Unit 4 Programming Assignment 1
Assignment 1 - Content of Programming
Explain how computational thinking skills are applied in finding solutions that can be interpreted into
software applications?
Computers can support us in finding solutions through breaking it down into smaller pieces and
making it easier to apply logical solutions. Computational thinking allows one to take a problem,
examine it, and create a solution. I can use a computer and present the solutions or understand it.
Computational thinking is a problem-solving solution that incorporates critical and computational
thinking. Computational thinking is a problem-solving approach that uses critical and logical thinking
to solve problems. Pattern recognition is finding similarities within and problems and using what has
worked in the past to help solve the task. Computational thinking is also thinking like a computer.
You cannot solve problems that can be solved by computers. Solving problems that they can be
solved by computers, it cannot have of problems that can be solved using the computer, but the
software will likely be more elegant and offer better ways for the user to interact. For example, the
quality of the user interface introduced by the iPhone turned mobile phone development.
Companies were competing to create the most complicated keyboards.
Principles of computer programming are applied in different languages to produce software
applications is described below in detail
The types of programming languages are:
functional programming language
scripting programming language
logic programming language
procedural programming language
object oriented programming language
The programming language authorizes us to write dynamically programs and advance online
explications such as - games, web applications, and mobile applications, etc. Programming is
employed to assemble, maintain, measure, and interpret the processing of the data and knowledge.
Computational reasoning abilities fundamentally give the capacity and ability to learn the PC
programming language and it additionally assumes a significant part in the product planning
measure. It likewise helps in improving the quality sorts of programming applications in the
programming world. The computational reasoning gives different kinds of advantages in the product
application in that it breaks the framework issue into the little parts so it can without much of a
stretch oversaw. It helps on zeroing in just on the significant and powerful data or the information
and overlooks the immaterial data in the product planning interaction and applications.
Issue decomposition, pattern recognition, and abstraction are all aspects of computational thinking.
Software better thought of as the processes or steps that lead to the development of working
software that solves problems for its users. Taking problems which are defined as requirements for
that software then understanding those problems and requirements well enough to be able to build
a computerised solution.
,What role does problem decomposition play in the process of creating working software which
solves users’ problems?
Software quality is a measure of success of software design - i.e. describing how effective/successful
the software is at solving those problems, so after looking at how those things affect software
design, consider each of them for software quality.
How does success or failure at problem decomposition affect the efficacy of the software at solving
users' problems? - e.g. what if the problem itself isn’t understood properly, or if details about the
problem are overlooked? or if some false assumptions are made?
How does success or failure at Pattern Recognition affect the efficacy of the software at solving
users' problems? - e.g. what if some obvious patterns are missed or the patterns are misunderstood?
How does success or failure at Abstraction affect the efficacy of the software at solving users’
problems? - e.g. what happens if a wrong abstraction is used? or the abstraction leaks.
Computational thinking skills
Computational thinking skills are using methods that a computer would use to solve
problems. It’s basically breaking down a problem to smaller problems and making it
manageable to apply solutions.
Decomposition - means breaking down a problem into tasks in order to make the
problem approachable.
To use decomposition first have to identify and describe the problem and processes that
trying to solve. Then start breaking down the problem into smaller tasks
algorithms - developing a step-by-step solution to the problem, the rules to follow to solve the
problem
Why should use decomposition
Makes it easier to focus on one thing rather than focusing on the problem.
- By breaking down problems it allows you to examine everything in good detail.
- The smaller the tasks are the easier it is to understand and solve them.
- It also makes the problems more manageable and it allows you to set for the tasks.
- We use decomposition in tasks.
Comparing to just trying solve the problem without breaking it down to decomposition. It is
hard the problem is not broken into smaller tasks, because it might be big and the people to tackle
such error straight away.
Decomposition is mistakes. Breaking down the task into smaller tasks.
, I think decomposition is important for each assignment or issue you are doing. This strategy is basic.
