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Unlock the secrets of efficient coding, develop an in-depth understanding of different strategies, and learn how decision-making plays a significant role in using problem-solving techniques in Computer Science. This enlightening journey begins with an exploration into the definition of problem-solving techniques and their paramount importance in Computer Science. You further…
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Problem Solving Techniques
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Unlock the secrets of efficient coding, develop an in-depth understanding of different strategies, and learn how decision-making plays a significant role in using problem-solving techniques in Computer Science. This enlightening journey begins with an exploration into the definition of problem-solving techniques and their paramount importance in Computer Science. You further discover the basic problem-solving methods, their practical applications, and how these foundational skills apply directly to coding.
Going deeper, you explore seven pivotal problem-solving techniques, understanding their concepts and their indispensable uses in Computer Science. Finally, learn the nuances involved in contrasting problem-solving and decision-making techniques, the subtleties that set them apart, and ways in which they can be combined for the most effective results, in terms of both efficiency and creativity.
Understanding Problem-Solving Techniques
Problem-solving techniques in computer science are the protocols, procedures, or methods employed to identify the root cause of a problem and construct an efficient solution.
Definition of problem-solving techniques in Computer Science
Problem-solving techniques in computer science refer to the methods used to find solutions to complex issues using algorithmic or heuristic approaches. These techniques can be systematic, analytical, or intuitive, encompassing traditional programming, machine learning, or artificial intelligence methods.
These techniques are used in various domains within computer science, including data analysis, software development, network troubleshooting, and cybersecurity. For example, in software development, problem-solving may involve Debugging an application. Here, the issue could be a broken functionality within the application, and the solution might be modifying a specific segment of code.
At a software development company, the team notices that their mobile application crashes whenever a user tries to upload a profile picture. By employing problem-solving techniques such as Testing , the team identifies that the crash occurs due to a buffer overflow when processing large images. Once identified, they solve this problem by modifying the code to handle large image sizes better.
Importance of problem-solving techniques in Computer Science
Problem-solving techniques are the cornerstone of computer science. From designing efficient algorithms for a given task to optimising or guaranteeing certain performance metrics, these techniques are used daily. Here's why they're important:
- Mitigating runtime errors and system crashes: By identifying and rectifying coding mistakes effectively.
- Optimizing software: Problem-solving techniques can help improve the efficiency of software, leading to enhanced user experience and reduced resource consumption.
- Data analysis: They help in organizing, evaluating, and interpreting complex datasets to derive meaningful insights.
- Cybersecurity: By identifying potential vulnerabilities and patching them before they can be exploited, thereby safeguarding digital assets.
In the domain of machine learning, problem-solving techniques are even more paramount. Here, problems can include determining the best machine learning model for a specific task, tuning the hyperparameters of a model, or dealing with issues like data imbalance or overfitting. These techniques can guide computer scientists in their quest to develop robust, accurate machine-learning models that can make sense of vast, complex data.
Given the rapidly evolving nature of computer science, mastering various problem-solving techniques is essential to stay ahead in this field. It helps you adapt to new advancements and tackle a wide range of challenges that come your way.
Basic Problem-Solving Techniques
Before diving into advanced, specialized techniques for solving problems, it is essential to become proficient in the fundamentals, which transcend specific problem domains and provide a solid foundation for exploring more complex areas within computer science.
Introduction to basic problem-solving techniques
There are several standard problem-solving techniques that you can employ irrespective of the field of study in computer science. The first step, however, is always understanding the problem, then you can choose the right strategy to solve it. Here are some of the basic problem-solving methods that are particularly useful:
Divide and Conquer: This technique involves breaking a larger problem into smaller, more manageable parts, solving each of them individually, and finally combining their solutions to get the overall answer.
Consider an example in the context of sorting a list of numbers. Using a divide-and-conquer algorithm like Merge Sort , the list is continually split in half, until you reach lists of size one. These lists are inherently sorted, and then you recursively merge these sorted lists, resulting in a fully sorted list.
Algorithm Design: This technique involves formalizing a series of organized steps into an algorithm to solve a specific problem. Common approaches include greedy algorithms, dynamic programming, and Brute Force .
Heuristics: These are rules of thumb or educated guesses that can help you find an acceptable, if not the perfect, solution when the problem is too complex for a direct mathematical approach, or when computational resources are limited.
Heuristics are not guaranteed to yield the optimal solution but are often good enough for practical purposes and can dramatically reduce the time and resources needed to find a solution.
Recursive Thinking: Recursion is predicated on solving a problem by breaking it down into smaller instances of the same problem. The idea is that, eventually, you will get to a problem that is small enough to solve directly.
Even though these techniques might sound simple, they form a cornerstone and are often cloaked within complex problem-solving techniques used in higher-level computer science.
Practical application of basic problem-solving techniques
The practical application of basic problem-solving techniques in computer science is broad and varied, depending on the specific domain. However, some applications cut across most sectors of computer science:
Each technique has its strengths and weaknesses, and the key is knowing which technique (or combination of techniques) to use for a particular problem. Remember, the goal is not just to find any solution, but to find the most efficient one possible.
Other fields, too, benefit from these problem-solving techniques. For example, bioinformatics implements algorithm design to match genetic sequences, while digital forensics employs divide-and-conquer techniques to sift through large amounts of data during an investigation. Moreover, heuristics play a significant role in the burgeoning field of AI, proving that these problem-solving techniques not only provide a solid foundation for computer science but also have real-world applications.
Coding Problem-Solving Techniques
Delving into the more specific realm of coding within computer science, the arsenal of problem-solving techniques takes on facets best suited for resolving issues related to programming and development.
Importance of coding problem-solving techniques in Computer Science
Coding problem-solving techniques are the tools that software developers use to create, optimise, and manage software applications effectively. These techniques play an instrumental role in many aspects:
- Enhancing code efficiency: Efficient code is faster to execute, consumes less memory, and results in responsive, user-friendly applications. For instance, choosing an optimal sorting algorithm based on the size of the list can markedly improve runtime.
- Mitigating errors: Through structured Debugging and systematic thinking, developers can track and rectify logic errors, Syntax Errors , or runtime exceptions, leading to robust, error-free code.
- Facilitating code readability and maintenance: Good coding practices, such as following a consistent naming scheme and using descriptive comments, make code easier to understand, troubleshoot, and maintain – essential when working in a team.
- Implementing complex functionalities: Many modern applications require intricate algorithms, use elaborate Data Structures , and handle large volumes of data. Mastery of coding problem-solving techniques enables developers to tackle these challenges effectively.
Examples of coding problem-solving techniques
There's a myriad of coding problem-solving techniques at a developer's disposal. These methods typically supplement basic problem-solving techniques with practices tailored for the coding environment. Let's delve into a few:
Debugging: Debugging is the process of identifying and rectifying coding errors. It often involves using built-in tools or software Debuggers to step through the code line-by-line, track variable values, and uncover where things go awry. A systematic debugging approach is essential for problem-solving in coding.
