I realize that I’ve done this topic before, but perhaps it needs some more attention, as it’s a very useful topic.
Diagrams are great, but they are also challenging. As for the other graphics (particularly those not generated by a plotting library), these can be tough too. But both diagrams and these unconventional graphics are often essential in our line of work, be it as data scientists or data analysts. Let's examine the hows and whys of all this.
First of all, diagrams and graphics, in general, are a means of conveying information more intuitively. When you look at a table filled with numbers and other kinds of data, you need to think about them, and sometimes you have to know something about the context of all this. With diagrams, you may get an idea of the underlying information even if you don't know much about the context. Of course, the latter can help bring about scope and perspective, helping you interpret the diagram better and make it more applicable to the task at hand.
Diagrams and unconventional graphics are paramount in presentations too. Imagine going to a client or a manager with just a code notebook at your disposal! Even if they may appreciate you having done all this work chances are that you'll need more than that to get them on your side and see the real value behind all these ones and zeros! Besides, the adage of "a picture is a thousand words" is valid, even in Analytics work. Data modelers have figured that out a long time ago, which is why diagrams are their bread and butter. Perhaps there is something to be learned from all this.
But how do you go about creating diagrams and unconventional graphics in general? After all, graphics design is a challenging discipline, and it's not realistic to try to do this kind of work without lots of studying and practicing. Also, it's doubtful we'll ever be as good as graphics professionals who often have the talent to drive their know-how. Still, we can learn some basics and create decent-looking diagrams and graphics, to facilitate our data science endeavors.
For starters, we can invest in learning a program like GIMP. This software is an open-source version of Photoshop, and it's well-established and documented. So, if you have a good image or graphic to work with, GIMP can make it shine. Also, programs like LibreOffice Draw can be practically essential for this sort of work, especially if you want to build something from scratch.
Contrary to what some people think, creating graphics is very detailed work, not some artistic endeavor. You need to use both your analytical and your imaginative faculties for such a task, even if the imagination part may seem dominant, at least in the beginning. So, for any graphics-related tasks, remember, zooming in is your friend! As for the properties box of any graphical object, that's your best friend!
Anyway, I could go on and talk about graphics in data science and data analytics work all day, but it’s not possible to do this topic justice in a single blog post. Besides, the best way to learn is by practicing, just like when it comes to building and refining data models for your Analytics work. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.