kaminskis

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

What do you mean by calling instructors jokers?

Contact me directly [email protected]

It should be Annually Recurring Revenue, somehow the different definition was pasted here. Thanks for making a remark.

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.

We aren't related with this Turing.edu. We are aware of them but we don't have any legal problems regarding trademarks as our brand is protected in Europe.

I'm sure we could accept crypto as upfront payment.

Sounds amazing! Thanks, we will share this with our students.

Yes it is. We look to everyone's case individually and it's possible to be a part-time student having ISA.

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.

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.

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.