Launch HN: Turing College (YC W21) – Online data science school
62 comments
Nice, thank you for making education an affordable option for people who don't have money for colleges.
My piece of a feedback. "Good data scientists are in huge demand" -- that's what everyone says. But I believe "good data scientists" are usually PhDs with a few years of experience.
I don't want to say your program isn't good enough. I think if you can educate someone to have an entry Data Science job in 6 months then it's a great success. By "entry DS job" I mean a real DS job: not Excel munging.
Your 6 months course seems like a bit of everything: which is fine because it's an entry course. But it's not much different from any other entry-level course. What differs you?
My piece of a feedback. "Good data scientists are in huge demand" -- that's what everyone says. But I believe "good data scientists" are usually PhDs with a few years of experience.
I don't want to say your program isn't good enough. I think if you can educate someone to have an entry Data Science job in 6 months then it's a great success. By "entry DS job" I mean a real DS job: not Excel munging.
Your 6 months course seems like a bit of everything: which is fine because it's an entry course. But it's not much different from any other entry-level course. What differs you?
To add to this: for someone who has never done any data science, how would you go from zero knowledge to machine learning, for instance?
This would require learning calculus, then statistics & probability, then linear regressions & generalized linear models, then machine learning(NLP, machine vision and/or AI). I’m not sure how someone can learn all of that in one year let alone 6 months with no prior experience in the field.
I can sort of see it being done but it would have to be very superficial knowledge which is sort of useless when your trying to recreate some paper that uses advanced stats and math. You could maybe recreate the paper but you would essentially being shooting in the dark and you wouldn’t really know the limitations of the model in any meaningful way.
Im curious to know how they will address this?
I don’t think you need a PhD in statistics or machine learning to be a good data scientist, but I’m just having a hard time believing all the skills can be learned in less than one year without any experience.
This would require learning calculus, then statistics & probability, then linear regressions & generalized linear models, then machine learning(NLP, machine vision and/or AI). I’m not sure how someone can learn all of that in one year let alone 6 months with no prior experience in the field.
I can sort of see it being done but it would have to be very superficial knowledge which is sort of useless when your trying to recreate some paper that uses advanced stats and math. You could maybe recreate the paper but you would essentially being shooting in the dark and you wouldn’t really know the limitations of the model in any meaningful way.
Im curious to know how they will address this?
I don’t think you need a PhD in statistics or machine learning to be a good data scientist, but I’m just having a hard time believing all the skills can be learned in less than one year without any experience.
The prereqs for ML are the same for most STEM courses. Despite the popularity of CS, there are plenty of graduates in legacy engineering fields like mechanical and electrical who are more than equipped to be on boarded quickly via a bootcamp model. They have the knowledge (and usually know their way around basic Python and MATLAB) but lacks the real world software engineering experience and a developer's mindset.
I love to how you refer to electrical and mechanical engineering as "legacy".
It's rare these days to meet a traditional engineering grad who is not desperately trying to switch to CS. The job market is just not there for pure hardware careers in big cities.
Agree. Market is overloaded with Data Scientists with a 6-12 months courses.
Thanks for questions. Almost all of our students have prior knowledge of university-level mathematics or did some coding, data courses on the internet. Of course, we have several students without such knowledge; nevertheless, they are fast learners. We test this in our admissions and in the very first month of learning in Turing College. If students are performing poorly, we terminate their contracts. In this way, we keep only motivated and determined students in Turing College and ensure that they would get a decent market salary. From the employers' perspective - just a few entry data science positions require independently "recreate some paper that uses advanced stats." Our students become a part of established data science teams, where some do more data analysis while others data engineering. As well as our base curriculum is more generic, our students get concrete, hands-on skills relevant specifically for hiring partners in specialization modules, which companies themselves create. When students finish these modules, they have a strong understanding of the company's business problem and tech stack, which is a competitive advantage over other candidates.
The thing with data science that it is a pretty new field, and data scientist tasks differ from company to company. Partnerships with companies help us understand the maturity of data science in every company and prepare students accordingly.