Code Refactoring: Refactoring implies rearranging and improving the structure of existing code without changing its functionality. Refactoring techniques, such as extracting repeated code into functions or simplifying conditional expressions, are integral problem-solving tools aimed at improving code readability and efficiency.
Using Data Structures & Algorithms: Effective use of data structures ( Arrays , LinkedList, Stack, Queue, Tree, Hashtable, etc.) and algorithms (Sorting, Searching, etc.) is fundamental in coding problem-solving. The correct choice and application of such tools can have a dramatic impact on a program’s performance.
Version Control: While writing code, you often need to try out different solutions or collaborate with other team members. Using Version Control Systems , like Git, helps manage changes, track history, and merge code from different branches. This aids in solving and managing complex coding problems.
Apart from these fundamental techniques, advanced paradigms, such as Test-Driven Development (TDD), Behaviour Driven Development (BDD), etc., also exist. In TDD, the developer writes tests for a function before writing the actual function. In BDD, the behaviour of an application from the end user's perspective is the guiding force behind development. These paradigms incorporate problem-solving in their methodologies and guide the development process to create effective, robust applications.
Indeed, coding problem-solving techniques enrich a developer's toolkit and provide avenues to tackle the myriad of challenges that arise in programming. Whether it's minimising bugs, improving code efficiency, or implementing complex functionalities, these techniques are indispensable in daily coding endeavours.
In-depth study of 7 Problem-Solving Techniques
Problem-solving takes centre stage in the realm of computer science, where challenges need methodical approaches for efficient resolution. Let's delve into an in-depth exploration of seven such techniques, with each offering a unique perspective on how to tackle and solve issues effectively.
Conceptual understanding of the 7 problem-solving techniques
Within the realm of computer science, efficient problem-solving techniques can be the key to unlocking streamlined workflows, effective data handling, and improved coding management. These problem-solving methods include:
- Divide and Conquer: This technique splits larger problems into smaller, more manageable sub-problems, solves the sub-problems individually and combines the solutions to get a complete resolution. This technique is pertinent to a wide range of Algorithms in Computer Science , including sorting and searching algorithms.
- Greedy Algorithms: Greedy algorithms solve problems by making the best choice at each step, with the hope that these local optimal solutions will lead to a globally optimal solution. They are often used in scenarios where the optimal solution has a 'greedy property', such as in the famous 'travelling salesman' problem.
- Backtracking : This technique incrementally builds candidates for the solutions and abandons a candidate as soon as it determines that this candidate cannot possibly be extended to a valid solution.
- Dynamic Programming: This method solves complex problems by breaking them down into simpler sub-problems, but unlike divide and conquer, these sub-problems are not solved independently. Instead, the results of sub-problems are stored and utilised to build up solutions to larger problems.
- Brute Force : This straightforward approach tries every possible solution until it finds the best one. The simplicity of this method often makes it a practical and easy-to-implement fallback plan, although it may not be the most efficient.
- Randomised Algorithms: For certain problems, deterministic algorithms may be too slow or complex, and the solution space too large to navigate exhaustively. In such cases, randomised algorithms offer an option where random choices drive the solution process. These algorithms have proven extremely efficient in problems like QuickSort and the Monte Carlo method.
- Heuristic Methods: Heuristics are problem-solving approaches that are not always guaranteed to provide the perfect solution but will produce a good solution in a reasonable time. Various AI and machine learning techniques, such as genetic algorithms or neural networks, heavily use heuristic methods.
A Greedy Algorithm is one where, at each step, the choice that looks the best at that moment is selected with the belief that this choice will lead to an optimal global solution.
Understanding the foundations of these techniques provides a comprehensive toolset to approach a wide array of problems in computer science. It's important to remember that a technique's effectiveness largely depends on the nature of the problem.
Uses of the 7 problem-solving techniques in Computer Science
Each problem-solving method can be coupled with different facets within computer science. For example, Encryption techniques, Compression algorithms, network routing strategies, and database searches all rely on precise problem-solving methodologies. Here are just a few of the potential uses for each method:
The flexibility and variety of these problem-solving techniques enable a far-reaching applicability across the vast landscape of computer science. By understanding and mastering these techniques, you can tackle a wide array of complex problems more efficiently.
Brainstorming Problem-Solving Techniques
In the context of problem-solving techniques, brainstorming is an invaluable tool. Brainstorming offers a creative, open-ended approach well-suited for troubleshooting challenges, stimulating new ideas, and tackling issues from fresh angles.
Role of brainstorming in problem-solving techniques
Brainstorming's emphasis on exploratory thinking and collaborative problem-solving makes it an excellent tool in computer science. This interactive technique encourages you to think outside the box, ushering a wealth of ideas and potential problem-solving approaches. Here's why brainstorming plays a pivotal role in problem-solving techniques:
- Encourages Creative Thinking: Brainstorming breaks down the barriers of conventional thought, promoting imaginative solutions that may not be immediately evident. This out-of-the-box thinking can generate unique problem-solving methods for complex computer science problems.
- Fosters Collaboration: Brainstorming is fundamentally a collective effort. By combining the expertise and viewpoints of multiple individuals, it can foster innovative problem-solving approaches that would not surface in isolated thinking.
- Aids in Problem Understanding: In the process of brainstorming, not only are solutions discussed, but the problem itself is dissected from different angles. This aids in gaining a deeper understanding of the problem, essential to uncover the most effective solutions.
Consider a team of developers brainstorming to develop a feature for a software application. One developer might suggest a direct approach that, although simple, may not be the most efficient. Another team member could propose a more complex, but efficient, algorithm for the feature. A third might contribute an innovative approach that balances both performance and simplicity.
Through this collective brainstorming, the team converges on the most well-rounded approach, emphasising the critical role that brainstorming plays in problem-solving methodologies.
Applying brainstorming in problem-solving techniques
Brainstorming is not just about generating as many ideas as possible; it's also about creating an organized Framework for synthesizing and evaluating those ideas.
For effective brainstorming in problem-solving and decision-making techniques, you can follow the steps below:
- Define the Problem: Clearly understand and define the problem that needs solving. The more accurately the problem is described, the more targeted the brainstorming will be.
- Set Guidelines: Establish rules for the brainstorming session to keep it focused and productive. These might include encouraging free thinking, postponing judgment, welcoming wild ideas, building on other ideas, and setting a time limit.
- Idea Generation: Begin brainstorming, inviting everyone involved to share their ideas. The key is to promote creativity and diversity of thought. No idea is too outlandish; often, the most unconventional suggestions lead to the most innovative solutions.
- Categorise and Consolidate: Once all the ideas are documented, start to group related ideas together and consolidate overlapping ideas.
- Analyse and Evaluate: It's time to analyse each idea based on its feasibility, potential impact, and resource requirement. Ideas that might not appear effective initially can be valuable when combined with other ideas.
- Select and Implement: After thorough analysis and discussion, decide on the best solution(s) to implement, based on the resources and time available, instantly making the brainstorming session instrumental in decision making as well.
