The idea of intelligent machines is one that has been a myth since antiquity. Yet in a single lifetime has gone from mainstream science fiction to mainstream science. We’ve gone from Alan Turing’s machine to an era where cars drive themselves and machine learning algorithms are almost commonplace.

How do you stay relevant in an industry that changes this rapidly?

For most of us, it’s not within reach to commit to a formal learning program such as grad school. The rest of us elect for online mediums, but you can watch tech webinars and read articles until your eyes bleed and not absorb a thing.

Instead, I suggest a do it yourself, hands-on approach to staying relevant. Spend a couple of hours each week following this three step approach and you will have no problem keeping up with this dynamic landscape.

For the things we have to learn before we can do them, we learn by doing them. ― Aristotle, The Nicomachean Ethics

Step 1: know where to start… and when to move on

Working in tech and especially within the data space, you can play office buzzword bingo every time a new solution is mentioned in earnest. There is so much out there, how do you know which ones are worth the time and which ones aren’t?

The answer is simple, if not a little cynical: you don’t know, you can’t know, and no one else can tell you.

My argument is this: just pick one.

In my opinion, what you choose to invest time into matters much less next to how much time you choose to invest in it. Pick what interests you, what gets mentioned the most at your office, or best yet, pick that technology that you are always talking about yourself, but have never actually implemented.

The key here is to actually pick something, and then, know when to move on.

Your professional life will dictate when you need to go into the extreme details on a technology. If you do the same with your personal time, that cool-hip-new technology you are diving into will be just one speck among billions by the time you are done.

Step 2: skip to step 3

No seriously, the most important thing to do in step 2 is to skip to step 3. If you get stuck, come back to step 2 for a brief visit, but the goal should be on building something tangible.

Of course, if you don’t understand the basic concepts of the technology you are attempting to study, you may get stuck before you start. I promise you, the Wikipedia articles, YouTube videos and online courses will be much easier to digest if you’ve actually attempted to play with the tech yourself.

Google is, and for the foreseeable future will remain, the king of search and the primary way we begin our searches for new information. If you need something more structured, some of my favorite resources include DataCamp, Coursera, stuDIY. If you are willing to spend a little cash, Lynda and Pluralsight are other great options.

Step 3: build something

This seems daunting, especially if you are just starting this method for the first time, but let me give you some pointers on where to start.

First, you will need a development environment. You can use your personal laptop or your work computer, but why not buy a Raspberry Pi and for $25 you already have your first project. Even easier, sign up for a Microsoft Azure account for free and get a $200 credit to start learning how to deploy web applications, analytics and data platforms on the cloud.

No time attempting to build something is wasted time. You are learning here.

Here are a couple of starting ideas:

Build a bot for your favorite instant messenger and deploy it on a server you set up yourself

Deploy an open source database (such as PostgreSQL) on a Raspberry Pi. Build a script to scrape a website (Twitter is a good option) and then use R or Python to ‘science’ your data.

Build an arcade from an emulator (we have one of these at our office thanks to our very hands-on CTO)

Build a custom function for your Amazon Echo or Google Home

Rinse and Repeat

Take action. Be hands on. Manage you free time by investing in only what is important (what is interesting to you). Know when to move on. Repeat and always keep learning.