Why every data scientist should write a blog, and how.
What spurred me to write a blog?
I’ve always thought I had superior genes because I managed to dodge Covid-19 for 2 years, but two weeks ago, I became an additional statistic to the pandemic. Although my symptoms were pretty mild - sore throat and cough - I was bored during quarantine. Like very bored. And it was after binge-watching “The Office” and reading “War and Peace” for hours when I thought: hold on, why don’t I start my own blog. This brings me to my next question: what content should I deliver to the online gods? Since I have been passionate in A.I., Machine Learning and Big Data, I have decided to devote my blog to these topics. Long story short, boredom spurred me to start a blog, but apart from that, here are my top reasons why every aspiring data scientist should write a blog:
When you’ve written the same code 3 times, write a function
— David Robinson (@drob) November 9, 2017
When you’ve given the same in-person advice 3 times, write a blog post
1. Shares and preserves your knowledge.
Even though I just started my data science (DS) journey, I often get the same questions from my colleagues and clients, such as “How do I predict churn with machine learning?” or “What’s the best data visualisation tool?” While the context and data may be different across various industries, the ask is similar, hence we could definitely apply the same fundamentals data science skills. With a blog, you could summarise the steps, key findings and pitfalls of your approach, as if you were explaining to another data scientist. This not only serves a reference or inspiration when you come across a similar problem in the future, but also becomes a valuable resource for other data scientists around the world.
2. Feedback and evaluation
Displaying your knowledge at the mercy of online experts might sound daunting at first, but remember, your blog does not need to be perfect. It’s a blog, not an article in Nature journal. Harnessing this fear has its benefits too; blogging compels you to compare your approach with current best practices, subsequently improving your skills. For example, you might know how to code a simple Natural Language Processing (NLP) model after finishing a Datacamp tutorial, but when you test it with a Kaggle dataset, it hasn’t reached your desired accuracy. You might want to find methods to improve the model accuracy before publishing the blog post. Even if there are mistakes or inefficiencies in your analysis, the comments from the data community are typically constructive e.g. “Great work, but have you considered using string lemmatisation with NLTK?” rather than toxic “This is trash!” If you are getting toxic feedback, it’s usually not your fault, its the community’s.
3. Boosts your portfolio
Which would be more convincing to a recruiter: a CV with a few bullet points, or a tech blog that outlines the details of your achievements and research? The answer is pretty obvious. Blogging presents your skills in the “best foot forward”, where you could showcase your strongest skills . Furthermore, blogging opens up new opportunities and may even help you land a new job - your future hiring manager might be reading your blog, at the other side of the world.
4. Meet like-minded people
My favourite reason of all is that blogging expands your network, enabling you to meet data experts around the world and hopefully making some life-long friends along the journey. While you are still here, don’t be shy and connect with me on LinkedIn.
How do you start a Data Science blog?
The best part about writing your own data science blog is that you can choose your favourite dataset for analysis. Here are some sources that could be your starting point:
Remember, writing something is better than writing nothing! Good luck!
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