Starting Simple
One of the core beliefs at Dataploma is the idea that data is, like natural languages that we as humans speak every day, a way of communicating that needs to be learned and nurtured. It isn’t something we are born with the ability to do, any more than we are born with an innate ability to speak English, or Mandarin, or Spanish. It’s a skill - one at which we can become quite adroit - but one that needs to be learned nonetheless.
One implication of that core belief is that data literacy requires an understanding of the basics of the language of data first, before trying to layer on additional complexity or specificity. We want to make sure that our students understand what they can achieve with the language of data, as a very first step, before anything else.
So, at Dataploma we conduct our training with a fully intentional aversion to specific technologies. There are several clear reasons for this:
There is a massive breadth of existing technology that is intended to address data problems; we can’t possibly cover all of that scope in the time that we have with our students
The technology landscape is constantly shifting; we want to spend our time communicating topics and concepts that are central and shared by the entire landscape
Our focus is broader than the technology teams that will do much of the implementation and use of data systems and we want to be inclusive of other team members who can and should be “speaking” data but who won’t be directly responsible for the technology solve
To us, it’s a simple choice, really, and one that makes sense. We wouldn’t ask kindergartners to develop multiple verses in iambic pentameter; nor would we suggest it’s appropriate for fourth-graders to be developing or reading dense legalese for, say, corporate contracts. While the language might all be English, the specific application of the language, the terms being used, and more - well, they’re all far too advanced for those grade levels.
We don’t want our students to have to try to learn Shakespeare on Day One. Instead, we want to start with the building blocks of the data language - the vowels and consonants, the simple words, the basics that make data communication possible - so that they can talk to one another as well as to data professionals about the world of data that surrounds them. Once they’re fluent in the language of entities, relationships, cardinality, information, knowledge, and prediction, they or their managers can choose to expose new concepts, new tools, specific approaches, and more.
Plus, for us at Dataploma, focusing on data concepts and removing focus from specific tools and applications allows us to work with teams on business processes, data and more that are specific to their organizations - which brings the experience much closer to our students and much more valuable overall.