Computational Thinking
a peek into world of computation
What is computational thiking?
Computers are used to help us solve problems. Before solving a problem we need to understand the problem and the ways in which problem need to be solved. We can do this using COMPUTATIONAL THINKING. By using COMPUTATTIONAL LANGUAGE we can break down a problem into bite-sized pieces that computer understands and helps in solving problem.
Computational thinking helps in building skills like confidence in dealing with complexity, persistance in working with difficult problems, tolerence of ambiguity, the ability to deal with open-ended problems and the ability to communicate and work with.
Thinking computationally is not programming. It’s not even thinking like a computer because computers do not and cannot think. Programming tells a computer what to do and how to do whereas, computation thinking enables us to work out exactly what to tell computer to do.
Computational thinking isn’t just used by computer scientists and programmers. It’s used by people in all kinds of professions, like doctors, carpenters, teachers, and artists.
For example, think about your process when you’re baking cupcakes on a Sunday afternoon. You break the recipe into smaller steps you do one at a time, and you know to bake them slightly longer than the recipe calls for because you’ve had to do that before. You also know chocolate chips aren’t a vital ingredient in your cupcakes, so you skip that step because you don’t have any in your pantry, and you know it’s a good idea to start preheating the oven before you pour the mix into the cupcake cases.
THE FOUR COURNERSTONES / FUNDAMENTALS OF COMPUTIONAL THINKING
There are 4 key techniques to computational thinking. They are:
Decomposition : the process of breaking a large problem into more manageable sub-problems.
Pattern recognition :the process of finding the similarities or patterns among small, decomposed problems
Abstraction : the process of filtering out — ignoring — the characteristics of patterns that we don’t need in order to concentrate on those that we do.
Algorithms : a plan, a set of step-by-step instructions to resolve a problem.
Computational thinking in practice
When we look at a complex problem for the first time, we don’t understand how to solve it easily.
Computational thinking involves taking that complex problem and breaking it down into some and more manageable problems ( decomposition ). Each of these smaller problems can then be looked individually, considering how similar problems have been solved previously ( pattern recognition ) and focusing on important details while ignoring irrelevant information( abstraction ) . Followed by designing rules and small steps to solve each of the smaller problems ( algorithms ).
Finally, these simple steps are used to program a computer to help solve the complex problem in the best way.
How to use computational thinking
Computational thinking is often not only used in different domains of Computer science but also in different disciplines.In mathematics, students use decomposition in solving complex problems. In geometry, identifying symmetry requires pattern recognition.When students approach an essay, they likely break the essay into the central argument, the introduction, and the conclusion. They write the sections separately and then combine
them for a full argument. Solving word problems requires students to
comb through the information to pull out what’s necessary.
Computational thinking in data science
Python and R are the top two programming languages for data science, the basic skill In data science is mastering the logical reasoning and flow of the process, not the programming language used.
As computational thinking involves breaking down a project into smaller parts and understand how these steps are interrelated it has nothing to do with programming languages which will be selected at last for program implementation. So if you understand the logical reasoning and flow of the process, you can implement the project using any of the programming languages.
■ Data science problems are usually posed on a dataset which can be obtained from real life artefacts time tables, shopping bills, transaction logs … or may be available in a digital format typically in the form of tables
■ Computational thinking in datascience involves finding patterns in methods used to process these datasets. Through computational thinking, several concepts and methods were introduced for doing this Typically involves first scanning the dataset to collect relevant information Then processing this information to find relationships between data elements Finally organising the relationships in a form that allows questions to be answered easily
Online Learning Resources for Computational Thinking
Many premier universities around the world have computational thinking course in their curriculum as beginning for programming and data science. you can explore many online courses and books to get started with computational thinking. I will be mentioning some of the Resources below, be curious to explore them.
