(By Tristan Shaeen)

Organizations the world over are awakening to the hidden potential contained within their data. And they have begun to call for help from the data fluent in order to realize it. In turn, this has caused an explosion of learning materials available online, and a corresponding rise in the number of people seeking to develop these skills.

Many of these seekers are the self-taught: this site is devoted to you.

In our introductory article we called the world of learning materials a “wilderness”. We draw this analogy because the datasphere is a vast, growing, and complex landscape to which many people are drawn and within which both triumphant and cautionary tales play out.

We cast the self-taught journey within this landscape as akin to a solo venture into backcountry wilderness. We believe this analogy is worth its weight in cliché because the types of tools that bring success in one bring success in the other. To this end, we unpacked the full data wilderness survival kit in the introductory article.

In that survival kit we found a mindset, a field journal, and a map. This article in particular is devoted to the map.

Why do I need a map?

It might sound trite, but in order to get anywhere, you need to know where you’re going. This is particularly true when you are dealing with a complex landscape.

The landscape of online data learning materials can be viewed as a massive network of learning paths. These paths all vary in their length, level of difficulty, and in the type of terrain they wind through.

In order to properly select a path that is right for you, and not get lost along the way, a map is required.

Are there currently maps out there?

Many learning platforms were launched around the mission of mapping out and trailblazing learning paths that will guide a learner to a desired destination.

A cursory internet search will produce an array of these learning platforms that have produced learning paths designed to lead to a degree of competency in some of today’s most well-known data disciplines.

A bottom up approach?

These map makers previously mentioned take the “top down” approach. They take some of the hottest data jobs currently out there (e.g., data scientist), reverse-engineer them to figure out the skills required, and then attempt to map out and trailblaze a learning path that links together these skills to reach the destination of becoming a potential candidate for that role.

Conversely, we take what is known as the “bottom-up” approach.

Rather than the guiding focus being on the job titles of today, we focus on the atomic skills that constitute them, as well as those that fall outside of their purview (e.g., data story-telling). We call this approach “atomic learning”.

We believe in atomic learning for two broad reasons: the first is that we believe it is more conducive to the learning process itself and the second is because it is actually a better fit to the real workforce. We’ll take up the latter reason first.

How is it a better fit to the real workforce?

Data fluency is now required at every level of an organization, from the professionals who administer the databases, through those who model and analyse the data, to those who make operational and strategic decisions on its basis. Data fluency indeed looks different at every level, but no level is remedial with respect to any other, and indeed there are no sharp lines that separate these levels.

This means that one should have the flexibility to learn the atomic data skills that are most relevant to their current professional or personal aims, which could mean either focusing-in on a particular set of atomic skills or broadening-out to cover the entire stack.

This is why we map our learning paths with atomic skills in mind. At the end of each skill being acquired, the learner has the flexibility to choose a new path and build upward in a direction that suits their aims rather than in a direction that has been pre-determined by the platform. Hence “bottom-up” “atomic” learning.

What does this all look like, specifically?

As previously mentioned, this wilderness is full of trails that have already been blazed by passionate members of the datasphere. Indeed, fantastic blog posts and web pages teaching valuable skills abound. The problem with this network is not the number of options, but often scope (not atomic) and accessibility (hard to find).

What we seek to be is the information centre; the place where you get the maps that highlight the best trails as well as other resources that will assist you along your journey.

Our maps will carve out the trails in an atomic fashion such that you can build up your experience according to your own personal aims. Where we discover in that process that a path or trail needs to be blazed, we will do so ourselves or post this as an opportunity for someone else. If you have blazed one yourself or find one you think should be mapped in our collection, please contact us!

Final Thoughts

By taking this approach to mapping the landscape, we are taking it in its entirety rather than focusing on a particular region from which we work backward. Given the breadth and ever-expanding nature of the datasphere, this means that we too will always be growing. If you feel you have something to contribute, again, please contact us!

We hope our website becomes an essential resource for you along your data journey.

And with that, we wish you nothing but happy trails!

All our best,

Sysabee Learning

p.s. For a fantastic source on the power of atomizing, read James Clear’s Atomic Habits