The online courses for data science and related tech are overflowing and there are many institutes and individuals offering online, offline, full-time, part-time, short-term and long-term courses for aspiring data scientists. However, one crucial debate that often takes a centre stage, is whether one should opt for online data science courses or stick to project-based learning.
While many online courses come with capstone projects built in the curriculum, there are many who prefer learning these skills on the go while training in companies as interns or taking up small quick projects as a part of their learning. There are many professionals who talk about the merits of learning by doing, i.e. project-based learning as a better alternative to online data science courses.
Why Should You Pick Project-Based Learning?
There are many online courses and MOOCs available for self-taught data engineers, especially those coming from a non-technical background, but it might not work well in most cases. While online courses are well structured, flexible in terms of time and location, and more, there are several challenges such as staying motivated and maintaining discipline. Especially for working professionals, it can be challenging to take out time during the working days. There can be other hindrances such as projects not aligning with one’s interests.
It is therefore advisable in many cases to choose a project that is interesting and allows you to learn skills that you actually want to learn. Learning these skills on the go will help you have a better understanding of the skills acquired than reading it online in pdfs or ppts. Project-based learning is more efficient, more practical, and more fun. Not to forget, it is more hands-on allowing for more practical learning, rather than just concepts. It also helps to quickly build a portfolio even as you learn, making a strong point when applying for data science jobs.
Project-Based Learning Is About Being Motivated
Self-teaching can demand a lot of attention and motivation to stick to the project. In such scenarios, picking projects that you would actually like to work on can come as an easy answer to staying motivated. If you are a working professional, you might not have the time to sit through long sessions of online classes and not even remember the content that was delivered in these classes. Also, the hard part of data science learning such as programming, data warehouse modelling, statistical applications might not be well understood theoretically.
Project-based learning can, therefore, prove to be more advantageous in terms of utility, driving passion, addressing the curiosity and staying competitive.
How To Accomplish Project-Based Learning
There are many courses that are specifically designed to be project-based. Courses such as Udacity provides excellent project-based courses. It focuses on skills that are needed to build cool projects which have been designed in association with prominent tech companies.
There are other platforms such as Skyfi Labs that provide an excellent platform to learn and build working projects on areas such as IoT, AI, data science, robotics and others. It includes online tutorials, hardware kits and tech-support to make the most of it. It is an innovative learning methodology that equips with required tools while being cost-effective.
It is also advisable to take time to install the applications and tools one wants to learn and explore its documentation to get started with it.
Challenges Of Project-Based Learning
While project-based learning has a lot of advantages, it may come with its own challenges. Such as it is not structured, the curriculum may not be designed, may not be comprehensive, may be harder to understand, harder to get a peer review, among others.
Both the modes of learning have their own set of advantages and disadvantages and also depends on the kind of learning that a learner is looking forward to. They are also not mutually exclusive. However, project-based learning can have an upper edge given that they are self-taught and may prove to be more resourceful. Theoretical knowledge can often be misleading resulting in inefficient codes and lack of actual knowledge about the tools and techniques. Having said that, it can only be concluded that there are no right means to get acquainted with the skills, it is the efficiency at the end that matters.