Was ist der beste Weg, um festzustellen, ob eine Datenstruktur oder ein Algorithmus für Ihr Problem geeignet ist?

In my experience, understanding the problem might be the hardest thing to do for a beginner. Lack of intuition leads to not knowing the right questions to ask and therefore missing the important constraints of a problem. The best way to solve for that is to go through many problems first (the hard way) or second (the observer way) hands. Once you learn about various problems, existing solutions in the space (e.g. existing data structures, how they differ in performance, their assumptions... etc.), you will be able to ask the right questions to understand the problem at hand.

I believe in breaking down the problem into its parts—like figuring out what information goes in, what results I want, and any rules I need to follow. It's like having a roadmap to guide me in choosing the right tools for the job. I pay attention to what the solution needs to do and how it should perform. I also think about different situations that might happen and how the solution should handle them. It's a bit like thinking about the possible challenges beforehand. Every problem has its unique needs, and I make sure to know them. This helps me pick the best ways to organize and process information. And in the end, I think about what's most important for the solution and what I might need to compromise on.

One thing that I have found helpful, is to understand two aspect of the problem: 1. What is the most important metric for the problem. a)If say accuracy is more important, one should spend more time developing the algorithm, while assuming you have the data in any relevant format. Then one can analyze the type of data association or relationship would be most suitable for the problem. For example if your code requires heavy recursion, you can handle the return values and the current value, without thinking much about the data type. b) Performance: In this case you would need to understand how the system actually handles the data internally and then say use dictionaries or hashes and use memoization for problem with deep re-computation.

To be frank the best way to determine if this ds/Algo fit or not is continuous learning without miss a single day and after that one day came that's u eventually read a problem and find out very fast that's this is problem of binary search, graph etc.

Determining whether a data structure or algorithm is suitable for a specific problem involves defining problem specifications, examining complexity of algorithms considering factors like input characteristics, scalability assessing how easy it is to implement, taking into account existing structures, and weighing various trade-offs. We consider constraints, community support, documentation, and conducting experiments using datasets. This process involves feedback, iterative optimization based on real-world usage and potential for use. Prototype, validation, reviewing existing implementations all contribute to making informed decisions that align with requirements of problem, at-hand performance expectations, broader technological landscape.

1. Identify the primary operations (insertion, deletion, search, etc.) your problem requires and how frequently each will be performed. Different data structures excel in different operations. 2. Consider how the data structure or algorithm scales with increasing data volume. What works for a small dataset may not be efficient for a large one. 3. A simpler, more straightforward solution is often preferable, as it is easier to maintain and understand. Avoid overcomplicating solutions with complex structures or algorithms if a simpler one suffices.

Try out different data structures and see how they perform with your problem. Be open to refining your approach based on experimentation. Understand any limitations or constraints such as time complexity, space complexity, or specific requirements for your solution. Consider the following points: 1. Access data fast - Use array or hash table 2. How things are connected - Use trees data structure 3. Adding and removing things a lot - Use dynamic data structures like linked lists or balanced trees 4. Saving memory - Think about how much extra space your data structure needs. Linked lists use more space than arrays because they have extra pointers. Consider this if memory is a big deal.

A right data structure is the most crucial but abstract thing to get a final solution which satisfies time & space complexity constraints E.g - You have your algorithm which has to read each element in your input so O(N) time complexity, but inside your core loop you have the business logic to solve your core problem, where you perform a operation of a lookup of an element in a data structure, if you use a data structure which has O(N) time complexity of lookup like a linked list or array, your overall algorithm becomes O(N^2) time impact your product and make it very slow for large input data sets. Instead if you use hash set which is O(1) , your solution stays O(N) causing huge difference between success of failure of your product.

Now that you understand the problem, it's time to choose a data structure. When choosing a data structure, here are a few things to look for: -Does it support the operations your algorithm will be performing. -Is it efficient? Can data be read and inserted as fast as possible. Let me give you an example; Let's say we have a unsorted array of integers and we want them sorted in an efficient way. What data structure would help with this? Well, a binary heap - it could be a min or max. Why a heap? -It has fast read time. -It has fast insertion time. -Etc If your algorithm is read heavy, then choose a data structure that has fast read times, the vice versa is true. Fast read DSs - arrays - hash tables Fast Insertion DSs: -heaps -BSTs

Based on your problem's nature, select a data structure that complements the type and structure of your data. Consider factors such as how frequently data needs to be accessed, inserted, or deleted.

Selecting the appropriate algorithm involves a systematic approach. Start by thoroughly understanding the problem at hand and its specific requirements. Consider the size and complexity of your dataset to guide your algorithm choice. Experiment with various algorithms relevant to your problem domain, evaluating their performance using key metrics. Factor in computational efficiency, and conduct empirical testing to observe how each algorithm performs on your data. Ultimately, align your choice with the overarching objectives and constraints of your project for optimal results.

