s an unfair reaction.
I think it is perfect because it assigns blame at the right place in my mind- the people that had no idea what they were doing and hired too many people in the first place.
Chill your hyperbolic outrage. We should be able to talk about sensitive topics like job loss intelligently and in context to the realities of building a startup. Working at a startup is inherently risky and the people who take on the risk are adults. They don't need you to defend them as if they're children
All of this is well taken, but IME, when you read a "perfect" statement about layoffs, it was the result of a process meant to generate exactly that. Which isn't really any more human or personable. This reads like Austen wrote it personally. Is that better or worse than an artisanally crafted statement by a team of PR people? Or worse -- no statement at all?
I'd expect that all of the clarifying questions you're looking for answers to are covered in internal Q&A's, at least that's how it has been done at every startup I've ever worked at.
To be fair, there are at least three distinct groups:
1) Employees being let go
2) Employees continuing
3) Students - current and prospective
Any statement has to balance the message across these three. For example, a statement that's "too negative" about future prospects risks spooking people in #3 - causing further damage to the business, as well as to people in #2.
Any former employees interested in speaking to a motivated reporter about their experience, please feel free to contact me. My contact information is on my account bio. I am happy to speak to you completely off the record, in any manner that makes you comfortable. I have never lost a source.
I'm kind of curious about what's making you follow up on the Lambda story a year since your initial reporting. Are you working on a longer story on the topic (a book?), or trying to build up a journalistic brand for yourself, or something else?
I don’t know who the author is but generally once a reporter becomes an expert in a controversial subject they follow it to the end. Look at Theranos and Enron.
No, I specifically wanted to know why Vincent Woo, founder of CoderPad and rookie investigative journalist, was staying on this story. Your generic answer is not what I was looking for.
"motivated reporter" Strange way to describe oneself as a reporter isn't it? Shouldn't a reporter's goal to be to follow the facts where ever they lead devoid of any particular motive?
Well, if you click through and look at the OPs work they have previously written an article about the company they describe as, "basically accused the school and its chief officer of fraud".
On the comment itself though it is looking for former employees and stressing the anonymous source angle. If I'm promising not to reveal the identities off the bat I'm clearly fishing for negative views on something. Have you ever read an article where an anonymous source said something glowingly positive about a subject?
I sort of agree that Lambda School is probably over selling itself but if I were looking to learn more about it in a balanced manner I would probably use more neutral language when soliciting information.
> In fact, many things at Lambda School are working very well, and we believe we're on the path to proving this model successful and sustainable.
While that may be true (doubt it), I work at a startup with about 150 employees and if we lost 32% of our employees I'd be polishing my CV and wondering when I'll be cut loose. That's not to mention all the negative stories coming out of Lambda School.
especially when you consider that at this point they raised 100 million in funding. For such a relatively small team thats a ton of money they are burning through.
That's too bad. I'm rooting for Lambda School, they are trying hard to innovate in the area of education. Maybe ISAs are the future, maybe not, but the traditional university track of spending out $100K+ is clearly not the future.
They should have been really excelling during the 2020 year of covid, I wonder where things went wrong. Anyone know?
I am a recent LS graduate. The program is a total shit show. I was able to get a job but I would never recommend it to anyone. They are just very good at marketing. Great idea, terrible execution.
I’ve been watching a housemate try the program, and it is so obviously terrible, I reached out to Austen on Twitter and offered to help.
He gave me an email address and then didn’t reply to some very specific examples I gave of obvious mistakes that are not based in cost or labor saving.
Anyway, why should he listen to me. Except that these are just elementary learning mistakes, and the whole thing appears to be run in a chaotic and unprofessional manner.
My friend will almost certainly quit. It is just so unfortunate because the premise is great.
There are solid programs that offer ISAs now. e.g. Hack Reactor. I appreciate what Lambda School has done for the industry (pioneering the ISA), but the execution has been a total disaster. Trying to scale a business that relies on services, and basically zero technology, is a very, very bad time.