The thing with data science that it is a pretty new field, and data scientist tasks differ from company to company. Partnerships with companies help us understand the maturity of data science in every company and prepare students accordingly.
If students are performing poorly, we terminate their contracts.
What do you mean by this exactly? You are school, right? It’s not possible you are not teaching them right? Should a person not have the right to fail the course from start to finish?
What do you mean by this exactly? You are school, right? It’s not possible you are not teaching them right? Should a person not have the right to fail the course from start to finish?
Everyone surely has the right to fail projects & work on their improvements - that's a large part of learning. To clarify Lukas' point, we are constantly in contact with every single of our students and are making sure that Turing College is bringing value to them. In cases when students are progressing extremely slowly because of some factors, we have a process through which we inform students of our concerns and start having more 1on1s to help & support them.
The reason we are doing this is that at a certain point knowledge starts to "fade away" and even if you are in the middle of a course, you might start forgetting things you learned at the start of it. That said, self-paced learning is unfortunately not something that works for everybody. Our students get to try it out in our admissions process & demo month[1] to cancel free of charge before they commit.
[1] https://www.turingcollege.com/faq/what-is-the-demo-period-an...
The reason we are doing this is that at a certain point knowledge starts to "fade away" and even if you are in the middle of a course, you might start forgetting things you learned at the start of it. That said, self-paced learning is unfortunately not something that works for everybody. Our students get to try it out in our admissions process & demo month[1] to cancel free of charge before they commit.
[1] https://www.turingcollege.com/faq/what-is-the-demo-period-an...
I think this is a valid line of questioning but personally I'm not sure everyone should "have the right to fail the course from start to finish" on the basis that it can be a severe drain on resources and perhaps even unfair to the person falling behind. If the work is highly collaborative then the person who fell behind becomes a burden for their peers. If that is not a concern you will still get people that either squeak through without flourishing (reducing the overall quality of your graduates), or people that realistically did not have a chance from the outset that then get denied at the finish line.
For the purpose of trying to determine what is or is not fair I don't think it matters whose fault this is (a failure of the school's pedagogy or of the student's ability to keep up) because I think allowing the failure state to continue is fundamentally unfair.
For the purpose of trying to determine what is or is not fair I don't think it matters whose fault this is (a failure of the school's pedagogy or of the student's ability to keep up) because I think allowing the failure state to continue is fundamentally unfair.
But I believe "good data scientists" are usually PhDs with a few years of experience.
This is the same argument that says only people with CS degrees can be good software engineers. That is provably wrong because there are good software engineers who don't have CS degrees. Good data scientists are just people who can do data science well. That's the only valid measure. Having a PhD and some years of experience increases the likelihood of that being the case but having those things doesn't automatically mean someone is good, and not having them doesn't automatically mean someone is bad.
This is the same argument that says only people with CS degrees can be good software engineers. That is provably wrong because there are good software engineers who don't have CS degrees. Good data scientists are just people who can do data science well. That's the only valid measure. Having a PhD and some years of experience increases the likelihood of that being the case but having those things doesn't automatically mean someone is good, and not having them doesn't automatically mean someone is bad.
Several things make us different:
1) Our program is self-paced, while almost all bootcamps have a strict learning schedule. We ensure accountability through daily meetups, 1on1, and project assessments. Turing College suits people, who work full-time and want to upskill part-time by flexible schedule. As we have platform, which organizes all the learning activities for students, project-assessments happen from Monday-Sunday from early morning to late evening without our operational staff involvement.
2) Students spend 1/3 of their learning time on hiring partners' projects. Companies create projects that reflect their business problem and tech stack. Thus, our students are more ready to deliver from day one than other bootcamp alumni.
