Our just announced Data Engineering (DE) Bootcamp is the first we’ve designed with two different starting points for incoming students. We thought it would be worthwhile to take a bit of a deeper dive into that structure and explore how the course design supports the needs of individual students in a mixed-experience learning environment.

As noted in the blog post announcing the DE program, the part-time bootcamp can be either 9 months or 12 months long. The 9 month version is designed for adult learners who already have tech training or experience in programming. They may have completed a prior bootcamp certificate, they may have completed a degree program that gave them training in programming, and/or they may have worked professionally doing software development, data analytics, possibly IT infrastructure work. But, whatever their level of programming and tech experience, they are looking for an intensive learning experience that gives them both a view of the breadth of data engineering tasks and tools as well as hands-on project experience at every step in the data engineering lifecycle.

The 12 month version is designed for students with very similar backgrounds to the students we’ve admitted for the last 14 years into our existing bootcamps - adult learners with aptitude and motivation but limited or no prior tech experience in programming, data management, etc. Those students get 3 months of intense immersion into programming and the Python programming environment as well as other essential software developer tooling such as GitHub. After those 3 months, these students join the 9 month students and they all participate in the Data Engineering Bootcamp

This intermingling of relatively more experienced and less experienced students may seem unusual. It’s certainly different than what you’d expect to run into in a typical university setting for example. So let’s talk about the NSS learning environment and the design of this program. Much of this will be familiar to prior NSS bootcamp students but we hope it’s useful to those who are new to NSS.

We’ve always designed our programs to provide a broader learning experience than one narrowly designed to teach hard technical skills. We’ve always tried to provide students with opportunities to experience elements of a (simulated) work environment - our goal is to have students prepared to hit the ground running as new professionals. That means understanding how to communicate with other professionals and how to work and collaborate effectively on a team and in a project-centered environment, because almost all tech work revolves around teams working together on projects. The so-called soft skills part of learning to be a professional is every bit as important as the hard technical skills when it comes to preparing students for a successful job search - as employers have often told us over the years when explaining why they liked hiring NSS graduates.

Project teams in the real work environment are inevitably a mix of experience levels - both in terms of years of experience but also in terms of the mix of tech skills, life skills, soft skills, etc. that each team member brings to the project. In a learning setting, we believe that well designed project experiences and peer-centered learning opportunities extend and expand the learning for each student. Students can assume different roles on project teams. More experienced students can take the lead on more complex aspects of a project or help a project drill deeper into a solution, less experienced students can still tackle aspects of the project that stretch their learning muscles while reviewing the work of more experienced developers (or even their less experienced peers) which is another form of learning - examining the work of others in order to learn.

We have always used the idea of an apprenticeship and an apprentice learning model as a foundational concept. After all, apprenticeship is the oldest proven model for learning a trade or craft or even profession (lawyers for example apprenticed to gain the skills to pass the bar long before there were law schools). We encourage our bootcamp students during orientation on the first day of class to approach NSS as the start of their apprenticeship as a software engineer or data analyst, and now data engineer - and to think of it as the beginning of a career-long apprenticeship since we can never stop learning as technical professionals.

In an apprenticeship environment, apprentices can and do learn from everyone - from the masters and journeymen who might be their class instructors or their team leaders on the job or from their peers on a team who might be an experienced journeyman but could also well be another apprentice. And equally, journeymen and masters learn from less “experienced” folks. We’ve all seen examples of how someone with the right few months of experience can be their team’s expert on a particular technology or problem domain.

All of this is similar to other time-honored learning environments. For example, in a teaching hospital, attending physicians, residents, and medical students all walk the same halls and examine the same patients, but their responsibilities and learning outcomes are different. (And yes, the attending physicians, just like our instructors, are also learning throughout the process. You can be a subject matter expert, a coach, a mentor, and still be a learner.) Think of our cohorts operating like a clinical rotation. You aren't just sitting in isolated and isolating lectures; you are diagnosing and collaborating to solve complex, hands-on problems. Advanced students act as the 'residents,' taking lead roles on project architecture and strategy, while newer students handle foundational execution.

We’re ready to welcome our first cohort of the Data Engineering Bootcamp. DE bootcamp adds a new variation to our curriculum structure but one that’s built on well-established and successful foundations of other NSS bootcamps, which in turn were built upon older generations of proven learning models. It’s very exciting for us to add another career path to the portfolio at NSS, in particular one where the demand for talent is being driven by the long-term trend of more and more data, of more and more diversity and volume, created by and needed for the proliferation of new systems, many of them enabled by AI and all dependent on new or evolved data architectures.