Having code that works or implementing a solution is not good enough. Some problems require optimizing for memory and time and also meeting given constraints. Efficiency matters, and the scalability of the code is crucial. It must also perform well for its given implementation.

Your performance might depend on others’ performance as well. If your solution works in 5 seconds and another’s works in 0.5 seconds, which is 10x faster, then obviously your solution is not the best or even close to the best. Sometimes reusing code or a one-size-fits-all solution might not work.

That’s where algorithms and data structures come in. Studying algorithms helps you identify bottlenecks and provide more efficient solutions.

As problem solvers, having code that simply works is not enough. It’s often necessary to optimize for memory and time while meeting given constraints. Efficiency and the ability to scale across systems matter. In fact, our performance might depend on others’ performance too.

Studying algorithms and data structures helps to identify areas in our code that might be causing slowdowns and provides us with more efficient solutions. Continuous improvement is essential for any problem solver, regardless of your background.

However, it’s worth noting that having some basic programming knowledge (such as Python, C++, Java, etc.) can be helpful when studying algorithms and data structures.

Here are possible resources to help you in your journey to write better and more efficient code:

Books

• “Introduction to Algorithms” by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein
• “Data Structures and Algorithms in Python” by Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser
• “Algorithms” by Robert Sedgewick and Kevin Wayne

Videos

• “Introduction to Algorithms” by MIT OpenCourseWare on YouTube: YouTube Playlist
• “Data Structures and Algorithms” by GeeksforGeeks on YouTube: GeeksforGeeks YouTube
• “Algorithms and Data Structures” by Abdul Bari on YouTube

Courses

• “Data Structures and Algorithms” by UC San Diego on Coursera
• “Algorithms and Data Structures” by edX
• “Algorithms, Part I” by Princeton University on Coursera
• “Data Structures and Algorithms” by FreeCodeCamp on YouTube: FreeCodeCamp YouTube

Interactive Platforms

• HackerRank: Offers coding challenges and exercises in data structures and algorithms
• LeetCode: Offers coding challenges and exercises in data structures and algorithms
• CodeSignal: Offers coding challenges and exercises in data structures and algorithms
• Project Euler: Offers mathematical and algorithmic problems to solve

Note: While these resources are free, some may offer paid upgrades for additional features and content. Also, you do not need to learn each of these resources; please pick your preferred learning method. Keep in mind that algorithms are not all that software engineering involves.

Let’s continuously improve our skills and write better code!

#coding #programming #algorithms #datastructures #efficiency #optimization #learning

Credits: Picture from freeCodeCamp