3) Students learn everything through practice. I know this could sound cliche as many bootcamps claim to do that, but in reality, it is just happening on paper. We have a custom platform that organizes curriculum into data science projects, which each of them should be assessed by a minimum of three people. To finish Turing College, students need to complete 20 projects that should be peer-reviewed by Senior Data Scientists and peers. Through this process, students get a lot of feedback about their learning and overall performance. They also need to assess at least 20 projects to finish the course - by this, they are pushed to learn to examine other's work, which is crucial in every technical position.
4) Students have personal development programs to improve their habits, communication skills, critical thinking, etc. Many employers' problems are related to the human factor, so we focus on shaping essential soft-skills parts.
1) Our program is self-paced, while almost all bootcamps have a strict learning schedule. We ensure accountability through daily meetups, 1on1, and project assessments. Turing College suits people, who work full-time and want to upskill part-time by flexible schedule. As we have platform, which organizes all the learning activities for students, project-assessments happen from Monday-Sunday from early morning to late evening without our operational staff involvement.
2) Students spend 1/3 of their learning time on hiring partners' projects. Companies create projects that reflect their business problem and tech stack. Thus, our students are more ready to deliver from day one than other bootcamp alumni.
3) Students learn everything through practice. I know this could sound cliche as many bootcamps claim to do that, but in reality, it is just happening on paper. We have a custom platform that organizes curriculum into data science projects, which each of them should be assessed by a minimum of three people. To finish Turing College, students need to complete 20 projects that should be peer-reviewed by Senior Data Scientists and peers. Through this process, students get a lot of feedback about their learning and overall performance. They also need to assess at least 20 projects to finish the course - by this, they are pushed to learn to examine other's work, which is crucial in every technical position.
4) Students have personal development programs to improve their habits, communication skills, critical thinking, etc. Many employers' problems are related to the human factor, so we focus on shaping essential soft-skills parts.
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.
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.
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.
> 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
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
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...
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...
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 :-)
YC recently invested in another European-based AI/data science online school, Strive School [0]. What are your main differentiators compared to them?
edit: Also, can you elaborate on this: "We're cheaper than Lambda school because we have optimized-focused learning platform & education processes. With that we can have 70% less staff to deliver the same and even higher education quality"?
Just want to understand your value propositon better as I am seriously considering to attend a bootcamp like yours.
[0]: https://strive.school
edit: Also, can you elaborate on this: "We're cheaper than Lambda school because we have optimized-focused learning platform & education processes. With that we can have 70% less staff to deliver the same and even higher education quality"?
Just want to understand your value propositon better as I am seriously considering to attend a bootcamp like yours.
[0]: https://strive.school
Hi, thanks, great questions!
We give a lot more flexibility compared to the Strive School. There are no lectures or schedules at Turing College, and you can progress at your speed by unlocking each new part of the course on our digital learning platform. So we target a wider audience of people from software engineers who want to upskill (this isn't the case for Strive school as they don't provide such flexibility) and people who requalify to data science.
Also, our curriculum is co-created with tech companies we partner with as our Hiring Partners (we have 17 partners now). So, we know what those companies are looking for in new hires, and we adapt our curriculum accordingly; we also have their commitment to hiring our grads with our job placement program. Strive school doesn't do that.
If I understand correctly, Strive School curriculum is mostly presented in video recordings. With us, you'll be a part of our tight-knit community of peers and industry professionals, interacting daily via online calls or discord chats and working on real-world projects that our Hiring Partners are now solving.
As for the "70% less staff to deliver the same and even higher education quality ", this is enabled by letting students assess each other's work with the supervision of Senior Data Scientists. It means that we don't need senior staff for every project assessment but only for crucial ones. So by having the platform that organizes assessments in that way, we ensure the quality and the need of less senior staff. We can track how students are assessing each other, and they are doing that objectively. With our learning platform, which follows that we can react to any cheating situation instantly.
Also, our curriculum is co-created with tech companies we partner with as our Hiring Partners (we have 17 partners now). So, we know what those companies are looking for in new hires, and we adapt our curriculum accordingly; we also have their commitment to hiring our grads with our job placement program. Strive school doesn't do that.
