An important part of being a professional data scientist is being able to communicate clearly - both in writing and in person. Because of that, we will be asking you to write at least seven blog posts before graduating. This short lesson provides a little more information about why we ask you to blog, what we ask you to blog about, and how we'd like you to blog.
As a professional data scientist, you'll often have to communicate in writing. Sometimes you'll have to write something technical for your peers, at other times you'll have to provide a non-technical summary for business stakeholders. In either case, it's important to be able to distill a complex technical topic and share in writing the most important points.
A blog can also help you to get a data science job. It's a way to showcase your skills to potential employers before they decide to interview you, so it's important to realize that your blog posts may well be reviewed by potential employers when deciding whether or not to grant you an interview.
There are seven blog posts that we'll ask you to complete. That is a graduation requirement - without the seven required blog posts, you won't be able to graduate from the program. Please feel free to write more posts, although in general a "reasonable quantity and great quality" is probably the best strategy so you can have 1-2 posts that you can point potential employers to that really show how you can write for both technical and non-technical audiences. The risk of too many posts is that employers will click onto some of the weaker ones if you didn't have the time to make them all great.
Here are the seven required blog topics:
- Why did you decide to learn data science?
- Write a post on a visualization technique of your choice (line plot, histogram, heat-map, etc). How is it being used to answer analytical problems and what are it’s strengths and weaknesses?
- Write about a topic you are finding particularly challenging. Do it in the form of a tutorial to help another aspiring data scientist to learn that topic.
- Write a tutorial (with a data set and code sample) on something that we did not cover during the course that you think might be interesting to other people taking the course. It can be a topic we didn’t cover at all, or can just go deeper into a topic that we did cover.
- Write about your experience with model selection, model validation and hyper-parameter tuning for a data set you’ve worked with on the program. Include hints and tips that would help another data science student do a better job of model selection, validation and tuning.
- Pick a data science paper written in the last 18 months and rewrite it to explain it to a non-technical business stakeholder. Focus on why it’s important and why it would matter to them.
- Pick one of the most influential papers in data science (ask us for a list!) and rewrite it to explain it to a non-technical business stakeholder. Focus on why it’s important and why it would matter to them.
These posts should be written in order. The first post requires no experience in data science. The second one assumes you've completed the first couple of sections in module 1, question 5 is one you won't be ready to write about until the end of module 3 and you won't want to dig into the last couple until you're in mod 4 or even in project mode in module 5.
In terms of length, there isn't a set requirement. That said, it'd be hard to do justice to most of these topics (except perhaps the first one) in under 800-1000 words. Equally, you should try to make it a posting - not a novel. If you're blowing through 3000 or 4000 words, it's going to be hard to get someone to read the post, so you might want to constrain the scope so it can be fitted into (say) 1000-3000 words (yes, 3500 words is fine if you need it!)
If you already have a blog for work related topics, feel free just to add your posts to that. If you have a personal blog, you might want to set up a separate "work" blog. It's not required, but do consider that employers will probably look at multiple posts from any blog you share, so if you mix personal and business postings, make sure they're equally high quality and would reflect well on you when being considered for a job. Finally, if you've never blogged before, not to worry! Pick your favorite blogging site/software and start blogging. Don't worry too much about which platform you pick - just pick something and focus on writing great content.
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