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.

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.

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...

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.

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