Machine learning models often process large datasets and perform complex computations. The efficiency of these operations depends significantly on the underlying data structures and algorithms employed. Choosing the right structure for storing data or the correct algorithm for a specific task can greatly influence training time, memory usage, and model scalability.

This initial chapter establishes the foundation for understanding this connection. We will begin by reviewing computational complexity analysis using Big O notation (e.g., $O(n)$, $O(n^2)$), an essential tool for evaluating performance trade-offs. We will then examine how standard Python data structures, such as lists and dictionaries, are used and perform within common ML workflows.

Particular focus will be placed on NumPy arrays and Pandas DataFrames, which are fundamental for numerical computation and data preparation in the Python machine learning ecosystem. You will learn about their specific strengths for handling large numerical datasets efficiently.

Finally, we'll start building the intuition required to map machine learning problems to suitable data structures. We will reinforce these concepts through practical examples, including profiling basic data operations to observe performance differences firsthand.