Remember: Brainstorming is not just a one-time activity. It can and should be done iteratively. Often, implementation of an idea will bring forward new challenges, requiring another round of brainstorming. The strength of brainstorming lies in its fluid nature, allowing it to adapt and iterate until the problem at hand is fully resolved.
All in all, brainstorming is a powerful problem-solving and decision-making technique in computer science. By cultivating creativity, encouraging collaboration, and fostering a deeper understanding of problems, it holds the potential to tackle complex issues effectively.
Problem Solving and Decision Making Techniques
In computer science, problem-solving and decision-making form the core techniques widely employed in managing software development, debugging, data analysis, network operations, and more. Incorporating these methodologies in a concerted, structured manner can significantly enhance the outcomes in various fields of technology.
Difference between problem-solving and decision-making techniques
While it might appear that problem-solving and decision-making are interchangeable terms, they signify distinct aspects of addressing challenges in computer science.
- Problem-solving: Within a computer science context, problem-solving involves identifying an issue within a system, application, or theory and resolving it effectively. This process often includes defining the problem, identifying root causes, generating alternative solutions, selecting a solution, and implementing it. Problem-solving often utilises techniques like debugging, algorithmic design, divide and conquer, dynamic programming, recursive thinking, heuristic methods, and more.
- Decision-making: Decision-making, on the other hand, is a process of choosing between different alternatives. It often follows problem-solving whereby, after identifying potential solutions to a problem, the best option needs to be chosen. Decision-making techniques might include tools like decision matrices, cost-benefit analyses, or simple pros-and-cons lists. In computer science, decision-making can involve choosing the right data structure, deciding which algorithm to use, or selecting a coding methodology.
For instance, problem-solving might involve identifying a bottleneck in a software's performance and brainstorming different ways to enhance the efficiency. However, decision-making comes into play when you need to choose one of the generated solutions based on various factors like resource availability, time constraints, the impact of the solution, etc. Thus, while both techniques cater to overcoming challenges, problem-solving is more focused on creating solutions, whereas decision-making prioritises choosing the most optimal one from these solutions.
Combining problem-solving and decision-making for effective results
Effective results in computer science often stem from an amalgamation of both problem-solving and decision-making techniques. Combining these approaches ensures a comprehensive solution to challenges, complete with a thorough understanding of the problem, an array of possible solutions, and a well-thought-out decision on implementing the best solution.
Consider a situation where a computer system is repeatedly encountering a fatal error. Here's how problem-solving and decision-making techniques can be combined for effective results:
- Identification: Firstly, identify the issue affecting the system. This could be established through system monitoring tools or error logs. Once the problem is identified, it sets the base for problem-solving.
- Problem-Solving: Now, brainstorm for possible solutions to rectify the error. This could involve debugging the system or reviewing the code to find potential bugs. Perhaps the issue might be a memory leak that needs addressing or a Race Condition in multi-threaded operations. These solutions emanate from problem-solving techniques.
- Decision-Making: Once a list of possible solutions is generated, use decision-making techniques to select the best course of action. You could create a pros-and-cons list for each solution or use a more formal decision matrix to evaluate effectiveness, resources required, impact on system performance, etc. Finally, implement the solution.
- Review: After implementation, monitor the system to ensure the solution is working as intended. If the problem persists, the process returns to the problem-solving stage to revisit the issue and generate new solutions.
It's important to keep in mind that real-word scenarios seldom follow a tidy linear sequence. More commonly, problem-solving and decision-making are iterative, cyclical processes that overlap and interrelate. It's a dynamic environment where a bottleneck can stimulate new decision-making criteria, or an unforeseen decisional Deadlock might call for fresh problem-solving ideas.
Combining problem-solving with decision-making offers a structured, strategic approach to tackle challenges commonly found in computer science. This conjunction of techniques provides a robust, versatile methodology to drive effective results across the diverse landscape of technology.
Problem Solving Techniques - Key takeaways
- Problem-solving techniques in Computer Science are techniques which typically use algorithmic or heuristic approaches to resolve complex issues.
- Problem-solving techniques can be systematic, analytical, or intuitive, and involve traditional programming, machine learning, or artificial intelligence methods. Applied in domains such as data analysis, software development, network troubleshooting, and cybersecurity.
- Basic problem-solving techniques comprises of methods like divide and conquer, algorithm design, heuristics, and recursive thinking, all aimed at understanding and tackling problems.
- Practical applications of basic problem-solving techniques include applications spanning across various sectors of computer science, including sorting and searching algorithms, routing protocols for networks, AI game playing, and parsing syntax trees in compilers.
- Examples of coding problem-solving techniques include Debugging which is essential in identifying and rectifying coding errors, Code Refactoring to improve the structure of existing code without changing its functionality, Using Data Structures & Algorithms to have a dramatic impact on a program’s performance, and Version Control System like Git for managing changes, tracking history and merging code from different branches.
Frequently Asked Questions about Problem Solving Techniques
--> what are some problem-solving techniques.
Some common problem solving techniques include brainstorming, the five whys technique, root cause analysis, lateral thinking, striving for simplicity, the 6 thinking hats and using flow charts or diagrams. Additionally, techniques such as SWOT analysis, Trial and Error, and Decision Trees can also be effective tools in problem-solving. Each technique is employed based on the nature and context of the problem to be solved. It's crucial to understand the problem fully before choosing a technique to apply.
--> What are the four problem-solving techniques?
The four problem solving techniques are:
1) Defining the problem clearly to understand its nature and scope
2) Generating a range of potential solutions through brainstorming or creative thinking
3) Evaluating and selecting the most feasible solutions by analysing their pros and cons
4) Implementing the chosen solution and monitoring its effectiveness.
--> How to apply problem-solving techniques?
To apply problem solving techniques, you first need to clearly identify and define the problem. Next, gather as much information as you can related to the problem. Once you have all the details, generate a range of potential solutions and evaluate each for its merits and downsides. Finally, implement the best solution and review its effectiveness, making adjustments as necessary.
--> What are the different problem solving techniques?
Different problem solving techniques include brainstorming, lateral thinking, root cause analysis, the five whys technique, mind mapping, SWOT analysis, "divide and conquer" technique and use of algorithms or heuristics. Additionally, the use of decision trees, fishbone diagrams, and PEST & STEEPLE analysis are also widely used in strategic problem solving. All these techniques help in breaking down complex problems into manageable parts and finding effective solutions. The choice of technique may vary depending on the nature and complexity of the problem.
--> How to choose problem-solving techniques?
Choosing problem-solving techniques involves understanding the nature and scope of the problem, identifying all potential methods for resolution, and then carefully evaluating each one in terms of its appropriateness, feasibility, and probable effectiveness, selecting the most promising one. Take into consideration multidisciplinary insights, and factor in resources available, time constraints, and potential risks. It can also be useful to bring in outside perspectives or utilise brainstorming techniques. The chosen method should ideally be both effective and efficient in resolving the problem at hand.
Final Problem Solving Techniques Quiz
Problem solving techniques quiz - teste dein wissen.
What are problem-solving techniques in computer science?
