{"product_id":"numerical-python-scientific-computing-and-data-science-applications-with-numpy-scipy-and-matplotlib-paperback","title":"Numerical Python: Scientific Computing and Data Science Applications with Numpy, Scipy and Matplotlib - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eRobert Johansson\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLearn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eNumerical Python, Third Edition\u003c\/em\u003e, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. \u003c\/p\u003e \u003cp\u003eAfter reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I\/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat You'll Learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWork with vectors and matrices using NumPy\u003c\/li\u003e \u003cli\u003eReview Symbolic computing with SymPy\u003c\/li\u003e \u003cli\u003ePlot and visualize data with Matplotlib\u003c\/li\u003e \u003cli\u003ePerform data analysis tasks with Pandas and SciPy\u003c\/li\u003e \u003cli\u003eUnderstand statistical modeling and machine learning with statsmodels and scikit-learn\u003c\/li\u003e \u003cli\u003eOptimize Python code using Numba and Cython\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho This Book Is For\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis. \u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eLearn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.\u003c\/p\u003e \u003cp\u003e\u003cem\u003eNumerical Python, Third Edition\u003c\/em\u003e, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. \u003c\/p\u003e \u003cp\u003eAfter reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I\/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat You'll Learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWork with vectors and matrices using NumPy\u003c\/li\u003e \u003cli\u003eReview Symbolic computing with SymPy\u003c\/li\u003e \u003cli\u003ePlot and visualize data with Matplotlib\u003c\/li\u003e \u003cli\u003ePerform data analysis tasks with Pandas and SciPy\u003c\/li\u003e \u003cli\u003eUnderstand statistical modeling and machine learning with statsmodels and scikit-learn\u003c\/li\u003e \u003cli\u003eOptimize Python code using Numba and Cython\u003c\/li\u003e \u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRobert Johansson\u003c\/strong\u003e is a numerical Python expert and computational scientist who has worked with SymPy, NumPy, and QuTiP, an open-source Python framework for simulating the dynamics of quantum systems.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 492\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.03 x 10 x 7.01 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 28, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":43371739611271,"sku":"9798868804120","price":37.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/ePkfOvr4qx9798868804120.webp?v=1761331808","url":"https:\/\/booksby.splitshops.com\/products\/numerical-python-scientific-computing-and-data-science-applications-with-numpy-scipy-and-matplotlib-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}