If I understand correctly, Strive School curriculum is mostly presented in video recordings. With us, you'll be a part of our tight-knit community of peers and industry professionals, interacting daily via online calls or discord chats and working on real-world projects that our Hiring Partners are now solving.
As for the "70% less staff to deliver the same and even higher education quality ", this is enabled by letting students assess each other's work with the supervision of Senior Data Scientists. It means that we don't need senior staff for every project assessment but only for crucial ones. So by having the platform that organizes assessments in that way, we ensure the quality and the need of less senior staff. We can track how students are assessing each other, and they are doing that objectively. With our learning platform, which follows that we can react to any cheating situation instantly.
Hey Lukas (and Benas and Tomas) congratulations on the launch! Always cool to see stuff happening in this space - from your comment it seems self paced but the website shows a cohort starting soon. How does that work exactly? For pricing, it looks like it's 9,000 euro total (with an ISA) or 5k upfront, is that right and are ISAs available globally or just in Europe)? How are you coming in so much cheaper than lambda school? Will you ever sell the ISAs?
Hey, ISAs are only possible for EU citizens for now. We're cheaper than Lambda school because we have optimized-focused learning platform & education processes. With that we can have 70% less staff to deliver the same and even higher education quality. We don't have plans to sell ISAs.
Got it, very cool! For any students that want to freelance after they graduate, feel free to have them ping me, [email protected] (or apply directly on the site). We mainly use more experienced folks but I'm sure there are some projects where an enthusiastic new data scientist would be useful
Edit: I'm also really glad you're not selling ISAs. To me the direct connection/ownership of the debt is what really aligns the parties, not the ISA itself. Once it's sold off to a third party debt collection agency of course there is still some alignment but I think the connection gets fuzzier.
Edit: I'm also really glad you're not selling ISAs. To me the direct connection/ownership of the debt is what really aligns the parties, not the ISA itself. Once it's sold off to a third party debt collection agency of course there is still some alignment but I think the connection gets fuzzier.
Sounds amazing! Thanks, we will share this with our students.
Have the hype and bootcamps moved on from programming to data science?
Can we have a normal profession once again?
Are the frauds all going to start calling themselves data scientists now?
I would love that.
I just went through 5 days of whiteboard interviews for a single company.
Can we have a normal profession once again?
Are the frauds all going to start calling themselves data scientists now?
I would love that.
I just went through 5 days of whiteboard interviews for a single company.
"I'm something of a scientist myself"
-- Data Scientist
> 17 tech companies contribute to our education model.
Pedagogy isn't something that tech companies are particularly known for, nor hire many experts of.
Pedagogy isn't something that tech companies are particularly known for, nor hire many experts of.
That's completely true. Companies contribute to our curriculum by co-creating projects. We work together with them to create these projects and are heavily involved in this process to assure quality.
Reasons we have this are: 1. For students it's a great way to see what tech stack is the company using and what problems they are solving.
2. For companies, each coming-in student has a "technical interview" already completed and a lot more context about the company.
Reasons we have this are: 1. For students it's a great way to see what tech stack is the company using and what problems they are solving.
2. For companies, each coming-in student has a "technical interview" already completed and a lot more context about the company.
I'm a bit late, but I'll ask my question anyway.
Data science requires a very strong mathematical background. Thee are libraries and software that do take care of some of the most complicated processes, but I don't believe someone can become a good data science engineer by always relying on such libraries/software.
Hoe rigorous is the treatment of mathematical topics in the AI course you offer?
Do you teach the concepts of probability/statistics, linear algebra and calculus required for the course, together with some testing or examination relevant to the subject material being taught? Or is your approach similar to Andrew Ng's Coursera course where he does give some introduction about the maths involved without going into details because they are not required, resulting in acquisition of, at times, half baked knowledge about core concepts.
Data science requires a very strong mathematical background. Thee are libraries and software that do take care of some of the most complicated processes, but I don't believe someone can become a good data science engineer by always relying on such libraries/software.