But there are companies that have been successful with it. (General Assembly, App Academy is having some success, Hack Reactor is growing, Flatiron School, Thinkful, Spring Board)
Next generation of bootcamps will be much higher quality instruction based on technology. (See Fullstack Academy)
And they will be partnered with big FAANGs, or built in-house by FAANGs. (Again, Lambda pioneering)
Most code schools (including Lambda School) don't really train students effectively. It's closer to a crash course in a bunch of different topics. Students that graduate often aren't actually able to perform whatever they were supposed to have learned effectively on the job. Worse, to pad hiring stats, lots of code schools also will hire recent grads that cannot find employment as instructors. They technically aren't lying when they say "95% of graduates get a job within 6 months", but their statistics would be a lot worse if you looked at those hired _externally_.
Code schools are actually pretty gross. The idea is fine, but reality and greed kind of ruin them.
> Worse, to pad hiring stats, lots of code schools also will hire recent grads that cannot find employment as instructors. They technically aren't lying when they say "95% of graduates get a job within 6 months", but their statistics would be a lot worse if you looked at those hired _externally_.
I'm aware that many other bootcamps and programs have done this, and it is obviously extremely misleading.
I have not heard that Lambda has specifically done this; are you claiming that they are as well? Your comment kind of implies it, without outright saying it.
I got an entry level SWE position. A bunch of my peers have also landed really awesome positions. It's all about what you put into it, but Lambda School as a whole is just very chaotic. I think if executed properly it would be a pretty good option for people.
I have worked with and managed quite a few code school grads (in my city there are several). This matches my experience with them. You kind of just get force-fed a bunch of content, and if you're a person that does the (extra) work to retain that information, you have the opportunity to find success.
I have also observed that lots of code school grads move away from coding immediately on graduation. Lots of QA/PM types come from them (at least here). This is not necessarily a negative.
It took me ~4 months. I interviewed with a handful of really great companies and made it to the final round with 4. Companies are definitely willing to interview/hire self taught developers. LS career services overall were pretty useless, but there were a few staff members who helped give guidance and I am really grateful for that. Try and find someone in the field who can help mentor you.
I have made an effort to reach out to potential mentees, but the interaction falls apart when it becomes very clear that all they really need is for someone to give them a job or introduce them to someone who will give them a job.
My mentors helped me create my resume/portfolio, prep for interviews, find different resources, looked over my cover letters, put me in touch with their connections, and were just there for overall guidance and support.
That's helpful and reassuring, thank you. I'm hoping to start attending in person tech events/meetups soon. Hopefully people are excited to be able to meet new people again :)
Thank you!! And good luck :) I also recommend checking out apprenticeships. Asana, Twilio, and Twitter are just a few companies that have apprenticeships that specifically target self taught/bootcamp grads.
I'm self taught like you, my biggest regret is not sending out resumes earlier. Try to find small and midsize businesses they are the most receptive in my experience.
I don't really regret attending Lambda School because it gave me structure that I needed, but I just got super lucky. I joined right before they made a ton of terrible changes to the program. I would either attend a better/different bootcamp or self teach and start working on solo projects. I participated in a few different hackathons and learned a whole lot more than I did during my 9 months at Lambda School.
On average, no not really. I'd say that roughly 15-20% have found decent positions but it is really hard to say because some students just might not be very active on slack/linkedin.
I'd take a look at the subreddit r/LambdaSchool to read more about the changes (note that Austen the CEO is the only mod there and removes posts). One example is the removal of the Team Lead program (which kind of sucked in the first place because they didn't vet anyone) and replaced it with an auto-graded system so everyone was just on their own. There are students who went through the entire curriculum and didn't have a single person review their code. Ever. This is just one example of how chaotic it is.
As a student that experienced the changes you were talking about... Before the TLs were removed, my code was vetted by my TL (and she was a good one, thank goodness), but after the changes, not a single person vetted it. I could probably have sent DMs to various people to get some sort of code review, but as a standard process, it wasn't available anymore.
I went through the program much earlier than you did. Having a good TL (or PM as they used to call them) was the biggest difference between passing and not some units. I was one at one point too, and there are a lot of bad habits you need to get students out of by pointing out in reviews repeatedly.
