You have finished working on your first data science project. It’s simple, but works. You are amazed by how much you have learnt while working on it. Now, you wish to share your work to the rest of the world. So, you begin writing a blog. Soon, you realise that your project is not decorated enough to be “blog-worthy”.

You could have always written a “How to do X” or a “Build X with Y” article. But, you prefer to not add onto the redundancy of certain content on the internet. You think about posting your project on LinkedIn. After all, its the network that could help you increase your professional contacts.

Then you think of the projects you have seen on your LinkedIn feed over the past few days. Each being scarier than its predecessor. Once again you realise that your project is not ready to go onto a platform, especially one with so many complex projects ruling the likes and shares.

At this point, some of you might save your code files into Github, with or without documentation. If you prefer to keep the code files in your local system, I pray you don’t delete them by mistake during a routine desktop cleanup. I was stupid enough to do it.

After a few weeks, you forget all about the project. Life moves on. You start learning new things. You build more projects, but do not discuss about your work with anybody. You are literally the Bruce Wayne of beginner data science projects. This is no doubt the road riddled with convenience. But convenience is a very poor catalyst to improvement.

The less convenient pathway would be to discuss your work with others, communicate your efforts with them, help them understand your work, understand their feedback and improve upon what you already know.

If you would be taking away only one statement from this article, I want it to be this — “In order to improve as a student of the data world, realise that everybody around you is an able mentor”.

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