Problem-solving techniques in computer science refer to the methods used to find solutions to complex issues using algorithmic or heuristic approaches, which can be systematic, analytical, or intuitive. They encompass traditional programming, machine learning, or artificial intelligence methods.
Why are problem-solving techniques important in computer science?
Problem-solving techniques in computer science are important for mitigating runtime errors and system crashes, optimizing software, organizing, evaluating, and interpreting complex datasets, and identifying potential cybersecurity vulnerabilities and patching them.
What is the 'Divide and Conquer' problem-solving technique in computer science?
The 'Divide and Conquer' technique involves breaking a larger problem into smaller, more manageable parts, solving each individually, and combining their solutions to get the overall answer. This is often used in sorting algorithms like Merge Sort.
What is the purpose of the 'Heuristics' problem-solving technique?
'Heuristics' are educated guesses that can help find an acceptable solution when the problem is too complex for a direct mathematical approach, or when computational resources are limited. They are often used in AI and language translations.
What are some of the important functions of coding problem-solving techniques in computer science?
Coding problem-solving techniques aid in enhancing code efficiency, mitigating errors, facilitating code readability and maintenance, and implementing complex functionalities.
What are some examples of coding problem-solving techniques?
Examples include debugging, code refactoring, using appropriate data structures and algorithms, and implementing version control.
What is the Divide and Conquer problem-solving technique in computer science?
Divide and Conquer technique splits larger problems into smaller, manageable sub-problems, solves them individually and combines the solutions for complete resolution. It's used in algorithms like quicksort.
What are the uses of Randomised Algorithms within computer science?
Randomised algorithms are used in areas where deterministic algorithms may be too slow or complex, for instance, cryptography for key generation, primality testing, and algorithm design like 'Randomized-Quicksort'.
What are the key roles of brainstorming in problem-solving techniques?
Brainstorming encourages creative thinking, fosters collaboration and aids in problem understanding by dissecting the problem from different angles.
What are the steps to effectively apply brainstorming in problem-solving and decision-making techniques?
The steps include: defining the problem, setting guidelines, idea generation, categorising and consolidating ideas, analysing and evaluating each idea, and finally selecting and implementing the best solution(s).
What are the key differences between problem-solving and decision-making techniques in computer science?
Problem-solving involves identifying and resolving issues within a system, while decision-making is the process of choosing between different alternatives or solutions. Problem-solving uses techniques such as debugging, algorithmic design, while decision-making might involve tools like decision matrices or cost-benefit analyses.
How do problem-solving and decision-making techniques interrelate in computer science?
Problem-solving and decision-making often work in tandem in computer science. Problem-solving identifies and resolves specific issues, generating potential solutions. Decision-making then selects the most optimal solution. These techniques are typically part of an iterative, cyclical process.
What is the definition of Computational Thinking?
Computational Thinking is a multidimensional problem-solving method that leverages the power of computational engines for effective interaction with computers.
What are the four vital skills encapsulated by Computational Thinking?
The four skills are Decomposition, Pattern recognition, Abstraction, and Algorithmic thinking.
How is Computational Thinking utilized in a traffic management system?
Decomposition categorises incidents, pattern recognition identifies peak hours, abstraction reduces irrelevant details, and algorithmic thinking devises a traffic signals schedule.
What is the role of Computational Thinking in Computer Science?
Computational Thinking serves as the backbone of every computational process, solving complex problems and enabling efficient, innovative solutions across various fields.
How does Computational Thinking facilitate the design of a search engine like Google?
Decomposition handles the data, pattern recognition detects user behaviours, abstraction filters out irrelevant content, and algorithmic thinking ranks the websites based on relevance and popularity.
What are the four core techniques of Computational Thinking?
The four core techniques are Decomposition, Pattern Recognition, Abstraction, and Algorithmic Thinking.
What is Algorithmic Thinking in the context of Computational Thinking Techniques?
Algorithmic Thinking refers to the ability to design step-by-step procedures or algorithms to solve problems or accomplish tasks.
How does Quick Sort algorithm demonstrate the principle of Algorithmic Thinking?
Quick Sort uses a divide-and-conquer approach, breaking down the problem of sorting a large array into smaller tasks of sorting two smaller arrays, serving as a solution executable by a computer.
What Computational Thinking technique could be used in a detective game to break down the overall problem of solving a mystery?
The Decomposition technique could be used to break the overall problem into smaller tasks like examining clues, questioning suspects, creating timelines etc.
How do Computational Thinking techniques find application in school timetable generation?
Decomposition breaks down the entire timetable into individual classes, pattern recognition identifies optimal times for subjects or class hours, abstraction simplifies by considering only critical constraints, and algorithmic thinking creates a schedule for assigning classes.
What are the fundamental Computational Thinking skills students need to acquire?
The fundamental Computational Thinking skills include Decomposition, Pattern recognition, Abstraction, and Algorithmic thinking.
How are the Computational Thinking skills applied in a sports context?
In sports, Decomposition can be seen when breaking the goal into smaller objectives; Pattern recognition is identifying player's performances; Abstraction is focusing on the game strategy by ignoring irrelevant details; Algorithmic thinking is creating a sequence of actions for the team.
How can the game of chess help improve Computational Thinking skills?
Chess can aid Computational Thinking through Decomposition (breaking down the goal), Pattern recognition (spotting recurring strategies), Abstraction (ignoring the color of the pieces) and Algorithmic thinking (devising game strategies).
Why is Computational Thinking important across a broad range of disciplines?
Computational Thinking skills are invaluable for critical thinking and problem solving in various disciplines, enhancing one's approach to tackling complex tasks and problems.
How can designing a piece of pixel art aid in developing Computational Thinking skills?
Pixel art involves Decomposition (deciding on the art subject), Pattern recognition (identifying repeating colours), Abstraction (ignoring the whole image while working on individual pixels), and Algorithmic thinking (determining an order to fill in the pixels).
What is decomposition in computer science?
Decomposition in computer science is a process where you breakdown a complex problem or system into smaller, more manageable parts. These smaller parts can then be analysed, solved, or addressed separately to solve the larger problem.
What is the role of decomposition in problem-solving techniques in computer science?
Decomposition plays a crucial role in problem-solving. It is a fundamental concept in areas such as algorithm design and software development, assisting in designing algorithms, creating software systems, developing games, and building websites.
What are the key principles of decomposition in computer science?
The key principles of decomposition in computer science include Division, where the problem is divided into smaller parts; Abstraction, simplifying each part in isolation; Solving, addressing each individual part; Integration, integrating the solutions to form the whole system.
What is an example of decomposition in algorithm design?
A common example of decomposition in algorithm design is the divide and conquer method, where a problem is divided into smaller subproblems, each solved independently, and their solutions combined to solve the original problem.
What is decomposition in the field of computer science?
Decomposition in computer science involves breaking down large problems into smaller, manageable sub-problems, making it easier to tackle complex tasks. It is integral to fields like software engineering, web development, artificial intelligence, and database management.
How is decomposition utilized in software engineering?
Decomposition in software engineering is used when designing software systems that are broken down into independent modules based on their different functionalities. This allows for simultaneous development by different teams, faster debugging, and easier software testing.
