Python can optimize complex data processing tasks through several means. Firstly, using libraries like NumPy and Pandas can significantly speed up operations on large datasets by leveraging optimized, low-level code. Additionally, employing parallel processing techniques, such as using the multiprocessing or threading modules, can take advantage of multiple CPU cores to process data more quickly.

Furthermore, optimizing algorithms and data structures can also lead to efficiency gains. For instance, using dictionaries for fast lookups or sorting algorithms like merge sort for efficient data sorting.

Lastly, utilizing tools like Cython or Numba to compile Python code to C or machine code can greatly improve performance for computationally intensive tasks.

For individuals looking to enhance their Python optimization skills, enrolling in a Python certification course can provide in-depth knowledge and practical experience in optimizing Python code for various data processing tasks.

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