I haven't had contact with anyone from the school since they exiled all the alumni last year. But before that I'd say at least 80% of my cohort got hired by 4 months out. Sad to hear how far it's fallen.
Not all degrees are 100k. Lots of STEM degrees pay themselves off. The real issue is the time commitment. Most code school students (IME) are career changers that can't afford to spend 2-4 years changing skillsets. Also, Lambda School caps ISA at 30k, which I would argue is insane for a 6 month fulltime program. To your 100k example, college is actually cheaper.
Yeah, maybe the ISA asks for too much. It is reasonable to find ways to align the incentives between students, schools, and employees. So in that way the ISA as a concept is plausible.
You can cherry pick, but with most random programs the value prop is much less obvious. And there are hundreds of them for every stand out program. In CS particularly you are probably ok right now, but it doesn’t generalize well, like your example.
[edit] if not clear, I’m not arguing post-secondary isn’t worthwhile, just picking a program like Waterloo isn’t good evidence.
I don't really agree - having a STEM degree from an accredited university gets you pretty far. Unless you're going to an elite school for a given field, it doesn't really matter what you pick. An elite school's degree is better than alternatives, but a STEM degree in general is better than not having one (for STEM industries).
Going to a mediocre state school for CS will pay itself off quickly, regardless of which school.
Aligning educational incentives is hard because no one wants to take the risks (a student who could be a 1x-10x developer or just wash out) and the costs (quality engineering talent that could be making serious money in industry) on.
Traditionally, you’d expect technology companies to have a pipeline to identify and cultivate junior talent (an apprenticeship). Instead, they rather pass the buck to leetcode and bootcamps. Quality engineers are expensive, so you have to operate your bootcamp similar to the traditional higher education TA model (low paid teaching the paying). Are ISAs predatory? Hard to say, depending on what you think of equity, free will, determinism, and “someone has to take the chance and pay for this.”
> Traditionally, you’d expect technology companies to have a pipeline to identify and cultivate junior talent. Instead, they rather pass the buck to leetcode and bootcamps.
Will they take you with zero engineering experience? Because my local trades union will take you from zero to an electrical journeyman (a friend of mine went through such a program and now runs a crew in California performing electrical infra overhauls). I am unaware of any engineering org that’ll take you right off the street to productive commits. If anything, everyone is looking for the cream of the crop.
I find this very interesting too. Firstly, let's be charitable and assume PG can look beyond any investment he has in judging this. I think he'd look at it then as some potholes on the way to an eventual smooth highway. He likes disruption of old, flawed ways. He likes motivated sorts doing the disrupting. Airbnb and Uber and the like are not without their own bumpy roads.
And the potholes here are lay-offs for people who otherwise saw employment for a period with Lambda. Or payments to Lambda that trigger only when you're making money yourself. He might think of those as reasonable downsides en route to a more polished, future Lambda service.
The model itself (pay if you earn) isn't outrageous. We had something similar (but government-based) with HECS in Australia, that's evolved a bit since.
why would PG care? His job was to get a bunch of hype for Lambda so their initial investment could be marked up to whatever the last raise put the company at. Austin is probably one of PG's favorite tech guys because he basically took a bootcamp, wrapped it in 1 legal nuance and with a fuck ton of twitter hype made PG/YC probably a 10x return.
Austin may have been the right guy to start the company, but IMHO, he should've handed it off to someone with a background in education a long time ago.
I don’t know Austin, but my general view is investors hunker down with their execs. And if they have to make hard decisions, they do it behind closed doors. Public fights with your holdings likely violates fiduciary duties.
This is a real testament to the failures of higher education.
The revenue-sharing once seemed "at least aligned" to me. But it's become clear that Lambda school is classic game theory at work, optimization without regard for process.
Because of this, I'm corned into thinking that education, like healthcare, is one of those things that should be off limits to profit seekers. And yes, that means having to do the hard work as a society and identifying how we can maximize education through common resources for a common good.
What amazes me even more is how this doublespeak has any effect at all anymore. Are there still people who don't see through this? Or are the execs in such a bubble they believe it themselves?
