I often hear data scientists mention that they're not "developers" and therefore don't need an in-depth understanding of Python. However, this perspective might limit your potential.
Gaining advanced proficiency in Python can significantly enhance your ability to write efficient, readable, and maintainable code, simplifying complex data analysis and machine learning tasks. It also allows you to leverage Python's extensive libraries and frameworks more effectively, facilitating the development of sophisticated models and solutions. Moreover, mastering advanced Python concepts opens doors to innovative problem-solving approaches, enabling you to tackle data challenges with greater precision and creativity. Investing time in advancing your Python skills not only boosts your productivity but also positions you to contribute more profoundly to the evolving field of data science.
To help you continue pushing the boundaries of your knowledge and embrace the continuous learning that makes data science such a dynamic and rewarding field, here are five valuable resources:
Books:
"Dead Simple Python" by Jason C. McDonald
"Python Crash Course" by Eric Matthes
Courses:
Collection of Advanced python tutorials
"Learn Intermediate Python" by codecademy https://www.codecademy.com/learn/learn-intermediate-python-3
"Intermediate tutorials" by realpython https://realpython.com/tutorials/intermediate
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