How does decomposition apply to the field of artificial intelligence and machine learning?
In AI and Machine Learning, decomposition is used to break complex algorithms, which often train models, into simpler steps like data cleaning, feature selection, model training, and result evaluation for a more understandable process.
What are some real-world applications of decomposition in computer science?
Real-world applications of decomposition in computer science are seen in search engines, where the search task is decomposed into smaller sub-tasks. It's also used in robotics for tackling complex tasks, and in video game development to manage different aspects of game creation.
What are the advantages of decomposition in computer science?
Decomposition simplifies complex problems, promotes collaboration, enhances understanding of the system, and improves the robustness of the completed work.
How can decomposition in computer science boost problem-solving efficiency?
Decomposition breaks down large tasks into independent sub-tasks that can be assigned to different teams to work in parallel, thereby enhancing efficiency, promoting collaboration, and reducing the risk of errors.
What are the disadvantages of decomposition in computer science?
Decomposition risks oversimplification, requires increased collaboration and communication, requires careful planning, and may need advanced technology or infrastructure.
How can the challenges in decomposition computer science be overcome?
Challenges can be overcome through careful planning, strong project management practices, thorough understanding of the problem, and investing in the right tools and systems.
What is the role of decomposition in problem-solving within computer science?
Decomposition in problem-solving involves breaking down a complex problem or system into smaller, more manageable parts. This allows for easier solving of sub-problems, testing of individual components, and ultimately, more efficient and effective problem-solving.
What does the concept of composition involve in problem-solving within computer science?
Composition involves taking smaller parts or solutions of a problem and combining them to build more complex systems or to solve the original problem. It facilitates the assembly of complex systems from simpler components.
How are decomposition and composition showcased in Object-Oriented Programming (OOP)?
In OOP, a complex system is decomposed into objects (instances of a class). These objects are composed of data and methods, which are developed separately, and the system is realized by composing these objects and their interactions.
What is the key to striking a balance between composition and decomposition in problem-solving?
The balance is found by decomposing problems until the sub-problems are solvable independently but remain meaningful, and ensuring composed components fit cohesively and effectively solve the original problem. Understanding the problem and the final goal are vital.
What is Pattern Recognition in the context of computer science?
Pattern Recognition refers to the automated detection and categorisation of patterns in input data. It involves the identification of patterns and regularities in data, enabling machines to mimic the human brain's ability to recognise and distinguish patterns.
What are the two types of learning in Pattern Recognition?
The two types of learning in Pattern Recognition are Supervised Learning and Unsupervised Learning. Supervised Learning involves an algorithm learning from labeled input data, while Unsupervised Learning means an algorithm discovers patterns in input data without guidance.
How does Pattern Recognition contribute to Machine Learning and Artificial Intelligence?
In Machine Learning, algorithms are built and trained to identify patterns and make decisions based on them. In Artificial Intelligence, pattern recognition aids cognitive capabilities such as vision, audition and tactile sensibility.
What is the fundamental aim of Machine Learning?
The fundamental aim of Machine Learning is to teach machines to learn patterns from data.
What is the role of Pattern Recognition in Machine Learning?
Pattern Recognition gleans patterns or features from raw data to provide insightful data, which aids Machine Learning algorithms in devising models to predict new outputs for fresh data inputs.
How do Machine Learning and Pattern Recognition differentiate based on their focus areas?
Machine Learning focuses on improving the accuracy and predictability of the machines' performance while Pattern Recognition concentrates on finding resemblances and repetitions in the multi-source data.
What is the function of Pattern Recognition Receptors (PRRs) in the immune system?
The function of PRRs is to maintain a surveillance system that identifies the presence of pathogens by recognising unique molecular components known as PAMPs. Once PRRs identify PAMPs, they trigger immune responses.
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Problem Solving Techniques in Computer Science
Problem-solving is the process of identifying a problem and finding the best solution for it. Problem-solving is a technique that can be developed by following a well-organized approach. Every day we encounter many problems and solve them.
Every problem is different. Some problems are very difficult and are needed more attention to recognize the solution.
A problem may be solved by multiple methods. One solution may be faster, cheaper, and more reliable than others. It is important to choose a suitable worthy solution.
Different strategies, techniques, and tools are used to solve a problem. Computers are used as a tool to solve complex problems by developing computer programs.
Computer programs contain different instructions for computers. A programmer writes instructions and the computer executes these instructions to solve a problem. A person can be a good programmer if he has the skill of solving problems.
Advantages of Computer Program
Properties of algorithm, logic design, advantages of algorithm, uses of logic flowchart, problem-solving techniques..
There are three different types of problem-solving techniques.
A set of instructions given to a computer to solve a problem is called a program.
A computer works according to the given instructions in the program. Computer programs are written in programming languages. A person who develops a program is called a programmer.
The programmer develops programs to instruct the computer on how to process data into information. The programmer uses programming languages or tools to write programs.
Different advantages of computer programs are as follows:
- A computer program can solve many problems by giving instructions to the computer.
- A computer program can be used to perform a task again and again and fastly.
- A program can process a large amount of data easily.
- It can display the results in different styles.
- The processing of a program is more efficient and less time-consuming.
- Different types of programs are used in different fields to perform certain tasks.
Algorithms & Pseudo Code
An algorithm is a step-by-step procedure to solve a problem. The process of solving
problem becomes simpler and easier with help of algorithm. It is better to write an algorithm
before writing the actual computer program.
Following are some properties of an algorithm:
- The given problem should be broken down into simple and meaningful steps.
- The steps should be numbered sequentially.
- The steps should be descriptive and written in simple English.
Algorithms are written in a language that is similar to simple English called pseudocode. There is no standard to write pseudo code. It is used to specify program logic in an English-like manner that is independent of any particular programming language.
Pseudocode simplifies program development by separating it into two main parts.
In this part, the logic of the program is designed. We specify different steps required to solve the problem and the sequence of these steps.
In this part, the algorithm is converted into a program. The steps of the algorithm are
translated into instructions of any programming language.
The use of pseudo-code allows the programmer to focus on the planning of the program. After the planning is final, it can be written in any programming language.
The following algorithm inputs two numbers calculate the sum and then displays the result on the screen.
4. Total A+B
5. Display Total
The following algorithm inputs the radius from the user and calculates the area of a circle.
Hint: Area 3.14* radius* radius)
2. Input radius in r
3. area = 3.14* r* r
4. Print area
There are many advantages of an algorithm
Writing algorithm and program separately simplifies the overall task by dividing it into two simpler tasks. While writing the algorithm, we can focus on solving the problem instead of concentrating on a particular language.
An algorithm is written so that the code may be written in any language. Using an algorithm, the program could be written in Visual Basic, Java or C++, etc.
Ease of Understanding
It is not necessary to understand a particular programming language to understand an algorithm. It is written in an English-like manner.
A flowchart is a combination of two words flow and chart. A chart consists of different symbols to display information about any program. Flow indicates the direction processing that takes place in the program.
