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How to prepare to become a good software engineer?
What types of Data Structure should I practice? How can I prepare for interview such as technical session and behavioral session?
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semi’s Answer
Master the fundamentals first: core programming concepts, clean code, and basic system knowledge. Build small, real projects consistently.
Practice arrays, strings, linked lists, stacks, queues, hash maps, trees, graphs, recursion, sorting, searching, and basic dynamic programming.
For technical interviews, practice timed problems and explain your thinking clearly. For behavioral rounds, prepare real stories about teamwork, challenges, and learning from mistakes. Communication is as important as code.
Practice arrays, strings, linked lists, stacks, queues, hash maps, trees, graphs, recursion, sorting, searching, and basic dynamic programming.
For technical interviews, practice timed problems and explain your thinking clearly. For behavioral rounds, prepare real stories about teamwork, challenges, and learning from mistakes. Communication is as important as code.
Updated
Joseph’s Answer
To be a good software engineer, you need to know how to use your tools well every day. If you want to work with data structures, a strong math background is important, especially in fields like Generative AI, which handle big data sets to get results.
AI isn't just about mysterious models; these models need well-organized data to work.
In Generative AI, text or images are turned into complex vectors. To manage these, you need special data structures like k-d trees or graphs for finding similar items, like the closest match to a given input.
The "T" in ChatGPT stands for Transformer. This system uses stacks and queues to control the order of tasks and manage how attention moves across words in a sequence.
Handling large language models requires knowing hash tables and heaps. This knowledge helps keep data retrieval fast, so response times don't get too slow.
Alison.com course "Applied Math: Practical Machine Learning with TensorFlow" if you want to see the math in action using code, this course bridges the theory with Google’s AI framework.
AI isn't just about mysterious models; these models need well-organized data to work.
In Generative AI, text or images are turned into complex vectors. To manage these, you need special data structures like k-d trees or graphs for finding similar items, like the closest match to a given input.
The "T" in ChatGPT stands for Transformer. This system uses stacks and queues to control the order of tasks and manage how attention moves across words in a sequence.
Handling large language models requires knowing hash tables and heaps. This knowledge helps keep data retrieval fast, so response times don't get too slow.
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