You're starting an internship and you want to impress your mentor and colleagues with your algorithm skills. But how do you learn about algorithms in a fast and effective way? Here are some tips to help you master the fundamentals and apply them to real-world problems.
Before you dive into complex and advanced algorithms, make sure you have a solid grasp of the basic concepts and techniques. These include data structures, sorting, searching, recursion, complexity analysis, and common algorithm design patterns. You can use online resources, books, or courses to refresh your memory and practice some exercises. A good resource to start with is the Algorithm Design Manual by Steven Skiena.
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To start, an algorithm manipulates data stored in a given data structure. Which then implies that you can learn algorithms before you master data structures. The two are like lego pieces. So start with data structures. You can break these into two categories: 1. Linear data structures - Arrays - Linked lists - Stacks & Queues 2. Non-linear data structures - Trees - Heaps - Graphs - Hash tables Once you have understood those, you can now proceed to algorithms. You can start by mastering these categories of algorithms: - Sorting algorithms - Tree and Graph algorithms - Recursive algorithms - Searching algorithms - Etc.. Note: You can't truly master algorithms if you have not put in the time to learn data structures. Happy learning :)
The best way to learn about algorithms is to implement them yourself. You can use any programming language you prefer, but try to choose one that is relevant to your internship domain and project. For example, if you are working on web development, you might want to use JavaScript or Python. If you are working on data science, you might want to use R or MATLAB. You can find many online platforms and websites that offer algorithm challenges and problems for different levels and topics. Some examples are HackerRank, LeetCode, and Codeforces.
Another way to learn about algorithms is to compare and contrast different solutions and approaches to the same problem. This will help you understand the trade-offs and advantages of each algorithm, as well as the context and assumptions that affect their performance and applicability. You can use online tools, such as Big-O Cheat Sheet or Visualgo, to visualize and compare the complexity and behavior of different algorithms. You can also read articles, blogs, or papers that explain and compare different algorithms for specific problems or domains.
One of the benefits of doing an internship is that you can learn from experienced and knowledgeable professionals who can give you feedback and guidance on your algorithm skills. Don't be afraid to ask for help, clarification, or advice from your mentor or colleagues when you encounter a problem or a challenge that requires an algorithmic solution. You can also ask them to review your code, suggest improvements, or recommend resources or best practices. This will help you improve your skills, avoid mistakes, and gain confidence.
Finally, the ultimate way to learn about algorithms is to apply them to real-world scenarios that are relevant to your internship project and domain. This will help you see the value and impact of algorithms in solving practical and meaningful problems, as well as the challenges and limitations that arise in real-world situations. You can look for opportunities to use algorithms in your internship tasks, such as data analysis, optimization, simulation, or recommendation. You can also propose or join projects that involve algorithmic innovation or research.
Theoretical concepts will get you started but practical application will get you to mastery. Here is the thing, if you learn about sorting algorithms, try to build a real world project around sorting. It could involve sorting through a bunch of records or even a visualization of any sorting algorithm. Maybe you're working with graphs. You could create a fictious map with a bunch of nodes as cities and use dijkstra's algorithm to find the shortest path between any two nodes. Or you're trying to optimize a certain algorithm, if the problem has a optimal sub-structure, then you can use dynamic programming (whether top-down OR bottom-up) By now you get the point. It is all about matching a given problem with the appropriate algorithm.