Define Problem Type: Classify as regression, classification, or clustering. Analyze Data: Assess size, type, and characteristics. Resource & Time: Consider computational constraints. Accuracy Requirements: Define required precision level. Familiarity: Prioritize algorithms you know. Experiment & Compare: Test on a data subset, compare results. Select Algorithm: Choose based on performance and constraints. Iterate if Needed: Adjust or try other algorithms if necessary. Adaptability: Confirm adaptability to changing needs.

There is no set rules to choose an algorithm for a real world problem. So the best way is to break the problem into multiple chunks which can be solved independently on their own as a singular module. Then best-fit algorithm can be chosen. But this does not guarantee the best solution. Since all these multiple chunks need to be combined to form the complete solution, at times individual solutions might seem efficient themselves but not as a whole. The best fit for the problem is one which give best solution as a whole.

When choosing an algorithm, here are a couple of factors to consider: 1.What is the time complexity of the algorithm. 2.Will the algorithm require extra space. 3.Prefer simple algorithms over complex ones. 4.How does the algorithm handle edge cases. 5.Etc. Different algorithms have their upsides and downsides. Some might be time efficient and yet space inefficient. And most data structures have algorithms that operate on them - so always first look into those. For example, DFS and BFS for Binary trees and Graphs. So here are some questions to ask yourself: 1.Does the algorithm need to be both time and space efficient? 2.Is there a simpler algorithm. (You don't want to be the guy that uses complex algos - maintaining them usually sucks)

Identify potential algorithms suitable for your problem. Assess their time and space complexity to ensure they meet your performance requirements. Consider the trade-offs between different algorithms.

Implement a prototype using the chosen data structure and algorithm. Benchmark its performance against various metrics. Understand the implications of time and space complexity on the overall efficiency.

Algorithm analysis in data structures involves a systematic evaluation of how algorithms behave concerning time complexity, space complexity, and practical efficiency when manipulating different types of data. By delving into this realm, computer scientists and developers gain insights into optimizing algorithmic performance, making informed decisions about algorithm selection, and crafting solutions tailored to specific data structure requirements.

1. Compare your chosen data structure and algorithm with alternatives. Use methods like time and space complexity analysis, testing, and benchmarking. Get feedback from peers or community forums. 2. Optimize and enhance your solution by applying principles like modularization, abstraction, and coding standards. Consider feedback from code review and continually refine your solution. 3. Analyze data characteristics, consider time and space complexity, assess scalability needs, and look at real-world examples in your industry. Understand trade-offs and be adaptable to changing needs.

Iterate on your implementation. If the initial solution falls short, refactor the code to enhance its efficiency. Consider alternative algorithms or data structures that might better suit your problem.

La refactorización del algoritmo es importante, ya que contribuye a mejorar su eficiencia, además también facilita su mantenimiento.

You always need code refactoring and improvements, always try to refactor & improve existing/old code. This is something you will get from experience only.

Refactoring reduces complexity and can also help software developers find hidden bugs or vulnerabilities in their code. By learning about how algorithms are used in different fields such as artificial intelligence, machine learning, data science, cryptography, bioinformatics or quantum computing and how algorithms are used to solve real-world problems such as social network analysis, search engine optimization, compression, encryption or recommendation systems can gain good insight.

Practicing different sets of questions around various data structures is beneficial as it helps in improving our problem-solving skills. By consistently working on diverse problems, we can enhance our ability to identify the most suitable data structure for a given scenario.

I think Learning and practicing are indeed crucial for determining the suitability of a data structure or algorithm for a problem. By understanding their principles and experimenting with them in various scenarios, you gain insights into their strengths, weaknesses, and applicability to different problems. - Understand the problem thoroughly. - Research relevant data structures and algorithms. - Analyze requirements and match with data structures and algorithms. - Experiment with different solutions. - Evaluate performance in terms of time and space complexity. - Refine and optimize implementations. - Practice solving similar problems with different approaches. - Stay updated with new developments and techniques.

Continuously enhance your knowledge of algorithms and data structures. Practice solving different types of problems to develop a deep understanding of their applications and limitations.

To become great at data structures and algorithms, learn about them and practice regularly. Use books, online courses, videos, blogs, and podcasts. Stay updated. Practice on sites like LeetCode, HackerRank, CodeChef, Codewars, and GeeksforGeeks to get better at solving different problems.

Forward Bias --> Overfitting --> Negative Performance Out-of-Sample. This is your biggest enemy, Forward Bias. No matter how you slice your dataset to test and training, you have to trade carefully when moving to prod. Learn quickly about your biases from the real-time performance, and repeat the model calibration lifecycle.