This week helped me understand algorithms way better because Big O, Theta, and Omega finally started to click, especially when I practiced them on both iterative and recursive problems and working through recurrences and brute force examples showed me why efficiency actually matters instead of just getting a solution that works. In terms of what I learned, I learned that Big O, Big Theta, and Big Omega are used to describe how fast an algorithm grows as input gets bigger. Big O shows the worst case, Big Omega shows the best case, and Big Theta shows the exact growth when best and worst are the same. Learning these helped me better understand algorithm efficiency and why some solutions scale better than others.