[artembugara]

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?

[CosineTangent]

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.

[ampdepolymerase]

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.

[snicker7]

I love to how you refer to electrical and mechanical engineering as "legacy".

[ampdepolymerase]

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.

[kaminskis]

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.

[runawaybottle]

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?

[tmoska]

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...

[burnished]

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.

[artembugara]

Agree. Market is overloaded with Data Scientists with a 6-12 months courses.

[onion2k]

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.

[kaminskis]

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.