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Thinking like a Computer: The Fundamentals of Programming

Authors: Marium Afzal Khan; Wilson, Aidan;

Thinking like a Computer: The Fundamentals of Programming

Abstract

About this webinar Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a time, and can keep the results in a large bank of short-term memory for quick recall. These two approaches are fundamentally different. Humans can only reasonably retain seven plus or minus two pieces of information in short-term memory, and new items push older items out, whereas a computer is hopeless when given multiple pieces of information simultaneously. Understanding this fact is key to being able to write instructions for computers – also known as programs – in a way that takes advantage of their strengths, and overcomes their drawbacks. Suitable for the programming novice, this webinar is good preparation for researchers wanting to learn how to program. Webinar Topics How a human solves tasks How a computer solves tasks Overview of programming concepts: Variables Loops Conditionals Functions Data types Licence Copyright © 2021 Intersect Australia Ltd. All rights reserved.

Keywords

algorithms, programming

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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