3 stages of Learning Data Science
By: Kurtis Pykes
Tags: Machine Learning, Data Science, Towards Data Science, Artificial Intelligence, Deep Learning

Associative Stage

Problem: The problem we are solving in this stage is that of transferring potential power into realized power.

We have all heard the saying that knowledge is power. In the rise of the information age, where there is tons of information that we have access to through one or two clicks, it turns out that knowledge isn’t exactly power. Knowledge is potential power, since knowing something doesn’t necessarily affect change.

We accumulated the know how of the skill we want to learn in the cognitive stage, amassing bags of potential power in the process. To realize that power we must act on it.

Practice, Practice, and more Practice!

I’ve been an athlete on two counts, as you see from my brief CV above, and one thing I can recall from this phase is the hours of repetition that go into it. I’d perform the same reverse kick for an hour, or when I was playing Football, i’d be taking free-kicks for hours to perfect my technique (If I can’t find a video of me, I may get my boots of the hanger if enough people request to see).

Photo by Hayley Catherine on Unsplash

In relation to Data Science, this could be simply feature engineering. Want to be good? Take the knowledge you’ve learnt about feature engineering from the cognitive stage and deliberately apply them to different datasets and evaluate how it affects the outcome of your model. In doing this, you get to learn what strategies are effective for different datasets and essentially eliminate things that don’t work.

Here’s my article on Feature Engineering to get you started…

Characteristics: This stage requires plenty of conscious effort. In fact, it may feel extremely awkward to begin with and require lots of little adjustments to improve your performance. Completing task may take you hours, days or a week longer than experienced people, but it is key to remember that once upon a time they were also like you and they went through this phase.

Goal: The goal here is to put together lots of small skills that in turn will accumulate to make you unrecognizable to your peers in the future.

Autonomous Stage

Woooosaahhh… The state of Flow.

Photo by Gordon Williams on Unsplash

We hear about it in the podcast, The TED talks, etc. The state of flow what we all crave.

In this state we can perform at maximum levels of proficiency. We aren’t so focused on the skill because we can do it automatically — meaning we don’t have to think about it, it just flows.

Well where does this fit in, in Data Science? Think of it like this, have you ever seen when a new competition starts on Kaggle and it’s always the same people that shoot straight to the top of the leader board? Yes, them! They are in flow. They have learnt about competing on Kaggle, what works and what does not. They have practiced, over and over on countless competitions and now what you see when they shoot to the top is the fruits of their labor.

The skill has become programmed into your subconscious mind and you can now direct your focus to other aspects of your performance.

Conclusion

This process can take a long-time but trusting the process makes the journey lighter. Although a key thing to remember is that the automatic phase will reinforce any bad habits that are picked up along the way and bad habits are very hard to change. Learning best practices from seasoned professionals is always the best way to start rather than having to come back to it.

If there is anything that you think I have missed or some points you don’t agree with, your feedback is valuable. Send a response! If you’d like to get in contact with me, I am most active on LinkedIn and i’d love to connect with you also.

Here are some of my other articles that you may find interesting…