[educat3]

I'm very intrigued by a lot of the new education opportunities coming out of YC. Lambda have highlighted a few concerns with this model, which I think you mostly address with selective admission, course specialism and experience, that said I have a few (Lambda inspired) questions:

1. The total cost (5k direct[1], 9k ISA) is significantly cheaper than Lambda: do you see that as a reflection on the cost sensitivity of the European market or is there a reason your model allows for lower-cost execution?

2. The ISA model gives the school a vested interest in the career outcomes of students, but Lambda has highlighted that ISAs can become a debt product that is resold: do you see that model (of reselling ISAs) as part of Turing College's future?

3. Lambda has had problems with new courses being unable to deliver on the promised quality: how do you see the next few years of growth for Turing, do you expect to introduce new courses based on demand or do you expect to build out new curriculums (and test with whole cohorts) before introducing them?

4. What's the relationship between supervisors and Turing? The team page notes that these people are part-time with Turing: how do you ensure that they're able to deliver the valuable mentorship required by students? Are they volunteering? Paid per hour? How does their mentorship accommodate students that require more support than average? Does their commitment to Turing come before their work, with their employers supporting?

Very promising proposition, team etc: very interested to hear answers to the above to better understand how you are approaching the more challenging aspects.

[1] I'm an employed Software Engineer but at that price it's very tempting to enroll to build out my data science skills.

[hintymad]

I feel that the challenge of Lambda school, as well as other vocational schools, is that their courses are too specific for too short a period with too easy assignments. Take this curriculum for example: https://lambdaschool.com/courses/data-science#curriculum. Stats fundamentals: 4 weeks. Predictive modeling: 4 weeks. Etc etc. I mean, really, 4 weeks for stats? Will the students have enough time to learn fundamentals on counting with such short time？Will they truly learn what a random variable is and why that matters? Will they learn what joint probability distribution is, what test of hypothesis is, and what pdf, cdf, and pmf are? Will they learn what an unbiased estimation is? All these concepts do not even scratch the surface of real data science work. Unfortunately one will not be able advance further without firmly grasping these concepts. And I'm just talking about problems at undergraduate-level. In addition, can we realistically ask the students to work on moderately challenging assignments given such a short time? If they can't, why would I, as a hiring manager, risk my team to hire a graduate from such schools? One may argue that a motivated and smart student can overcome such obstacles and get her foot in the door by attending such schools. But then the challenge is flipped: not many such students need to attend such schools.

[catwind7]

> One may argue that a motivated and smart student can overcome such obstacles and get her foot in the door by attending such schools. But then the challenge is flipped: not many such students need to attend such schools.

i guess it question is is there enough such students who can overcome these obstacles who are also willing to fork over X tuition? they may not need it per-se, but I think you may be discounting the value of the pre-existing networking leverage these schools have over individuals that may have no network in data science related work

[tmoska]

Great questions! I’ll go through them one by one:

1. You are correct to say that our pricing reflects the European market and is naturally different from schools in other markets. In addition to that, we are a self-paced school and have no actual classes happening (work-like learning). Our students get projects via our platform, attend daily standups and receive (from mentors & peers) & do (to peers) code reviews. This means we have no full-time teaching staff, and that’s a huge cost-saver. Instead, we spend more effort on our underlying tech & mentors, which results in a lower variable cost per student. Important to note that even though there are no classes, our students still receive a lot of contact hours with industry experts through code reviews, help sessions, standups, etc. These hours are focused on helping our students (2-way), not teaching them (1-way) - just like you would have it in any workplace.

2. We don’t have plans to resell ISAs. We see ways to be a successful school without taking this path.

3. We believe in being focused, and Data Science[1] will continue to be our main focus. We do not plan to start any courses in other fields.

4. Our mentors (Senior Team Leads) team is hired by Turing College and is paid by the hour. We do have agreements with each of them about their weekly involvement and each of them is managing their time themselves. Because of our education model, STLs don’t have to always be available at specific hours. They mark themselves available at specific hours and we have ways to assure consistent availability coverage for help & code reviews. Because of this, our students can get code reviews any time of the week, including weekends. As for students who require more attention, that’s completely fine, and they receive help from our staff, mentors, and other peers.

[1] https://blog.turingcollege.com/data-science-job-roles-explai...

[educat3]

Thank you very much for the comprehensive answers, very insightful and reassuring about the future of Turing. Hopefully I’ll see this all in practice in September :-)