I wonder to what extent it has simply become a term of art. In biology, a specimen that is killed in the course of an experiment is described as "sacrificed" and has been for over a hundred years.
We expect certain words in a layoff press release, and so the writers of those press releases use those words.
To be fair, while I'm no "corporate simp", it is entirely possible that a company has hired too many people and needs to fire them to return to the "right size" for profitability to be sustained.
"Rightsizing" is a weird one, because it's occasionally used for benign stuff like reducing the rate of hiring. Much like "enhanced interrogation techniques," the broader meaning provides shelter for what it's really used for most of the time.
The traditional higher education is becoming more and more a scam. Federal government is backing almost all the student loans, but universities automatically increase their tuitions. The student loan crisis is a clear indication that this model is not working. For the past 20 years, in-state public university tuitions have increased more than 200% [1]. Now the total student loans count to approximately $1.6 trillion [2].
In the past, America had a tradition of wealthy people create new universities. Now we only have the rich donating to established universities, but no longer building new ones. Elite universities are all about powers, instead of educating more people, that's why they don't make efforts to expand their campus or create new campuses. Ever heard Harvard build a new campus in a different city? Or Yale acquire an not so successful private university to level to it up so that their success can spread out and more students can be benefit? No.
I cherish people who want to build a new model for higher / technical education. And wish them the best luck. There certainly will be trails and errors. And I hope them can power through.
My ISA with lambda is for $30k USD that I started paying after I landed a job. There is no additional tuition. Program is less than a year. That's significantly cheaper than any of the universities I could have gone to.
The trade off, in my mind, is that I didn't become a SWE in lambda. Engineering is far more broad than learning how to build a CRUD app. Since Lambda, my self appointed curriculum has been engineering fundamentals, design patters/language/etc. Knowing how to write a BST is a lot different than understanding the core concepts behind everyday algos.
> Federal government is backing almost all the student loans, but universities automatically increase their tuitions.
On the condition of getting the federal student loan is that the loan is exempts from bankruptcy. The loans follow us to the death or until it is paid off. It actively screws those naive students until to the point of no return. I have a friend who is still stuck paying the loan more than 15 years because the wage are not above the cost of living.
This is a huge legal binding part caused a lot of them instantly debts trap while the universities keep pushing the envelopes with the tuitions without oversight
Edit: But there are federal loan forgiveness programs. They have many of them. If your degree in your career and it end up into career ending situation, they have a program for this. However, I don't know if those program changed due to DeVos involved meddlings with various programs.
On the other hand, this sort of needs to exist on some level for two reasons:
1. If you go bankrupt, most stuff can be taken away if you can't pay for it. I can take your car, house, etc, but I can't take you education. You still have the full and complete value of that, so I'm not sure it's obvious that bankruptcy should remove the debt.
2. When you graduate, you typically haven't really got any money yet. It would be very easy for recent grads to just go bankrupt before they start accumulating wealth, and then go on with their lives without the loan debt.
The goal of bankruptcy laws is not to rob you blind, but to find a mutually acceptable way out of a bad situation. At least in the US, most bankruptcy is civil bankruptcy. There's no element of criminal justice at play. It's about what's practical, not what's fair.
It shouldn't need to be said, and yet it appears that it does, that bankruptcy is not without consequences for me, even if you fail to rob me blind in the process. A bankruptcy will limit my ability to rent an apartment, get a car loan, or win certain jobs. There is no story of people scamming bankruptcy in the US, not even in the recent past when educational debt was still dischargeable.
You should look up bankruptcy laws. They can probably take your house, but oftentimes debtors reaffirm their mortgage to avoid that scenario. You can keep one car used as transportation to and from work, and like 90% of your personal property unless you were already loaded. It really only forces you to sell off assets. You have to meet specific discretionary income to debt forumulae to apply, which locks a lot of people out of bankruptcy as an option.
Your second point is fair, though. That's why student debt shouldn't be a thing.
Anyone with knowledge know if this is isolated to lambda falling behind their peers, or the industry itself starting to turn away from hiring boot camp grads?
In my experience, the signal to noise ratio for bootcamp is very low.