Flowchart is a graphical representation of an algorithm. It is a way of visually presenting the flow of data, operations performed on data, and the sequence of these operations.
Flowchart is similar to the layout plan of a building. A designer draws the layout plan of the building before constructing it. Similarly, a programmer prefers to design the flowchart before writing the computer program. Flowchart is designed according to the defined rule.
Flowchart is used for the following reasons
- Flowchart is used to represent an algorithm in a simple graphical manner.
- Flowchart is used to show the steps of an algorithm easily.
- Flowchart is used to understand the flow of the program.
- Flowchart is used to improve the logic for solving a problem.
- Programs can be reviewed and debugged easily.
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Lesson 27 of 33 By Hemant Deshpande
Table of Contents
Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future.
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Topics covered in this problem solving in programming article are:
- What is Problem Solving in Programming?
- Problem Solving skills in Programming
- How does it impact your career ?
- Steps involved in Problem Solving
- Steps to improve Problem Solving in programming
What is Problem Solving in Programming?
Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.
When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.
How Does It Impact Your Career?
Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified.
People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment.
The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.
Problem Solving Skills in Programming
Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.
The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.
Steps Involved in Problem Solving
Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.
Basically, they are divided into four categories:
- Analysing the problem
- Developing the algorithm
- Testing and debugging
Analysing the Problem
Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily.
Developing the Algorithm
It is required to decide a solution before writing a program. The procedure of representing the solution in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.
Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code.
Testing and Debugging
The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.
Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.
The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.
Steps to Improve Problem Solving in Programming
The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.
Making Right Decisions
When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.
Keeping Ideas on Track
Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.
Learning from Feedbacks
A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.
Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.
These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.
- Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
- These problem solving skills also help to develop more skills in a person and build a promising career.
- These skills also help to find the solutions for critical and complex problems in a perfect way.
- Learning and developing problem solving in programming helps in building a good foundation.
- Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities
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Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.
If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.
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About the author.
Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.
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How to Solve Coding Problems with a Simple Four Step Method
I had fifteen minutes left, and I knew I was going to fail.
I had spent two months studying for my first technical interview.
I thought I was prepared, but as the interview came to a close, it hit me: I had no idea how to solve coding problems.
Of all the tutorials I had taken when I was learning to code, not one of them had included an approach to solving coding problems.
I had to find a method for problem-solving—my career as a developer depended on it.
I immediately began researching methods. And I found one. In fact, what I uncovered was an invaluable strategy. It was a time-tested four-step method that was somehow under the radar in the developer ecosystem.
In this article, I’ll go over this four-step problem-solving method that you can use to start confidently solving coding problems.
Solving coding problems is not only part of the developer job interview process—it’s what a developer does all day. After all, writing code is problem-solving.
A method for solving problems
This method is from the book How to Solve It by George Pólya. It originally came out in 1945 and has sold over one million copies.
His problem-solving method has been used and taught by many programmers, from computer science professors (see Udacity’s Intro to CS course taught by professor David Evans) to modern web development teachers like Colt Steele.
Create a function that adds together two numbers and returns that value. There are four steps to the problem-solving method:
- Understand the problem.
- Devise a plan.
- Carry out the plan.
Let’s get started with step one.
Step 1: Understand the problem.
When given a coding problem in an interview, it’s tempting to rush into coding. This is hard to avoid, especially if you have a time limit.
However, try to resist this urge. Make sure you actually understand the problem before you get started with solving it.
Read through the problem. If you’re in an interview, you could read through the problem out loud if that helps you slow down.
As you read through the problem, clarify any part of it you do not understand. If you’re in an interview, you can do this by asking your interviewer questions about the problem description. If you’re on your own, think through and/or Google parts of the question you might not understand.
This first step is vital as we often don’t take the time to fully understand the problem. When you don’t fully understand the problem, you’ll have a much harder time solving it.
To help you better understand the problem, ask yourself:
What are the inputs?
What kinds of inputs will go into this problem? In this example, the inputs are the arguments that our function will take.
Just from reading the problem description so far, we know that the inputs will be numbers. But to be more specific about what the inputs will be, we can ask:
Will the inputs always be just two numbers? What should happen if our function receives as input three numbers?
Here we could ask the interviewer for clarification, or look at the problem description further.
The coding problem might have a note saying, “You should only ever expect two inputs into the function.” If so, you know how to proceed. You can get more specific, as you’ll likely realize that you need to ask more questions on what kinds of inputs you might be receiving.
Will the inputs always be numbers? What should our function do if we receive the inputs “a” and “b”? Clarify whether or not our function will always take in numbers.
Optionally, you could write down possible inputs in a code comment to get a sense of what they’ll look like:
//inputs: 2, 4
What are the outputs?
What will this function return? In this case, the output will be one number that is the result of the two number inputs. Make sure you understand what your outputs will be.
Create some examples.
Once you have a grasp of the problem and know the possible inputs and outputs, you can start working on some concrete examples.
Examples can also be used as sanity checks to test your eventual problem. Most code challenge editors that you’ll work in (whether it’s in an interview or just using a site like Codewars or HackerRank) have examples or test cases already written for you. Even so, writing out your own examples can help you cement your understanding of the problem.
Start with a simple example or two of possible inputs and outputs. Let's return to our addition function.
Let’s call our function “add.”
What’s an example input? Example input might be:
// add(2, 3)
What is the output to this? To write the example output, we can write:
// add(2, 3) ---> 5
This indicates that our function will take in an input of 2 and 3 and return 5 as its output.
Create complex examples.
By walking through more complex examples, you can take the time to look for edge cases you might need to account for.
For example, what should we do if our inputs are strings instead of numbers? What if we have as input two strings, for example, add('a', 'b')?
Your interviewer might possibly tell you to return an error message if there are any inputs that are not numbers. If so, you can add a code comment to handle this case if it helps you remember you need to do this.
Your interviewer might also tell you to assume that your inputs will always be numbers, in which case you don’t need to write any extra code to handle this particular input edge case.
If you don’t have an interviewer and you’re just solving this problem, the problem might say what happens when you enter invalid inputs.
For example, some problems will say, “If there are zero inputs, return undefined.” For cases like this, you can optionally write a comment.
// check if there are no inputs.
// If no inputs, return undefined.
For our purposes, we’ll assume that our inputs will always be numbers. But generally, it’s good to think about edge cases.
Computer science professor Evans says to write what developers call defensive code. Think about what could go wrong and how your code could defend against possible errors.
Before we move on to step 2, let’s summarize step 1, understand the problem:
-Read through the problem.
-What are the inputs?
-What are the outputs?
Create simple examples, then create more complex ones.
2. Devise a plan for solving the problem.
Next, devise a plan for how you’ll solve the problem. As you devise a plan, write it out in pseudocode.
Pseudocode is a plain language description of the steps in an algorithm. In other words, your pseudocode is your step-by-step plan for how to solve the problem.
Write out the steps you need to take to solve the problem. For a more complicated problem, you’d have more steps. For this problem, you could write:
// Create a sum variable.
Add the first input to the second input using the addition operator .
