Chapter 19. Final Thoughts
Learning to program is not easy, but if you made it this far, you are off to a good start. Now I have some suggestions for ways you can keep learning and apply what you have learned.
This book is meant to be a general introduction to programming, so we have not focused on specific applications. Depending on your interests, there are any number of areas where you can apply your new skills.
If you are interested in data science, there are three books of mine you might like:
Think Stats: Exploratory Data Analysis (O’Reilly, 2014)
Think Bayes: Bayesian Statistics in Python (O’Reilly, 2021)
Think DSP: Digital Signal Processing in Python (O’Reilly, 2016)
If you are interested in physical modeling and complex systems, you might like:
Modeling and Simulation in Python: An Introduction for Scientists and Engineers (No Starch Press, 2023)
Think Complexity: Complexity Science and Computational Modeling (O’Reilly, 2018)
These use NumPy, SciPy, pandas, and other Python libraries for data science and scientific computing.
This book tries to find a balance between general principles of programming and details of Python. As a result, it does not include every feature of the Python language. For more about Python, and good advice about how to use it, I recommend Fluent Python: Clear, Concise, and Effective Programming, second edition by Luciano Ramalho (O’Reilly, 2022).
After an introduction to programming, a common next step is to learn about data structures and ...
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