You might get a diamond in the rough sometimes but it's so inefficient compared to a hiring pipeline from real schools. Lambda now has a program where you can "try" one of their graduates for a few weeks free of charge. Contrast that with the signing bonuses you see negotiated at serious career fairs... That should give you an idea how much people trust Lambda's grads.
> Lambda now has a program where you can "try" one of their graduates for a few weeks free of charge.
Any idea how this works? It seems like it would be difficult to have such a program without running afoul of labor laws. Does Lambda have the equivalent of a temp staffing agency subsidiary to facilitate this program?
I am about to start with a company through the Fellowship program on Monday. There is a staffing agency that we are paid through. The pay isn't phenomenal, but it is a buck or two above a living wage for the state you reside in. If the company decides they like us and want to keep us on, then they reimburse Lambda for what they paid us during the Fellowship. We aren't locked into the Lambda pay rate if we are hired, and are encouraged to negotiate our offers. There is supposed to be an actual position open for hire in order for organizations to take advantage of the program, but obviously that could be exploited. I am cautiously optimistic, though.
Good question. I didn't really pry into other's situations. I was told compensation was based on state, and I really only know my own extremely well. I will tell you Bernie Sanders would approve, and then some, but for my particular area of the state (where most of us live, figures) it is probably deficient by about 3 dollars.
When it was first announced, the students weren’t paid. There was massive outcry on Twitter and elsewhere that this was an illegal violation of labor laws so Austen changed his tune and said Lambda would pay the students minimum wage.
There are so many shady, obvious blunders like this when it comes to Lambda School
I think it is an industry moving away from boot camp grads. Almost all these boot camps focused on JavaScript and HTML/CSS.
None of them were really teaching Linux sysadmin, clustering, scaling, etc. The real world needs developers that understand more than just frontend development.
There are plenty of bootcamp grad alumni from the ~2013 era that are now senior and staff level engineers. Any company that wants to be stuck up about university degrees is missing out on a lot of potentially strong contributors. The bootcampers I know haven't had any trouble getting hired.
I've worked at coding bootcamps in the past... Most camps are closer to 50% job to not job ratio now. Most camps that have tried to scale quickly have failed. It's not easy to scale education.
> Lambda pioneered the income share agreement (ISA) in the career
> and technical education space, and we're now watching many of
> the schools who followed us abandon their ISA offerings because
> they believe it's impossible to make this model work. We strongly
> disagree. In fact, many things at Lambda School are working very
> well, and we are...
> ...laying off 65 Lambda School employees.
I am about to start with a company through the Fellowship program on Monday. There is a staffing agency that we are paid through. The pay isn't phenomenal, but it is a buck or two above a living wage for the state you reside in. If the company decides they like us and want to keep us on, then they reimburse Lambda for what they paid us during the Fellowship. We aren't locked into the Lambda pay rate if we are hired, and are encouraged to negotiate our offers. There is supposed to be an actual position open for hire in order for organizations to take advantage of the program, but obviously that could be exploited. I am cautiously optimistic, though.
> One immediate change, however, is that we are pausing new enrollment in our part-time programs.
This all surprised me a little since I imagined schools like this would be doing well, pandemic job losses and expanded unemployment ought to make for a great market of prospective students. I guess it hasn’t been that simple.
I think the problem is that their part time outcomes were much smaller than full time. And it wasn't entirely an instructor or curriculum related problem.
You are correct that schools like this (secondary/trade, not ISA, necessarily) do well during economic downturns, historically.
My (limited) experience tutoring at another code school resonates with this opinion. It would be great to see a high rate of positive outcomes from part-time students. Many of them simply had too many other priorities to focus on making their coursework successful.
Kind of weird seeing this after following Austen on Twitter for so long, he is always bragging on there about how well they’re doing. I guess things aren’t always as great as they seem!
Reading this feels a bit unfortunate, I was looking forward to making a post on next week's 'Who's Hiring May 2021' thread having finished their data science curriculum recently then polishing up my resume & personal site over the weekend. Going through a fully remote bootcamp backed by YC seemed like an excellent fit to be done in 6 months right around when second wave covid was about to hit, having been doing Advent of Code & Hackerrank challenges in Python for the better part of a year and was excited at the opportunity to formalize in-demand skills combining data analysis and programming.
