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 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.
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?
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...
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.
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.
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.
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?