If you’ve made the decision to attend a coding bootcamp, no doubt you want to do everything you can to inform yourself about the challenges ahead. And since bootcamps famously focus on projects for learning the material and building a GitHub portfolio, it’s important to get as clear as possible as to what this will mean.
In this article we’re going to discuss project-based learning generally, and then explore what this tends to mean for attendees of web development and data science bootcamps.
What Is Project-Based Learning?
Project-based learning is an approach to acquiring knowledge and skills oriented around building projects of successive difficulty. It’s popular in the programming and data science communities.
There are alternatives which you are more likely to encounter in traditional classrooms or in individuals who are self-taught. A theory-based approach emphasizes the study of high-level theoretical concepts before actually making anything, for example.
Benefits of Project-Based Learning in Coding Bootcamp
Working on projects as a means of learning has numerous advantages. It familiarizes you not only with specific skills, but in how those skills all tie together. Building a machine learning model is one thing, building it in the context of a complete data science pipeline is quite another, and it’s this second task that’s really more valuable to employers.
Projects are almost always done with other people, which also teaches you how to work in groups, how to use version control systems like Github, and how to communicate effectively.
Typical Web Development Bootcamp Projects
Web development bootcamps tend to come in front end, backend, and full stack flavors. Naturally, the kinds of projects you’ll build will be strongly influenced by which of these programs you’ve ended up in.
Front End Development Bootcamp Projects
Backend Development Bootcamp Projects
In contrast, backend web development work concentrates on the technologies that power the internet ‘under-the-hood’. Server-side scripting and database management are precisely the kinds of things you’ll be learning to do. Expect to learn technologies like MongoDB, Java, .NET, and Node.js to store a web application’s data and dynamically update it.
Full Stack Development Bootcamp Projects
Full stack development, of course, combines these two things together, and your projects will reflect that.
Typical Data Science Bootcamp Projects
The term ‘data science’ stands for a pretty broad range of activities that can be broken down into data acquisition, data wrangling, database management, data analytics, and machine learning. At the Galvanize Data Science Immersive, I worked on projects that hit all of these major points, and as far as I can tell, pretty much every data science bootcamp does something similar.
Early in the program, our projects involved performing simple statistical analysis on pre-cleaned data to introduce us to the data science workflow. Later, we did some pretty grueling work which required us to take poorly-formatted JSON and get it into a format that would be usable for natural-language processing.
The second half of the program featured a lot of cloud computing for big data. We worked with Spark and Amazon Web Services to process truly huge amounts of data. The field of data science is moving more and more in this direction, and given the unique technical challenges required for working with distributed computers and huge datasets, I’m glad I was exposed to these technologies.
We also spent time on machine learning and deep learning. We built simple neural networks in class, bigger ones for our projects, and learned to use popular ML frameworks like TensorFlow.
Given how important project-based learning is to the bootcamp community, hopefully this information will help you enter your bootcamp knowing what to expect.
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