Here's how you can appreciate feedback from your colleagues in Algorithms.
Navigating the complex world of algorithms can be a daunting task, and receiving feedback from your peers is an invaluable part of the learning process. Understanding and appreciating this feedback not only enhances your own skills but also fosters a collaborative environment. Whether you're a seasoned developer or just starting out, embracing the insights of others can lead to more efficient and innovative solutions. Remember, every piece of advice, whether it's about optimizing a sorting algorithm or refining a search function, is a step toward mastering the art of algorithms.
When you receive feedback on your algorithmic work, approach it with an open mind. This means setting aside your ego and considering the possibility that there's room for improvement. Recognize that your colleagues have different experiences and perspectives that can provide valuable insights into your work. By being receptive, you create an atmosphere of mutual respect and continuous learning. Remember, algorithms are about finding the best solution, not just your solution.
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Saber dar e receber feedbacks sobre códigos é extremamente positivo em um ambiente de trabalho colaborativo, trazer perspectivas novas e ideias colaborativas para a melhoria do código de alguém faz de você uma pessoa preparada para a senioridade, e lógico saber receber e saber abordar de maneira colaborativa e produtiva as críticas te faz mais sênior ainda. Revisão de código é essencial em um ambiente que deseja ter uma boa experiência de equipe com boas metodologias agéis.
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Embracing a growth mindset involves being open to learning and continuously improving your code. Adapt the practice of code pairing, where you collaborate with others to review and enhance code together. When mistakes occur, view them as learning opportunities rather than failures. Seek continuous feedback from code reviewers to refine your skills. Additionally, don’t hesitate to contribute your ideas about effective algorithm usage—even if they’re not always correct—because sharing insights fosters collective growth
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There are multiple approaches to problem-solving, and adopting this mindset encourages openness to receiving feedback. The feedback process can be challenging for both parties—the receiver and the giver. It requires the giver to have a growth mindset to provide constructive feedback. Therefore, it's important to show appreciation whenever you receive feedback.
If feedback on your algorithm isn't clear, don't hesitate to ask questions. It's important to fully understand the suggestions and the reasoning behind them to make informed decisions about your work. This dialogue can also help clarify any misunderstandings and provide a deeper insight into algorithmic concepts such as time complexity or space optimization. Effective communication is key to making the most of the feedback you receive.
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Asking questions is an important part of setting a positive impression. Most often the questions are incomplete set by the interviewer to test how far the interviewee can think about it. Also try to think about the edge cases that may occur and seek clarity on it. This not only helps to set your first impression but also helps you to clearly think about the approach.
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Ein Review ist keine Einbahnstrasse. Häufig kann der Reviewprozess dazu genutzt werden um Unklarheiten zu erläutern und auch Kritik/Verbesserungsvorschläge können disktutiert werden. Im Review ist es wichtig Fragen und Kommentare so konkret und pregnant wie möglich zu stellen. Eine Rückfrage etwas zu erläutern kann hilfreicher sein als ein Kritikpunkt der auf unwissenheit / fehlendem Verständnis beruht.
Once you have a clear understanding of the feedback, take the time to critically analyze it. This doesn't mean being critical of the person providing the feedback but rather evaluating the merits of their suggestions. Consider how their advice might improve your algorithm's performance or readability. Does it make your code more efficient or maintainable? Critical analysis will help you decide which pieces of feedback to implement and which to set aside.
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Man sollte klar zwischen dem Review und dem Reviewer trennen. Ein Review ist ein fachlicher Hinweis, keine persönliche Kritik. Vorschläge aus dem Review sollten unvoreingenommen und ohne Ego bewertet werden. Anstatt Vorschläge kommentarlos zu ignorieren sollte es immer begründet/erläutert werden warum man von der Vorgeschlagenen Lösung abweicht. Eine Diskussion vor der Implementierung kann wertvolle Zeit sparen und Missverständnisse vermeiden
After careful consideration, decide which feedback to incorporate into your algorithm. Implementing changes wisely means not only making your code better but also learning from the process. You might discover new methods or refine existing ones, such as improving a recursive function or simplifying a complex data structure. Each change is an opportunity to grow your understanding of algorithmic principles and best practices.
Feedback is most beneficial when you reflect on it continuously, not just when you receive it. Consider keeping a log of the feedback you've received and the changes you've made. This can help you track your progress over time and identify patterns in the types of feedback you're getting. Reflection will also help you internalize the lessons learned, making you more adept at both giving and receiving feedback in the future.
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The thing about algorithms is that the first one that you think of is usually inefficient. For example sorting algorithms can be easily implemented in O(n^2). But it takes a bit of work to implement one in O(nlogn). But again there are several algorithms which are efficient. But some are more suited to datasets that are likely to be presented by the user. So coming up with the most efficient algorithm requires that you are reflecting on your approach and thinking of constantly improving your solution.
Lastly, always express gratitude for the feedback given by your colleagues. A simple thank you acknowledges their effort and encourages a culture of sharing knowledge. It also reinforces the value of constructive criticism within your team. Showing appreciation can strengthen professional relationships and lead to more open exchanges of ideas, which is essential in the ever-evolving field of algorithms.
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Discuss Tradeoffs: Appreciating feedback from colleagues on algorithms involves understanding the trade-offs between different approaches. When evaluating feedback, consider that it might offer a better or complementary solution. Discuss code extendibility, considering how easily the new approach can adapt to future changes. This might favor a more modular and maintainable codebase. Assess performance by comparing time and space efficiency, as the alternative could scale better with larger datasets. Examine complexity by analyzing the algorithmic complexity (Big O notation), balancing simplicity and efficiency. Open discussions on these factors can lead to a more robust and efficient solution, leveraging feedback for better algorithms.
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