My name is Lukas. My co-founders (Benas and Tomas) and I are building Turing College (
https://www.turingcollege.com), a career school that ensures that students are work-ready on day one of their new position. We’re currently focusing on data science skills.
When the three of us entered university, we were taken back by the outdated teaching methods.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.
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?