The difference between a beginner and someone who is experienced is confidence, which seems circular because confidence is built through experience.
But that is because we use “experienced” as a synonym for competence, and there is a misconception in the amount of experience needed to achieve competence.
One of my favorite, favorite, favorite TEDx Talks is Josh Kaufman’s “The First 20 Hours: How to Learn Anything.” In it, he dispels the myth that it takes 10,000 hours to learn something. It doesn’t take anywhere near that many hours, and I can personally attest to that.
This past January, I started simulating data for our Industrial Machine Learning Offering. I’ve already written about a method I developed for simulating progressive dates in a dataset. However, prior to doing this, I had never simulated data and would have described myself as an advanced novice R user.
I had worked a few simple projects and had gone through several tutorials, but a tutorial is like being introduced to your neighbor’s friendly dog.
Whereas real-world programming is like encountering a pack of wolves while hiking in the backcountry – related and yet doesn’t really compare.
The disconnect between the simplicity of tutorials and the complexity of reality can be discouraging to a learner. I know it’s left me feeling that I need to study more: read another book, blog, tutorial, take a class at my local college … anything to move beyond being a novice.
However, while these can all be beneficial, they don’t build experience. Time and struggle build experience — and by extension, confidence. But the amount of time needed to get there is less than most people think.
In the past two months, I have spent 20 to 40 hours working on the simulations, and this has significantly improved my programming abilities. Am I fast? No, not particularly. Especially, when I am encountering a problem for the first time. However, I have practiced enough now to feel confident that I can find an answer to any problem that I’ll face.
If you don’t have work projects that will give you practice opportunities, there are plenty of free options on the Web. A quick google search of Data Science Projects provides plenty of good links. Try them out, and gain the practice that will take you from novice to competent. It’s closer than you think.
Logan Wilt is a Data Scientist within CSC’s ResearchNetwork, and most recently was the Taxonomy Architect for CSC’s Integrated Workforce Management Center of Excellence. She is interested in practical tactical data science applications as well as leveraging semantic technologies for better data discovery.