// Store value of both inputs into sum variable.
// Return as output the sum variable. Now you have your step-by-step plan to solve the problem. For more complex problems, professor Evans notes, “Consider systematically how a human solves the problem.” That is, forget about how your code might solve the problem for a moment, and think about how you would solve it as a human. This can help you see the steps more clearly.
3. Carry out the plan (Solve the problem!)
The next step in the problem-solving strategy is to solve the problem. Using your pseudocode as your guide, write out your actual code.
Professor Evans suggests focusing on a simple, mechanical solution. The easier and simpler your solution is, the more likely you can program it correctly.
Taking our pseudocode, we could now write this:
Professor Evans adds, remember not to prematurely optimize. That is, you might be tempted to start saying, “Wait, I’m doing this and it’s going to be inefficient code!”
First, just get out your simple, mechanical solution.
What if you can’t solve the entire problem? What if there's a part of it you still don't know how to solve?
Colt Steele gives great advice here: If you can’t solve part of the problem, ignore that hard part that’s tripping you up. Instead, focus on everything else that you can start writing.
Temporarily ignore that difficult part of the problem you don’t quite understand and write out the other parts. Once this is done, come back to the harder part.
This allows you to get at least some of the problem finished. And often, you’ll realize how to tackle that harder part of the problem once you come back to it.
Step 4: Look back over what you've done.
Once your solution is working, take the time to reflect on it and figure out how to make improvements. This might be the time you refactor your solution into a more efficient one.
As you look at your work, here are some questions Colt Steele suggests you ask yourself to figure out how you can improve your solution:
- Can you derive the result differently? What other approaches are there that are viable?
- Can you understand it at a glance? Does it make sense?
- Can you use the result or method for some other problem?
- Can you improve the performance of your solution?
- Can you think of other ways to refactor?
- How have other people solved this problem?
One way we might refactor our problem to make our code more concise: removing our variable and using an implicit return:
With step 4, your problem might never feel finished. Even great developers still write code that they later look at and want to change. These are guiding questions that can help you.
If you still have time in an interview, you can go through this step and make your solution better. If you are coding on your own, take the time to go over these steps.
When I’m practicing coding on my own, I almost always look at the solutions out there that are more elegant or effective than what I’ve come up with.
In this post, we’ve gone over the four-step problem-solving strategy for solving coding problems.
Let's review them here:
- Step 1: understand the problem.
- Step 2: create a step-by-step plan for how you’ll solve it .
- Step 3: carry out the plan and write the actual code.
- Step 4: look back and possibly refactor your solution if it could be better.
Practicing this problem-solving method has immensely helped me in my technical interviews and in my job as a developer. If you don't feel confident when it comes to solving coding problems, just remember that problem-solving is a skill that anyone can get better at with time and practice.
If you enjoyed this post, join my coding club , where we tackle coding challenges together every Sunday and support each other as we learn new technologies.
If you have feedback or questions on this post, feel free to tweet me @madisonkanna ..
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How to Solve Programming Problems
Written By John Sonmez
Right before the holidays, I said that you had better learn how to solve programming problems .
This time I am going to try and give you some good tools to enable you to get good at solving programming problems. (Really algorithm type problems specifically.)
When most programmers are given a programming problem in an interview, they make several key mistakes. The most severe of those is the improper allocation of time.
If you have heard the saying “measure twice and cut once,” then you are probably familiar with the idea of spending upfront time to make sure something is done right, rather than diving right in.
The most common mistake I see when conducting interviews or watching someone try to solve a programming problem is they try to start writing code as soon as possible.
You must resist this urge.
You really want to make sure you take enough time to understand the problem completely before attempting to solve it.
Another big mistake is trying to over solve the solution on the first iteration. Keep it simple, don’t try to get fancy.
A simple set of steps
I am going to give you a simple set of steps to follow which you can use for any algorithm type programming problem.
- Read the problem completely twice.
- Solve the problem manually with 3 sets of sample data.
- Optimize the manual steps.
- Write the manual steps as comments or pseudo-code.
- Replace the comments or pseudo-code with real code.
- Optimize the real code.
As much as 70% of our time should be spent in steps 1-3.
Let’s look at each step.
Read the problem completely twice
This is the single most important step. You may even want to read the problem 3 or 4 times.
You want to make sure you completely understand the problem. A good test of this is whether or not you can explain the problem to someone else.
I cannot over-emphasize how important this step is!
If you don’t understand the problem, you cannot solve it. Do not worry about wasting time here, because the better you understand the problem, the easier it will be to solve it.
If you are given any examples along with the problem, make sure you have worked through the examples and understand why the answers are correct for each one.
Solve the problem manually
“Nothing can be automated that cannot be done manually!”
Programming is automation plain and simple. You may have the ability to skip the manual steps and jump directly to code, but there is a manual process which is the foundation of any code you write.
It is very important to solve the problem manually first, so that you know what you are going to automate, otherwise you are just slinging code around. Which while can be fun, will make you look like an idiot in a programming interview and will probably cause you to sweat profusely.
I recommend that you solve the problem with at least three different inputs to make sure you really understand your solution and that it will work for more than one case.
I often use a Mathematical Induction approach if possible. Using this approach I might try and solve for 1 first, then for 2, then for n.
Also don’t forget to look for corner cases and edge cases and do any examples for those kind of cases you can think of.
It’s very important that when you solve a problem manually, you recognize what your brain is actually doing to solve the problem. You may need to write out all the things you are normally storing in your head. You want to be aware of each step, it is easy to gloss over them.
Let’s look at a very basic example, reversing a string.
If I give you a string “Zebra”, and ask you to reverse it, most people will do the following manual steps.
- Write “Zebra” down.
- Start a new word, and put “a” as the first letter. (Why –> because it is the last letter, we want to start here)
- Put “r” down as the 2nd letter. (Why –> because it is the next letter backwards from the last letter we copied)
- Put “b” down as the 3rd letter. (Why –> same as above)
Notice how I write down each little step and why.
Optimize the manual solution
People often don’t realize how valuable this step is. It is much easier to rearrange and reconstruct and idea or algorithm in your head than it is in code.
It’s well worth the effort to try and optimize the actual solution or simplify it when it is still in the most easily malleable state.
What you want to do here is figure out if there is another way you can solve the problem easier, or if there are some steps you can cut our or simplify.
Let’s look at our string reversal example and see if we can simplify the steps.
We should be able to immediately recognize that we can use a loop here to reduce the manual steps. Our duplicate why’s for most of our steps tell us that we are doing the same thing over and over for each step, just with different data.
- Start at the last letter in the word and create a new empty word.
- Append the current letter to the new word
- If there is a previous letter, make the previous letter the current letter and start back at 3.
Look how close we are getting to code at this point. You should be tempted to actually write the code for this. That is good, it tells you that you have solved and simplified the problem well. Writing code should now become very easy.
Write pseudo-code or comments
Many times you can skip this step if you have a really good handle on the problem or your previous steps already created a detailed enough description of the solution that coding it is already a 1 to 1 translation.