While I think Lambda is doing a fairly decent job with the curriculum on the data science side (having been condensed from 9 -> 6 months); the job market has doubled down on being competitive through hard skills while the school has doubled down on training students in soft skills, and isn't providing enough resources to upskill before reaching the ceiling of becoming a mid-level software engineer within a few years of landing their first job. Some of the curriculum especially unit 3 felt like a chore where I had a lot of trouble with how quickly material was being crammed in, with almost no regard to ensure understanding of the previous day's. The forced mentor/mentee relationships and track teams also felt like I'd gotten zero value-add from, and Lambda continues to have the pitfall of no student accountability whilst passing forward multiple students who didn't contribute a single line of code to our build week projects that will need to be fully redone if showing them off as portfolio pieces.
This lack of being thorough and due diligence coupled with seeing recent posts in the #hired channel receive lower salary offers seems to have confirmed that not enough is being done to address the skills gap between college grads and the lousy reputation bootcamp grads have been getting as of lately. Having maxed out the amount I'm able to borrow in student loans & exceeding the max number of credits taken during my time at university to be eligible for a degree, a non-traditional education approach like Lambda really appealed to me and I wish the staff and school the best of success going forwards. Hopefully the Amazon partnership will be a stepping stone for big tech to tell Lambda and the broader hiring market what they want from junior developers in an ever-tightening labor market. The CS and labs units felt like an excellent experience and the students in our labs project have been valuable to work directly with on a real-world software engineering application.
Disclosure: I have no contractual agreements with an ISA.
Can agree with you 100% about Unit 3 in the data science track.
I know a few of my peers that walked out without even setting up their local development (because they were on windows and there was little help or instruction for students with windows).
But I'm glad you got something good out of CS and Labs. I'd describe CS as neutral for me... It was just grinding CodeSignal. Labs was extremely disorganized and suffered from a lack of leadership and its sudden shortened length (from 2 months to 1 month).
“ I know a few of my peers that walked out without even setting up their local development (because they were on windows and there was little help or instruction for students with windows).”
I remember the pains in undergrad of trying to get tensorflow to correctly make use the gpu on a windows machine. Not fun!
Anyways, not trying to be rude here, but this is pretty concerning to read. If someone needs to have their handheld through installs then I’m not sure how effective I’d ever expect them to be in any autonomous role with any ambiguity (hint: data science requires both). Even more concerning if this is at a later stage of the curriculum.
It's in the third month of the curriculum, after primarily only working with Google Colab in the previous two.
Keep in mind that a majority of the students have never seen or touched a command-line, and trying to install various tools resulted in a lot of errors for a lot of students, troubleshooted by TLs that were only students 2+ months ahead.
Right, that all makes sense, but you can’t join a company just to say, “well only 6 months ago I’d never touched a command-line!”. I know plenty who have succeeded from boot camps and plenty who haven’t. I think the best thing boot camps can do is have a stronger acceptance criteria - letting anyone in just sets them (and the brand’s reputation) up for failure. But I guess they need to make money.
I’m not sure how lambda could help with the jump to mid level engineer. That’s up to you once you start at a job... the main issue I see is that one has to compete with actually scientists (PhDs) that have many years of formal education and work on addressing hard problems.
Also, exceeding the max number of credit hours while at university is a mistake. Classes should only be a part of a university experience. Good students takes advantage of the other opportunities such and internships and working in research groups. If you want more education then go to grad school.
My last memory of Lambda School was Austin blocking me on Twitter after asking him why every dev bootcamp grad I’ve ever hired is now on my product team.
The implication is that good developers don’t need bootcamps as much as potential product managers need them for crash courses on development fundamentals. I’ve lived this personally as an actual founder of a non profit dev bootcamp.
How do you manage anyone with that level of lack of self awareness? Yea, of course prospective product managers will go to bootcamps to learn the technicals.
And why would "good developers" go to a bootcamp? That would defeat the entire purpose.
Think smarter.