It can diminish the pressure and energy on assignments. Individuals will in general disregard this
strategy or truly downright awful they are simply making themselves the undertaking harder, by
separating the errand into more modest assignments and gradually. This kind of impact it does to
the individual who utilizes deterioration is truly gainful, on the grounds that it causes them to feel
stuck.
Pattern recognition
Patterns are characteristics that could be found anywhere all trees have leaves. Knowing these types
of characteristics, we can try to replicate them by any way. The things that could be different are the
specifics that are unique.
Computational thinking unless they has a pattern. The reason why they aren’t used is because the
point of pattern recognition, is to make it easy.
Comparing pattern recognition to just making unique patterns every single time to meet
similarities. Pattern recognition is easier and used than any method. It’s most
used in factories, where machines produce objects that fits the same pattern. If factories
wouldn’t use pattern to produce products, they would make more products with defects. Its
impossible for them to produce something that is completely unique each time. They have to follow
pattern. If the factories wouldn’t use machines, it would massively reduce the production rate, that
is why pattern recognition is important. I believe pattern recognition is a good method for looking
into similarities of objects or tasks and applying methods to creating the same object.
Generalisation and abstraction
Since generalisation and abstraction are similar, they are used together. The goal of generalisation
and abstraction is to create complex processes easier to read by ignoring ambiguous details and
filtering out insignificant characteristics. Humans, for example, have characteristics such as standing
on two legs or having two arms and one head. These characteristics are basic and not as detailed as
the others, although they are necessary.
Why should use Generalisation and Abstraction?
When there aren't enough specifics, it makes patterns easier to understand.
- Saves time
- Helps focused on the important tasks and prevents from getting disturbed.
Representing parts of problem and the system?
Representing parts of problem and system, is identifying what is required to solve problems or to
build system and see how the solutions together.
Why is it important?
This type of computational thinking is important because it helps in the analysis of problem-solving
strategies. In the solution, you're basically laying out the most important tools for the problem, not
Assignment 1 - Content of Programming
Explain how computational thinking skills are applied in finding solutions that can be interpreted into
software applications?
Computers can support us in finding solutions through breaking it down into smaller pieces and
making it easier to apply logical solutions. Computational thinking allows one to take a problem,
examine it, and create a solution. I can use a computer and present the solutions or understand it.
Computational thinking is a problem-solving solution that incorporates critical and computational
thinking. Computational thinking is a problem-solving approach that uses critical and logical thinking
to solve problems. Pattern recognition is finding similarities within and problems and using what has
worked in the past to help solve the task. Computational thinking is also thinking like a computer.
You cannot solve problems that can be solved by computers. Solving problems that they can be
solved by computers, it cannot have of problems that can be solved using the computer, but the
software will likely be more elegant and offer better ways for the user to interact. For example, the
quality of the user interface introduced by the iPhone turned mobile phone development.
Companies were competing to create the most complicated keyboards.
Principles of computer programming are applied in different languages to produce software
applications is described below in detail
The types of programming languages are:
functional programming language
scripting programming language
logic programming language
procedural programming language
object oriented programming language
The programming language authorizes us to write dynamically programs and advance online
explications such as - games, web applications, and mobile applications, etc. Programming is
employed to assemble, maintain, measure, and interpret the processing of the data and knowledge.
Computational reasoning abilities fundamentally give the capacity and ability to learn the PC
programming language and it additionally assumes a significant part in the product planning
measure. It likewise helps in improving the quality sorts of programming applications in the
programming world. The computational reasoning gives different kinds of advantages in the product
application in that it breaks the framework issue into the little parts so it can without much of a
stretch oversaw. It helps on zeroing in just on the significant and powerful data or the information
and overlooks the immaterial data in the product planning interaction and applications.
Issue decomposition, pattern recognition, and abstraction are all aspects of computational thinking.
