This roundup by Lorena Barba is a great summary of all the things I’ve been finding on the Web before I bumped into her writings. The basic premise behind computational thinking is that it’s NOT about knowing how to write code.
All roads lead to finding the foursome of:
- (problem) decomposition
- abstraction
- pattern matching
- algorithms (and procedures)
And then there’s the additions by Google in 2016:
- automation
- data collection and analysis
- data representation
- modeling and simulation
- parallelization
And if you are a master of CT (Computational Thinking) then you need these skills:
- confidence in dealing with complexity,
- persistence in working with difficult problems,
- the ability to handle ambiguity,
- the ability to deal with open-ended problems,
- setting aside differences to work with others to achieve a common goal or solution, and
- knowing one’s strengths and weaknesses when working with others.