And if you actually think coding bootcamps don't work, just take a fking look at the alumni...
at the time I had 15 product managers, five of which had come to me as entry level developers out of Flat Iron and some course in Boston I can’t remember the name of. I had a Ruby dev bootcamp grad switch to my finance team as controller (he was also a CPA). He manages timber leases now.
Do they themselves hire folks they train? This is akin to Byju's in India with WhiteHatJr coding classes for kids by milking the parents fear of not wanting their kids to be left out. That monster ended up unleashing a fake kid to be used in their Ads & launching completely useless & random app(which they themselves create & pass off to parents) onto play stores & boasting about it with Twitter a/c's of kids who are not old enough to even register an account. The only shade that they hide behind is that they hire only women teachers, nobody out here is denigrating on that point yet they try to make that the case by hiring sub-par faculties.
Doesn't seem _too_ surprising, given how many part-time programs (ie, Coursera) have struggled to retain users distracted by jobs and life.
I hope they figure it out. 4-year university education for everyone is a broken model, and students deserve a cheaper path that doesn't eat half a decade of their life.
I feel that immersive programs are the way to go. Teach each subject in 3 month chunks. Candidate can continue to apply for jobs with the skills they have learnt. Let's say they have acquired enough skills to find jobs at the 3rd, 6th or 9th month, they can leave the program. This compulsion to finish 4 years is nonsense.
Agree. I used to be against the technical interview process, but now I wholeheartedly believe in every facet of it (Leetcode, book knowledge, project based behavioral questions, system design).
The quicker everyone learns these important concepts, non of which are taught in school or in bootcamps, the quicker one gets to be productive in programming.
While some of the leetcode questions have tedious edge cases and ambiguous logic, there is no doubt in my mind that lists, trees, arrays, queues, maps, graphs, sorting, binary searching, and memoization are crucial for being an amazing programmer
If you truly believe that the measure of amazing programmer is churning out bullshit exercises on a timed call, I can only state that I'm thankful we don't work together. It's incredibly audacious gate-keeping and I'd dare say, discriminatory. It ignores disabilities, social adjustment conditions, etc that wouldn't impact daily work.
I know too many incredible engineers that don't think in the way that those timed exercises require, and these folks are wildly talented and wildly successful.
I do agree with you that the system blocks more people than it should. Personally, I think there should be many more apprenticeships than there are currently. There are many technical roles that can be fulfilled by people of all backgrounds.
But, for the most serious roles, there needs to be skill scrutiny and performance under pressure.
Think of the difference between a performing artist and a music teacher. Both are vital roles and have related but distinct skill sets. The performing artist must have everything drilled and ready to go on stage, much like a heart surgeon shouldn't be doing research on the operating table.
I won't have a great answer since I have no experience in the area. But I would probably ask them to repair something, leave them alone and come back in 2 hours. Pick the mechanic that repaired it the best. I would not stare over their shoulder while they worked.
For the music teacher I would have them record the piece, and send in the best take. I wouldn't demand they get on stage and play the piece.
Are we going to ask a symphonic performer to produce scales during an interview, a heart surgeon to suture a laceration? We would not, it would be insulting for a myriad of reasons.
I don't think you quite appreciate the volume of utterly incompetent candidates (with plausible resumes) companies get for every software dev opening. It's a deluge, and no company on earth can deal with interviewing them all.
The only realistic alternative to hackerrank is to do strict university filtering, only interviewing candidates who come from ranked universities. This is obviously terrible.
Hackerrank, even though it annoys you, is an ENORMOUS leveling of the playing field, giving opportunities to people who didn't have the money or connections or whatever to make it into a top CS program.
If you aren't those people, you don't understand what doors it opens.
I'm not sure you appreciate the volume of utterly incompetent interviewers and processes now out there. I have 20 years of experience, a sizable catalog of open source work available for browsing, and yet even at this level I'm subjected to the nonsense that is leetcode.
Another beef - I have yet to see an exercise which is remotely relevant to the work being done. Shuffling arrays, regurgitating interview preparation exercises, mutating objects, all-out silly exercises like "convert this number to a roman numeral" don't measure a candidate.