Software better thought of as the processes or steps that lead to the development of working
software that solves problems for its users. Taking problems which are defined as requirements for
that software then understanding those problems and requirements well enough to be able to build
a computerised solution.
,What role does problem decomposition play in the process of creating working software which
solves users’ problems?
Software quality is a measure of success of software design - i.e. describing how effective/successful
the software is at solving those problems, so after looking at how those things affect software
design, consider each of them for software quality.
How does success or failure at problem decomposition affect the efficacy of the software at solving
users' problems? - e.g. what if the problem itself isn’t understood properly, or if details about the
problem are overlooked? or if some false assumptions are made?
How does success or failure at Pattern Recognition affect the efficacy of the software at solving
users' problems? - e.g. what if some obvious patterns are missed or the patterns are misunderstood?
How does success or failure at Abstraction affect the efficacy of the software at solving users’
problems? - e.g. what happens if a wrong abstraction is used? or the abstraction leaks.
Computational thinking skills
Computational thinking skills are using methods that a computer would use to solve
problems. It’s basically breaking down a problem to smaller problems and making it
manageable to apply solutions.
Decomposition - means breaking down a problem into tasks in order to make the
problem approachable.
To use decomposition first have to identify and describe the problem and processes that
trying to solve. Then start breaking down the problem into smaller tasks
algorithms - developing a step-by-step solution to the problem, the rules to follow to solve the
problem
Why should use decomposition
Makes it easier to focus on one thing rather than focusing on the problem.
- By breaking down problems it allows you to examine everything in good detail.
- The smaller the tasks are the easier it is to understand and solve them.
- It also makes the problems more manageable and it allows you to set for the tasks.
- We use decomposition in tasks.
Comparing to just trying solve the problem without breaking it down to decomposition. It is
hard the problem is not broken into smaller tasks, because it might be big and the people to tackle
such error straight away.
Decomposition is mistakes. Breaking down the task into smaller tasks.
, I think decomposition is important for each assignment or issue you are doing. This strategy is basic.
It can diminish the pressure and energy on assignments. Individuals will in general disregard this
strategy or truly downright awful they are simply making themselves the undertaking harder, by
separating the errand into more modest assignments and gradually. This kind of impact it does to
the individual who utilizes deterioration is truly gainful, on the grounds that it causes them to feel
stuck.
Pattern recognition
Patterns are characteristics that could be found anywhere all trees have leaves. Knowing these types
of characteristics, we can try to replicate them by any way. The things that could be different are the
specifics that are unique.
Computational thinking unless they has a pattern. The reason why they aren’t used is because the
point of pattern recognition, is to make it easy.
Comparing pattern recognition to just making unique patterns every single time to meet
similarities. Pattern recognition is easier and used than any method. It’s most
used in factories, where machines produce objects that fits the same pattern. If factories
wouldn’t use pattern to produce products, they would make more products with defects. Its
impossible for them to produce something that is completely unique each time. They have to follow
pattern. If the factories wouldn’t use machines, it would massively reduce the production rate, that
is why pattern recognition is important. I believe pattern recognition is a good method for looking
into similarities of objects or tasks and applying methods to creating the same object.
Generalisation and abstraction
Since generalisation and abstraction are similar, they are used together. The goal of generalisation
and abstraction is to create complex processes easier to read by ignoring ambiguous details and
filtering out insignificant characteristics. Humans, for example, have characteristics such as standing
on two legs or having two arms and one head. These characteristics are basic and not as detailed as
the others, although they are necessary.
Why should use Generalisation and Abstraction?
When there aren't enough specifics, it makes patterns easier to understand.
- Saves time
- Helps focused on the important tasks and prevents from getting disturbed.
Representing parts of problem and the system?
Representing parts of problem and system, is identifying what is required to solve problems or to
build system and see how the solutions together.
Why is it important?
This type of computational thinking is important because it helps in the analysis of problem-solving
strategies. In the solution, you're basically laying out the most important tools for the problem, not