Hoe rigorous is the treatment of mathematical topics in the AI course you offer?
Do you teach the concepts of probability/statistics, linear algebra and calculus required for the course, together with some testing or examination relevant to the subject material being taught? Or is your approach similar to Andrew Ng's Coursera course where he does give some introduction about the maths involved without going into details because they are not required, resulting in acquisition of, at times, half baked knowledge about core concepts.
Your observation is on point - a strong understanding of maths is crucial for data science engineers. The topics you've mentioned - probability/statistics, linear algebra, calculus- are covered in our course, and our learners are expected to build a solid understanding of them gradually.
To ensure effective learning of these topics, we space out these topics over nearly all of the course. It's a change from our initial version where we've had it concentrated early in the study - however, we saw that such an approach was quite demotivating to many learners.
Next, we use spaced repetition. This happens during our daily standups and project reviews, where senior team leads (expert data scientists) regularly ask questions about topics that might have been covered in previous modules. The questions also tend to focus on understanding (e.g., why something is relevant, how it can be used in business situations) rather than simply recalling formulae or definitions.
Compared to Andrew Ng's Coursera course, we require our learners to understand these topics deeper. However, upon graduation, the level of most learners will be less than PhDs graduates, who spend half a decade on learning these topics. Nevertheless, our students will have strong practical skills to do data science, which isn't properly taught in academic data science education (we hear this a lot from hiring partners). What we have as a key goal, however, is to give our graduates enough understanding to:
a) be able to work on junior-level tasks effectively from day 1. The libraries you mentioned are helpful here, even though not enough on their own;
b) develop the capacity to continue learning maths-related topics independently so that the learner, even after graduation, can continue getting better at maths and feel comfortable with it instead of fearing it
I've heard that those who seek to pay for an education are usually unfit to evaluate its quality before they enter into an agreement. Might have been a paraphrase of a Socrates quote IIRC. I have basic coding skills and do DFIR professionally but I don't know much about data science/ML and I don't have experience with a lot of the math involved. So not being equipped to evaluate the quality of the instructors due to my own ignorance, I had a question:
The website states that 70% of the instructors are industry experts. Are any of them known to the Data Science community outside of being backed by Ycombinator?
The website states that 70% of the instructors are industry experts. Are any of them known to the Data Science community outside of being backed by Ycombinator?
Thanks for your question!
A good example is Dovydas Čeilutka, our Lead of Data Science, next to being ML team lead at Vinted (2nd hand clothing marketplace, valued at $4.5b), he is the President of the Artificial Intelligence Association of Lithuania and a founder of Tribe of AI, artificial intelligence learning community in Lithuania.
While Dovydas is well respected in the Baltics, we are in the progress of bringing more Data Science industry experts from global markets.
Another way we are making sure our quality is great, is by working closely with companies (Hiring Partners) by co-creating and integrating their projects into our curriculum. This assures that whatever projects our learners are working on are relevant to the market & companies they might eventually work at.
A good example is Dovydas Čeilutka, our Lead of Data Science, next to being ML team lead at Vinted (2nd hand clothing marketplace, valued at $4.5b), he is the President of the Artificial Intelligence Association of Lithuania and a founder of Tribe of AI, artificial intelligence learning community in Lithuania.
While Dovydas is well respected in the Baltics, we are in the progress of bringing more Data Science industry experts from global markets.
Another way we are making sure our quality is great, is by working closely with companies (Hiring Partners) by co-creating and integrating their projects into our curriculum. This assures that whatever projects our learners are working on are relevant to the market & companies they might eventually work at.
Thought this was related to https://turing.edu/ (a non-profit) before digging deeper to see they are using the same name (FYI to OP).
Education is long overdue for disruption, so whatever moves the needle even a little is huge in my eyes. And it's the first Lithuanian start-up that I know of to be a part of YC, congrats, this is a great win!
Does Turing College actually offer a degree?
Knowledge is important, but an actual degree is still a major consideration, since it does still determine pay bands and such at major companies.
