AI News, A quick guide to deliberate practice for data science
- On Sunday, June 3, 2018
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A quick guide to deliberate practice for data science
When you ask people what makes a person great – what makes someone an elite performer – they commonly say “talent.”
From the outside, people look at top performing data scientists and say, “that could never be me … I don’t have that gift.”
In data science and more generally, people think that innate talent is what causes exceptional performance.
As it turns out, there has been extensive research on elite performers of all kinds: musicians, doctors, chess players, even mathematicians.
They categorized the performance abilities of these students into 5 (i.e., the top performers, middling performers, low performers, etc).
According to Anders Ericsson, a famous researcher of human expertise and human performance, when most people practice they focus on things that they already know how to do.
If you you’ve already mastered the syntax to create a bar chart, but you keep practicing it intensely without moving on to more advanced techniques, you are unlikely to improve as a data scientist.
However, if you don’t push yourself forward by expanding your skills to more advanced techniques, then you won’t develop as a data scientist.
Another example is with musicians: if you’re a musician, and you learn only 5 simple songs, and you only play those 5 songs for 10 years, you won’t improve.
To quote Anders Ericsson: “[Deliberate practice] entails considerable, specific, and sustained efforts to do something you can’t do well—or even at all.”
What this means for you, as a developing data scientist, is that you need to practice in a way that: These principles will help you develop a good practice system.
However, to give you a clearer understanding of how to optimize your practice, let’s dig a little deeper into the nature of deliberate practice.
In Talent is Overrated, Colvin gives the example of Tiger Woods dropping a ball in a sand trap and deliberately stepping on it to press the ball further into the sand, making the shot very, very challenging.
So for example, if you’ve already learned the basics of ggplot2, like geom_line(), geom_bar(), geom_point(), pushing yourself might mean learning the theme() function and the corresponding element functions.
As a beginning or intermediate student, it will be difficult for you to design a system yourself that teaches you the right skills, in the right order, in such a way that you’re continuously challenged by your practice.
In most cases, I suspect that this is because the course lacks a well designed practice system to teach you skills, but then push you beyond your skill level to higher and higher levels of mastery.
One way or another, I highly recommend that you create or invest in a practice system that is designed to improve your performance.
Again, you frequently hear people say “I took several online courses, but I still can’t write R code very well.”
If you want to be a top-performing data scientist, you need to repeat your data science practice until you can perform techniques unconsciously (i.e., without thinking about it).
For example, if you want to learn data science, but you don’t even know which packages to learn, you would be “unconsciously incompetent.”
For example, the first time you learn how to create a bar chart with geom_bar(), you’ll essentially be consciously incompetent.
For example, if you systematically and repeatedly practice the techniques from ggplot2 and dplyr for a couple of weeks, you’ll eventually reach a point where you can execute those techniques.
Finally, if you stick with it and you systematically practice your data science techniques, you’ll reach unconscious competence.
Your goal should be unconscious competence in the techniques of the tidyverse, like ggplot2, dplyr, stringr, and tidyr functions.
Moreover, once you start using a feedback-driven practice system to learn data science, your progress will accelerate.
If you’re really pushing yourself to learn and practice skills that are out of your comfort zone, it’s going to hurt a little.
For example, the first time you start learning the functions from the tidyr package (which I recommend if you’re a beginner), they might be a little difficult to understand.
The process of learning new, advanced techniques (and pushing yourself to memorize the syntax) is hard.
The challenge for you, as someone who’s learning data science, is that you need a practice system that continuously pushes you beyond your skill level towards techniques of increasing difficulty.
I think you can learn data science 2x, 3x, even 5x faster than average, if you know how to practice.
If you can embrace the fact that deliberate practice – the type of practice that leads you to mastery – will be hard sometimes, then you can succeed.
“Deliberate practice requires that one identify certain sharply defined elements of performance that need to be improved, and then work intently on them.”
As a data scientist, this means that you should sharply define individual techniques, practice those techniques repeatedly, and move on to harder techniques as you progress.
As I wrote in a recent article, the modular nature of R’s tidyverse makes it somewhat easy to define “practicable techniques.”
promise you though, if you can commit to practice, and you practice data science the right way, then you can learn data science very, very quickly.
- On Sunday, August 18, 2019
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