3 DATA SCIENCE AND MACHINE LEARNING BLOG POST IDEAS

Of all the advice to professionals and students in the analytics, data science and machine learning field out there, the one point that everyone agrees is that you need to have excellent communication skills.

And the one technique to showcase those skills to your network and beyond is through blogging. Starting a data science blog is a great way to highlight your thought process, demonstrate learning ability and undoubtedly your communication skills- nothing like a blog post to convey your style of sharing information and insights.

But what do you write about? Staring at a blank page is no fun – so, here are 3 topics to blog about in data science, analytics or machine learning, with example questions. Writing blog posts on data science and analytics just got easier!

1. PROJECTS

Write-ups on projects are an excellent way to create a blog post. We normally link up GitHub for projects, but a short summary post helps to share what the project is all about. A high level overview along with a brief summary of the tech specs makes these super easy to jot down. If you are in the habit of adding README pages to your projects (if not start), it gets simpler.

A word of caution, most companies have a non disclosure policy when it comes to internal projects. So, keep that in mind when you are composing.

If you are still struggling over what to write, here are some prompts:

• Share an overview of your favorite personal project. If you have not yet started, just pick a dataset on Kaggle and document your insights you gleaned or the process you followed for preprocessing and cleanup. Better yet, do both.
• Recreate a particularly interesting project- do cite the original creator and give them credit. Kagglers share notebooks and kernels, so that is another reason to frequent the space.
• If being original is more your style; add your own spin as an extension or a separate project altogether to someone else’s- like doing in a different programming language, using a more optimized code, etc.

Bonus: These snippets are super handy for sharing at networking events and interviews.

2. COURSES

Everybody begins their learning journey somewhere. Courses and what you learnt in the process are another source of blog posts. Be it a traditional university class, a bootcamp, an online course, or an email class; you can chronicle all the amazing concepts learnt and add notes on your overall experience.

Bonus: Recruiters and hiring managers cite learnability and continuous professional development as top traits in non-technical skills. These kind of blog posts give you an impressive way to highlight exactly those skills.

3. LISTS

I love lists. As does 95% of the blogosphere and online content consumers. Then why re-invent the wheel? Use this tried and trusted formula, and you can chug out plenty of posts too.

The beauty of this method is that you can be as much or as little technical as you like.

Here are a few examples of both:

• R or Python: 5 Points of Comparison
• Top 3 Favorite Machine Learning Libraries
• 3 Ways to implement a BarChart with midpoint Line Graph (R, Python, Tableau)
• 5 Lessons Learned from Kaggle Competitions
• 10 Favorite Data Science Learning Resources
• 5 Sites to find Datasets

Hope these topics and sample prompts inspire you to start writing data science blog posts as well. If you found this post useful, please share- it might help a fellow data science and analytics blogger looking for ideas.

Also, let me know in the comments below what are your favorite ideas for writing machine learning and analytics blog posts are.