Actively learning the foundational concepts of system design, including how software components interact, basic architectural patterns, and essential design trade-offs. The focus is on developing system-level thinking around scalability, reliability, and performance using real-world examples.

Working on machine learning and data science projects involving data preprocessing, exploratory data analysis, feature engineering, and model development. Emphasis is placed on understanding how data-driven solutions are built, evaluated, and applied to real-world problems.

Regularly practicing competitive programming to strengthen problem-solving skills, improve algorithmic thinking, and write efficient code. This includes working on data structures, time complexity optimization, and logical reasoning through consistent practice.