Knowledge is important, but an actual degree is still a major consideration, since it does still determine pay bands and such at major companies.
We don't provide a university degree as becoming a university will strictly limit our pedagogical approach. Our credibility builds with time as more companies hire our students.
Aside: at my company ARR stands for Annually Recurring Revenue (and is explicitly defined at every town hall), rather than "accounting return rate".
Is one of these wrong, or is it just a case of more than one meaning for an abbreviation?
Is one of these wrong, or is it just a case of more than one meaning for an abbreviation?
It should be Annually Recurring Revenue, somehow the different definition was pasted here. Thanks for making a remark.
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Great project, I hope that you'll be successful. I'm pleased to see an european startup in the field. Do you happen by any chance to accept cryptocurrencies for the upfront payment?
I'm sure we could accept crypto as upfront payment.
It appears as though they are not hiring teachers. Not sure why they're eager to find students but not looking for talent to teach those students.
We are hiring industry experts (we call them Senior Team Leads) to consult and help our students. They are not teachers in a traditional sense, since they are not running daily classes. You can read more about their role in our blog [1]. We're constantly looking for more mentors and if you know anyone who'd be interested - we would be happy to talk to them!
[1] https://blog.turingcollege.com/senior-team-leads-at-turing-c...
[1] https://blog.turingcollege.com/senior-team-leads-at-turing-c...
I'm confused.
The code you have on one of the quiz questions is:
----------------
a = 1 b = 2 c = 3
def calculator(a,b,c): b = a - c a = 2 return a+b
x = calculator(c,b,a) print(x)
----------------
This returns 4. But the only options you have are:
A. no output B. 0 C. 3
The code you have on one of the quiz questions is:
----------------
a = 1 b = 2 c = 3
def calculator(a,b,c): b = a - c a = 2 return a+b
x = calculator(c,b,a) print(x)
----------------
This returns 4. But the only options you have are:
A. no output B. 0 C. 3
The iframe that the form is in is too small. 4 is there, you just need to scroll
Yep, make sure to scroll down to pick the right answer; which is 4.
4 was an option for me. Perhaps you had to scroll to see it.
For folks looking for a shorter / cheaper crash course I've seen this one pop up that seems connected to a practical environment to use long term.
https://developers.google.com/machine-learning/crash-course/
I'm tempted to try this to add SOME data analysis skills to my existing skills.
https://developers.google.com/machine-learning/crash-course/
I'm tempted to try this to add SOME data analysis skills to my existing skills.
What relationship do you have with the estate of Alan Turing?
We are not connected to the family of Alan Turing in any way. I'll refer you to our blog post [1], where we talk about why we take inspiration from Turing.
[1] https://blog.turingcollege.com/turing-college-and-the-man-be...
[1] https://blog.turingcollege.com/turing-college-and-the-man-be...
What on earth was "controversial" about Turing?
Seems a bit weird to take inspiration from someone, use their name, but then malign them like that and refuse to engage with his estate.
Seems a bit weird to take inspiration from someone, use their name, but then malign them like that and refuse to engage with his estate.
We, as the founders, aren't questioning the integrity and legitimacy of Turing. The whole context about our position on Turing is in the full article. Turing was abandoned by his home country for over 50 years and was just lately recognized as a hero (https://www.bbc.com/news/technology-25495315). So from the public perspective, his persona has some controversy (which is unfortunate as he should be recognized earlier).
OK. It makes it sound like you think he is controversial. There was no controversy I am aware of regarding his pardon.
Will you be donating any of your profits to the Turing Trust? https://turingtrust.co.uk
It's a charity set up by Turing's family to honour his legacy and to provide computers to schools in sub-Saharan Africa.
Will you be donating any of your profits to the Turing Trust? https://turingtrust.co.uk
It's a charity set up by Turing's family to honour his legacy and to provide computers to schools in sub-Saharan Africa.