So where do you set the bar? This goes being annoying - leetcode exercises are pure laziness and a red flag of an inept hiring process for all but the least experienced candidates. The job market solved this problem eons ago - references, references, references. If you have none and you have no portfolio on which to measure your ability, you fall below the bar and sure, then I'll concede that some measure of basic ability is necessary. No one asks for, calls, or contacts references anymore (we do). Want to know if someone is capable? Talk to the people they've been working with. Scrutinize references for obvious softballs. If you actually care about the person you want to work with, and think you want to work with someone you respect, put a modicum of effort in.
We've shifted to using tiered collaborative "projects" that we book an hour to two hours to work through with a candidate. We give them the ability to review what we'll be working on together, in detail, before the call so that they can prepare. It's also _not_ about finishing, there is no finish line, it's about collaboration, and we tell the candidates that. We've had many candidates state this was being their ability, and see themselves out. Thats FAR more respectful of a candidate than leetcode for someone who's spent 10 years delivering microservices for a profitable business. It's also far more representative of the work they'll be asked to do:
- Being given a description of work
- Pairing or collaborating
- Stating what they do and do not know
- Researching a thing
I'd hire someone that failed an array shuffling algo bullshit leetcode challnge, but worked very well with a small group for an hour, and was able to deliver good answers and make progress on a task.
> Hackerrank, even though it annoys you, is an ENORMOUS leveling of the playing field, giving opportunities to people who didn't have the money or connections or whatever to make it into a top CS program.
I hope I've made it clear that this has nothing to do with leveling the playing field, and nothing to do with getting into CS programs. I mentioned prior that I have 20 years of experience professionally, no degree, and the last 4 people I've recommended for hire had only one prior position, were bootcamp grads (one self-taught), and are excelling in every area. They're wonderful, motivated people. What Hackerrank DOES do is shift the burden of actually caring about the people you're speaking to. If volume is an issue, the process is BROKEN.
Your interview process is not far from the ones I've been involved in and I think you make a number of compelling points. The onus remains on the hiring folks to actually put in the work required to follow a sane process. I don't understand why you're blaming these platforms for teams having bad interviewing practices.
There's nothing wrong with hackerrank-esque platforms themselves. They're actually pretty sweet if you want to work through a couple narrowly scoped problems and get instant feedback while picking up a new language. There are even some diamonds in the rough that are actually decent multi-stage problems you can work through with a candidate during an interview. They make data structures and general code literacy more accessible to aspiring programmers, which is not a Bad Thing.
If hiring teams are attempting (and failing miserably) to use these platforms as a shortcut, that's on them. Their system may be busted, but coding sandboxes aren't forcing teams to not put effort into their hiring processes; that's entirely up to the folks making decisions.
> I don't understand why you're blaming these platforms for teams having bad interviewing practices.
Never said that I did. I said they were the worst thing to happen to tech, and elaborated that it's because of their overuse and abuse in hiring. Many good things start with good intentions and are abused.
The sad fact that Leetcode is still a required part of the interview process for a developer with 20 years of experience shows that there is no social trust in this industry. Company B has no faith that Company A employs competent developers. That needs to change. Perhaps what is needed is a standard competency that everyone in the network agrees to.
“ We're trying to do something that hasn't been done before – prove that an accessible, incentive-aligned education model can work sustainably for all students willing to put in the work. ”
“Willing to put in the work” shows me that Austen is already rationalizing his failure in terms of his indentured servants —- I mean students — not working hard enough.
The tech prerequisite is: "You should feel extremely comfortable with how a computer works including: touch-typing, web browsers, search engines, and basic computer programs."
The Curriculum:
Stats, Linear Algebra AND Data Wrangling: 4 Weeks
Modeling: 3 Weeks
Data Engineering, Databases, SQL, Productionisation: 3 Weeks
Machine Learning, NLP, Neural Networks: 3 Weeks
Python, OOP, Algorithms & General CS: 3 Weeks
Project work: 3 Weeks
At that pace over so many subject is anyone walking out able to remember what a dot product or a t-test is from week 1?