A prospective employer will look at your online profiles. This will help them get a bigger picture of you than what was in your resume and profile you filled out for your data science job application. When they find your data science portfolio they will judge your work. Which brings up a very important question - should you focus on quality or quantity?
Obviously both quality and quantity
Employers obviously want to see tons of high quality data science projects. This shows how deep you can go as well as gives you an advantage of showing your best work. It also allows you to showcase the context around your work. Finally, it looks a great deal better than just throwing in some work into your “portfolio” and hoping for the best. If you do this, you will be judged harshly. In this case, no portfolio will have been better than crappy portfolio.
Before the end, where should you start
You want to have both quality and quantity. But when you start, it is not clear whether you should focus on quality first or on quantity first. If you focus on quality, the fear is that you might only finish one project and it might not even be that good. If you focus on quantity, the fear is that you might finish lots and lots of projects and they might all not be that good. So where should you start?
Data Science as art and the book “Art & Fear:Observations on the Perils (and Rewards) of Artmaking”
“Art & Fear:Observations on the Perils (and Rewards) of Artmaking” is a book by David Bayles and Ted Orland that speaks about making art. The reason that this book is relevant in this discussion is because the book talks about “the gap that inevitably exists between what you intended to do, and what you did.” Which is important when thinking about your data science project for your data science portfolio.
The key part of the book to look at is the following passage:
The ceramics teacher announced he was dividing his class into two groups. All those on the left side of the studio would be graded solely on the quantity of work they produced, all those on the right graded solely on its quality.
His procedure was simple: on the final day of class he would weigh the work of the “quantity” group: 50 pounds of pots rated an A, 40 pounds a B, and so on. Those being graded on “quality”, however, needed to produce only one pot - albeit a perfect one - to get an A.
Well, come grading time and a curious fact emerged: the works of highest quality were all produced by the group being graded for quantity!
It seems that while the “quantity” group was busily churning out piles of work - and learning from their mistakes - the “quality” group had sat theorizing about perfection, and in the end had little more to show for their efforts than grandiose theories and a pile of dead clay.
What this means to your data science portfolio and your work
The take away is a simple one - you should focus on quantity rather than quality. Yes, you have a strong taste and are able to recognize great work, but there exists a gap between what you can produce and what you recognize as great work. The way to close that gap is to produce many many many projects. As you do this, just like the ceramics students above, you will learn from your mistakes and won’t focus on “perfection”, you’ll focus on getting things done.
Why this matters for a data science jobs
In a data science job, you will be asked to do many many projects. And while yes, great results will be expected, the more projects you do the better you will get at your job. This is how data scientists rise from junior level to mid-level to senior level to principal level roles. Through experience. So if you can start not by practicing doing lots of projects and encountering all kinds of issues with data, with models, with programming languages, with libraries, with AWS, with data storage, and everything else that comes with data science, just think how much further along you will be.
Which is great, because you’ll have this mind-set going into the job application process as well as the job interview. And this will be showcased, not only in your data science portfolio, but when you talk about data science in general. All of which is super beneficial and something that potential employers really want to know and see in action.
The next step
To that end, start on a project today and don’t worry about how “terrible” it is. The key is that you have started and that by focus on quantity, rather than quality, you will slowly start chipping away at the gap between what you’d like to make and what you can make today. Today’s project will be good practice for you!
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