Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with Python
About This Book
Leverage the new features of the IPython notebook for interactive web-based big data analysis and visualization
Become an expert in high-performance computing and visualization for data analysis and scientific modeling
A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations
Who This Book Is For
Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists… Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
What You Will Learn
Code better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experiments
Master all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgets
Analyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn)
Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more
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