If you are a beginner or struggle with these kinds of problems, I would go ahead and take the time to do this step anyway though.
What we want to do here is capture all the steps we created and now either put them into our editor as comments or write them as psuedo-code that we can translate to real code.
By doing this, we can know exactly what the structure of the code we are going to write is going to look like which makes the job of filling in the actual code later trivial.
Let’s look at some psudeo-code for reversing a string.
// NewWord = “” // Loop backwards through word to reverse // NewWord += CurrentLetter // Return NewWord
Pretty simple, but the key thing we have done here is outlined the structure of the code we will write to solve the problem.
Replace comments with real code
This step should be extremely easy at this point. If you have done all the other steps, this step involves no problem solving at all.
All we do here is take each comment and convert it into a real line of code.
Taking the string reversal, we might end up with something like this.
1 for 1 translation of the comments we created above for real code.
If you struggle here, there are usually two possible reasons:
- You didn’t break down the problem into small enough steps
- You don’t know your programming language well enough to do the conversion
If you didn’t break the problem down enough, try going back to the second step and being as meticulous as possible. Write out each and every single step. I know it is a pain, but do it, believe me it will be worth the effort.
If you don’t know your programming language well enough to do the translation, you may need to brush up here on some basic constructs. Any language you expect to be able to solve algorithm type problems in, you should know how to do the following things:
- Create a list
- Sort a list or array
- Create a map or dictionary
- Loop through a list, or dictionary
- Parse strings
- Convert from string to int, int to string, etc
If you don’t know how to do all of these things. Stop what you are doing now and learn them. It’s not a very long list, and the benefits will be profound.
Optimize the real code
Sometimes this step isn’t necessary, but it’s worth taking a look at your code and figuring out if you can cut out a few lines or do something simpler.
This is also a good place to make sure all your variables are named with long meaningful names. I cannot stress enough how important having good names for your variables and methods is for helping the person evaluating your code to understand what you were trying to do. This is especially important when you make a mistake!
I won’t give an optimization for our trivial example of a string reversal, but a word of advice here is not to get too tricky. Just try to mainly simplify your code and get rid of duplication.
A few final tips
If you follow this template for solving algorithm type problem, you should do very well in programming interviews, but the key to doing so is having confidence in this process.
The only way you are going to have confidence in this process is to practice it. It takes a good amount of faith to believe that spending 70% of your 30 minutes to solve a problem just thinking about the problem and not writing any code is the right approach, so make sure you have that faith when you need it.
I’ve talked about using TopCoder to become a better programmer before, and I still recommend it. Codility.com is another great site I have recently been introduced to.
There is one important step I did not include in the outline above, because I didn’t want to make the process any more complicated than it needed to be.
Many times you will find that a problem itself involves multiple large steps or is very complicated. In those instances, you will want to try and find a way to cut the problem directly in half and then following the process above for each half.
This method of tackling a problem is called “divide and conquer” and is quite effective. A good way to know where to break a problem in half is to think about what part of the problem if already given to you would make solving the rest easy.
The programming interview is merely one battle in a larger war: marketing yourself. For the full lowdown, take a look at my course: How to Market Yourself as a Software Developer .
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Easy problem solving techniques for programmers
Serial Ai Publisher January 5, 2023 4IR , Code Standards , CRM , Software Development
Overview of problem solving:
Effective problem-solving is a crucial skill for programmers to have, and first principles thinking is a powerful approach that can help you come up with innovative solutions. To use this approach, start by defining the problem clearly and breaking it down into smaller, more manageable chunks. Identify the data you need, and then write pseudocode to outline the steps needed to solve the problem. Next, translate the pseudocode into a programming language, test the code to ensure it is working as intended, and debug any errors that may arise. Finally, consider refactoring the code to make it more efficient or readable, and document the code with comments to explain its purpose and how it works. By following these steps and using a range of problem-solving tools and techniques, you can effectively tackle any programming problem you encounter.
- Define the problem: Clearly identify and articulate the problem that needs to be solved.
- Generate possible solutions: Come up with a list of potential solutions to the problem.
- Evaluate and select the best solution: Consider the pros and cons of each solution, and choose the one that is most likely to be effective and feasible.
- Implement the solution: Put the chosen solution into action, and monitor its progress to ensure it is working as intended.
- Reflect and learn: Reflect on the problem-solving process, and consider what can be learned from the experience to apply to future problem-solving efforts.
How to relate this to programming:
- Define the problem: Clearly articulate the problem that needs to be solved.
- Break the problem down: Divide the problem into smaller, more manageable chunks.
- Identify the data: Determine what data is needed to solve the problem, and where it can be found.
- Write pseudocode: Outline the steps needed to solve the problem using plain English (or a similar language), without worrying about the specific syntax of a programming language.
- Write the code: Translate the pseudocode into a programming language.
- Test the code: Run the code to see if it produces the expected results.
- Debug: If the code does not work as expected, use debugging tools and techniques to identify and fix any errors.
- Refactor the code: Once the code is working as intended, review it to see if it can be made more efficient or more readable.
- Document the code: Include comments in the code to explain its purpose and how it works.
Have a look at first principle thinking
First principles thinking is a way of thinking that involves breaking a problem down into its fundamental principles or building blocks, and then using these principles to come up with new and innovative solutions. This approach is often associated with analytical and logical thinking, and can be applied to a wide range of problem types.
Here are some steps to follow when using first principles thinking to solve a problem:
- Break the problem down: Identify the fundamental principles or building blocks that make up the problem.
- Determine the relationship between the principles: Understand how the principles are connected and how they interact with each other.
- Identify any assumptions: Examine any assumptions that are being made about the problem and consider whether they are valid.
- Generate new solutions: Use the fundamental principles and the relationships between them to come up with new and innovative solutions to the problem.
- Reflect and learn: Reflect on the problem-solving process, and consider what can be learned from the experience to apply to future problem-solving efforts
How do you apply this to problems in the coding world?
Example: “I need to create a program that calculates the average score of a list of test scores.”
Example: “I will first create a function that calculates the sum of a list of numbers, and then I will use that function to calculate the average of the list.”
Example: “I will need a list of test scores, which I will get from the user as input.”
Example: “1. Create a function called ‘sum_list’ that takes in a list of numbers as an argument. 2. Initialize a variable called ‘total’ to 0. 3. Iterate over the list of numbers, adding each number to ‘total’. 4. Return ‘total’. 5. Create a function called ‘average’ that takes in a list of numbers as an argument. 6. Call the ‘sum_list’ function to get the sum of the list. 7. Divide the sum by the length of the list. 8. Return the result.”
Example: (in Python)
Example: “I will test the ‘average’ function by calling it with a list of test scores and printing the result to the console.”
Example: “I will add print statements to the ‘sum_list’ and ‘average’ functions to see where the error is occurring, and then I will use the debugger to examine the values of variables and trace the execution of the code.”
Example: “I will refactor the ‘average’ function to use a built-in Python function to sum the list of numbers, since this is more efficient than the loop I used in the ‘sum_list’ function.”