AI News, How much data science do you actually remember?

How much data science do you actually remember?

How many data science books have you read?

(I’d be willing to bet: you’ve read dozens.) If you’re like most budding data scientists, you’ve probably consumed a lot of material.

The problem though, is that the vast majority of people learn but then quickly forget.

If you’re like most aspiring data scientists, you’ll try to learn this code by using the copy-and-paste method.

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.

If you learn some R syntax (or a data science concept) today, it’s effectively worthless if you forget it by next week.

Will you tell them “I don’t remember how to write the code, but I have a data-science certificate from an online learning course?”

If you can’t write the code fluently, on command, no good company will hire you.

As long as you’re a decent person and don’t have any major character flaws, your mastery of R and data science concepts will help you nail the interview and get the job.

That’s just the way that things are right now … most good companies operate lean, and they expect you to perform.

know a few people who, either through luck or guile, have obtained good data jobs at good companies, even though they aren’t good data scientists.

If you’re working on a hard project and using advanced procedures, you’ll need to consult reference materials sometimes.

If you want to master data science, you need to know how to learn, so that you can learn data science efficiently and effectively.

Ironically, while people in the tech industry are ecstatic about machine learning, no one is paying any attention to human learning.

I won’t explain my complete thoughts on human learning in this blog post (I’ll eventually write a separate blog post), but I think that human learning is one of the most important subjects of this century.

As they continue to develop, many of them supplement their practical, hands-on skill (i.e., playing the notes) with music theory.

There is certainly a theoretical component (similar to music theory), but in practical terms, writing data science code is a hands-on skill.

If you want to discover how to learn, practice, and ultimately remember data science, sign up for our email list.

In coming weeks and months, Sharp Sight will be writing extensively on data science learning.

If you sign up to the email newsletter, you’ll learn the tips, hacks, and strategies for rapidly learning data science (so that you never forget).

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