This course introduces the foundational concepts of data structures and algorithm complexity, which are essential for computer science, programming, and technical interview preparation.

The course begins with a general introduction to data structures, explaining how data is organized, stored, and managed efficiently in computer systems. It builds the foundation for understanding more advanced computational concepts.

It then moves into algorithm complexity, explaining why performance analysis is important when designing efficient programs. Learners are introduced to the idea of measuring how algorithms behave as input size increases.

A key focus of the course is Big O notation, which is explained as a mathematical way to describe the efficiency of algorithms. It helps in understanding how fast or slow an algorithm performs in worst-case scenarios.

The course also provides practical examples of Big O notation, helping learners understand how different algorithms compare in terms of time complexity.

By the end of this course, learners will understand the basics of data structures and have a clear understanding of algorithm complexity and Big O notation, forming a strong foundation for advanced programming and problem