The phrase "algorithmic thinking" is correct and usable in written English.
It can be used when discussing problem-solving approaches that involve the use of algorithms or systematic methods to analyze and solve problems.
Example: "Developing strong algorithmic thinking skills is essential for computer science students as it helps them tackle complex programming challenges."
Alternatives: "computational thinking" or "algorithmic reasoning".
"It teaches computational and algorithmic thinking [solving problems that don't have right or wrong solutions], logic, trial and error, collaboration.
Selby and Woollard (2013) addressed that problem and made a review of literature to propose a definition based on elements that are widely accepted: abstraction, decomposition, evaluations, generalization, and algorithmic thinking.
IBMers could have taught it to round the bets to the nearest $20 bill, but they decided to keep it precise to make the cold, algorithmic thinking more transparent.
Computers are an aggregation of the long existing mechanistic and mathematical or algorithmic thinking traditions that used to be separated.
Algorithmic thinking is a core skill for constructing algorithms to solve problems and for understanding computer science.
Apple data visualizer and MIT System Design and Management graduate Ali Almossawi solves these common dilemmas and more in his new book, "Bad Choices: How Algorithms Can Help You Think Smarter and Live Happier," a quirky, illustrated guide to algorithmic thinking.
Similarly, Stein and Lane [37] characterize 'doing mathematics' as "The use of complex, non-algorithmic thinking to solve a task in which there is not a predictable, well-rehearsed approach or pathway explicitly suggested by the task, task instructions, or a worked out example.
As a computer science major, you'll learn algorithmic ways of thinking and study the elements of computing and information technology such as system design, problem specification, programming, and the modeling, analysis and evaluation of complex systems.
This having said, I now want to argue that the algorithmic "NFL-way" of thinking about evolution is very meaningful when it concerns computer simulations of certain evolutionary processes.
Or is because there is something more to thinking than simple algorithmic computation, and that true reason must, and ought to, be the slave of passions?
He didn't want to walk off the beach to get another book, and he didn't want to lie on the beach and dig moist holes with his feet, thinking about the algorithmic beauty of seaweeds.
