Here's how you can navigate the challenges of transitioning into algorithm careers.

How you can build skills in algorithms? The first thing you must have thought would be completing a straight 500 questions on Leetcode. But does it help? To answer this question, I researched on the internet that, is doing 500 questions on Leetcode is necessary, and obviously, the answer is no. You don't need to grind that hard just to clear interviews and build logic. What you need is some set of questions that help you "think". If you are thinking how you can achieve it. You can use let's say neetcode chartbook for Leetcode questions. If you're stuck anywhere, they have solutions to the questions as well.

Transitioning into algorithm careers is not difficult if you get proper guidance. For building a career in this space you have make your basics strong. I will not suggest you to just go and solve thousands of problems in some online platforms but what really important is to solve problems regularly. Maintaining consistency is very important to build a career in this domain. Try to solve some problems regularly on you own just allot 1 hr daily for this. First start solving problem on your own later take help from online platforms/tutorials if you can't but make sure you have analysed the problem. This will increase your problem solving ability as well.

A crucial part of learning algorithms is understanding the theory of how the algorithm works. Let's say you feel bored while learning algorithms, one of the best ways is to know their real-life applications. Let's say if you are studying let's say Doubly Linked-List you must know that it's used in Spotify where one node connects to another through the next function and through that we can iterate left and right in the playlist easily. Another thing is the browser's back button, it uses stack which is implemented by FIFO i.e. first in first out theory.

Doing practical is very good and interesting as well while learning algorithms., however understanding the basic theories is also very important. If you only do practical things and don't learn the underlying theories you will never be able to solve complex problems. So try to clear your basics first and build a good base then only try creating some practical things. Here is one secret tip :) Always take a pen and paper while learning algorithms or doing code. This will help you relating with the problems/theories. I will also suggest online videos for learning theories.

A common pitfall is treating AI packages as black boxes without truly understanding their inner workings, leading to misinterpretation of results and suboptimal application of algorithms. To become truly proficient, delve into the theoretical underpinnings of algorithms, including graph theory, combinatorics, and number theory. This knowledge empowers you to not only use existing algorithms effectively but also to devise new ones and optimize current solutions. In doing so you gain the ability to customize applications, interpret results accurately, and make informed decisions when choosing between different approaches. This deeper comprehension also facilitates innovation and the ability to tackle complex problems more effectively.

Participate in Kaggle competitions to test your knowledge, share solutions, and learn from others. These challenges expose you to diverse problem-solving techniques. Use community platforms like Discord and forums to network and collaborate. Attend Meetups, and join LinkedIn and X groups to stay updated on trends and events, which can accelerate learning and open new opportunities. Create content to solidify your understanding and build your brand. Write articles on Medium about interesting topics or challenges, make YouTube videos explaining complex concepts, and share your code on GitHub to showcase your skills. Engage in coding competitions and hackathons to challenge yourself, network, and gain real-world experience for your portfolio.

Building a career becomes easier with a strong network. Since college, I've engaged with like-minded individuals, participated in contests and hackathons, expanding my circle and gaining valuable insights from domain experts. Starting early on LinkedIn, I've grown my network to 5-7k connections. I enjoy learning from articles shared within my network and interacting with professionals who share my interests. Sometimes I also reach out to people seeking guidance and believe me people do help in LinkedIn :)

Engage in online and offline communities to maximize growth. Join algorithm-focused groups on LinkedIn, X to stay updated. Participate in forums, Discord, share knowledge. Attend Meetups, conferences, workshops to connect and gain insights from the more experienced. Create and share content to establish your presence. Write articles about your experiences or even start a YouTube channel to explain concepts or showcase projects. Participate in Kaggle, hackathons to test skills and interact with algorithm enthusiasts. Use GitHub to showcase and contribute to open-source initiatives, attracting collaborators and demonstrating skills to employers.

The one thing about IT these days is technologies keep changing every day. Let's take an example of google maps. It uses Dijstra's algorithm to find the smallest path between 2 points. These are the things one should stay updated on. So when its the time, you can showcase it to the world or invent your own.

After you have practised hard enough and you think you have sufficient skills to crack interviews, you can practise your skills via mock interviews or test yourselves on coding platforms like HackerRank or CodeChef.

Navigating the challenges of transitioning into algorithm careers involves building a strong foundation in core algorithms and data structures through study and practice. Engage in coding challenges, competitions, and open-source projects for practical experience. Network with professionals, stay updated on industry trends, and seek mentorship for guidance. Adaptability, continuous learning, and perseverance are key to overcoming challenges and succeeding in the dynamic field of algorithms.