Learning Versus Remembering

Via R-Bloggers, a discussion on learning versus remembering with respect to data science:

If you’re like most aspiring data scientists, you’ll try to learn this code by using the copy-and-paste method. You’ll take this code from a blog post like this, copy it into RStudio and run it.

Most aspiring data scientists do the exact same thing with online courses. They’ll watch a few videos, open the course’s sample code, and then copy-and-paste the code.

Watching videos, reading books, and copy-and-pasting code do help you learn, at least a little. If you watch a video about ggplot2, you’ll probably learn how it works pretty quickly. And if you copy-and-paste some ggplot2 code, you’ll probably learn a little bit about how the code works.

Here’s the problem: if you learn code like this, you’ll probably forget it within a day or two.

This is a thought-provoking article that applies to all disciplines, not just data science.

Related Posts

Reviewing The Team Data Science Process

I am starting a new series on launching a data science project, and my presentation quickly veers into a pessimistic place: The concept of “clean” data is appealing to us—I have a talk on the topic and spend more time than I’m willing to admit trying to clean up data.  But the truth is that, in a […]

Read More

Methods To Improve Model Accuracy

Tristan Robinson shows how to go back to the drawing board when your model’s accuracy isn’t cutting it: One of the reoccurring principles that appears with machine learning is that of Ockham’s razor, which states that the best models are simple models that fit the data well; this is not an irrefutable principle of logic, but […]

Read More

Categories

December 2016
MTWTFSS
« Nov Jan »
 1234
567891011
12131415161718
19202122232425
262728293031