My dear friend first tell me with bunch of jokers as instructor how you are going to teach data science to student. How these jokers are allowed to teach data science without any credentials
What do you mean by calling instructors jokers?
When the three of us entered university, we were taken back by the outdated teaching methods. We still smile when we remember learning Excel via a whiteboard! While studying, we were also running an IT and education consulting business that had an accounting return rate (ARR) of $0.6M and was expanding fast. This quickly taught us that the way developers are educated is not aligned with the hiring and onboarding processes of the tech companies looking to hire them.
We saw this issue from both sides of the hiring process. As students, we were learning subjects that didn’t prepare us practically to deliver results for companies from day one. As employers, we were frustrated when hiring students based only on their educational credentials, as these weren’t a good guide to future performance. So, we decided to organize a non-profit data analysis bootcamp, where the curriculum was supplemented with hiring partners' projects. First batches were oversubscribed and we were nudged to build a school, which would create specialized data science courses.
Our programs are self-paced, so we’re not a bootcamp in the sense of forcing more and more information on people each day, whether or not they have digested the previous material. Completing a course with us usually takes 9-12 months, but students can progress as fast as they like, and some experienced software engineers have completed 1,000 hours of coursework in 6 months. Conversely, students who are transitioning to data science from other fields, and lack fundamentals in maths or statistics, can go slowly and build solid foundations in these areas.
Students choose between several data science specialisations, including data analysis, requiring a solid understanding of statistics and mathematics and excellent data wrangling skills so that data analysts feel comfortable importing, cleaning, and manipulating data; and machine learning engineering, focused on building machine learning models that solve business challenges. Our curriculums are co-created with tech companies who we partner with, who tell us specifically what they are looking for in new hires. Since we started 6 months ago, we have had 17 companies contribute to our learning concept, including Moody’s and NordVPN. We teach current tech stacks and use specific problems companies have worked on as the basis for projects that students work to solve. These later turn into project portfolios that help them get hired.
Each student also gets regular industry professionals guidance from our staff and hired Senior Team Leads, working professionals in the data science field. They perform 1-on-1s, standups, do mock-up interviews, and more. These professionals are paid consultants who joined us from Waymo, Unity, and more. One student writes: “I studied in university, and at other coding schools, but Turing College is just something totally different. The best part is the ratio of personal tutoring hours we get - it is 10x more than in the places I have tried before!” We use standups and 1-on-1s with senior leads, and students get a weekly minimum of 3 hours of personal consultation with their leading peers and/or senior staff.
Students also get feedback, motivation and encouragement from their classmates. We have a diverse community, including fresh graduates in STEM subjects looking to specialise, right through to software engineers who want to enrich their data knowledge. This diversity enables mutual support. Those with backgrounds in maths and statistics can help those with pure coding background, and those with experience in business can support with soft skills. It’s a collaborative, community-oriented approach that we support and encourage through regular live and online meetups and events.
Students can track their performance via a personalized online learning platform. It unifies everything students need for work-like learning in one place: projects, standups, sprints, code reviews, etc. Upon graduation, such performance data is compiled into personalized reports for the students to show to prospective employers and get hired.
Crucially, these reports focus not only on hard skills but also on developing soft skills. Our hiring partners consistently tell us that 60% of their decision-making when hiring a junior role is based on a candidate’s soft skills. Our personal development program focuses on students’ time management and growth mindset, communication, and other interpersonal skills. Six workshops elevate our students’ soft skills awareness of each of these different skills. Then their progress is tracked throughout the course by having our students fill research-based self-reflection questionnaires and during 1-on-1 meetings with mentors and feedback sessions with staff and STLs.
We make money by charging students tuition fees. In terms of tuition fees, our goal is to take the most flexible approach possible. Students can pay in one up-front payment, make monthly installments, or defer payment until they are working via an Income Share Agreement (ISA). We don't have a preference between any of these choices, but rather work with each student to figure out which is the best one for them.
We’re looking forward to your feedback as we are really aiming to make learning and hiring as integrated and as least biased as possible.