Johansson, R: Numerical Python -

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Résumé :
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.

Biographie:
Robert Johansson is a numerical Python expert, computational scientist. He has experience with SciPy, NumPy and works on QuTiP, an open-source python framework for simulating the dynamics of quantum systems.

Sommaire:
1. Introduction to computing with Python

2. Vectors, matrices and multidimensional arrays

3. Symbolic computing

4. Plotting and visualization

5. Equation solving

6. Optimization

7. Interpolation

8. Integration

9. Ordinary differential equations

10. Sparse matrices and graphs

11. Partial differential equations

12. Data processing and analysis

13. Statistics

14. Statistical modeling

15. Machine learning

16. Bayesian statistics

17. Signal and image processing

18. Data input and output

19. Code optimization

20. Appendix: Installation