Learning Scientific Programming With Python
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Learning Scientific Programming With Python
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Author : Christian Hill
language : en
Publisher: Cambridge University Press
Release Date : 2015
Learning Scientific Programming With Python written by Christian Hill and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computers categories.
Learn to master basic programming tasks from scratch with real-life scientific examples in this complete introduction to Python.
Learning Scientific Programming With Python
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Author : Christian Hill
language : en
Publisher: Cambridge University Press
Release Date : 2020-10-22
Learning Scientific Programming With Python written by Christian Hill and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-22 with Computers categories.
This fast-paced introduction to Python moves from the basics to advanced concepts, enabling readers to gain proficiency quickly.
Python Programming For Data Analysis
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Author : José Unpingco
language : en
Publisher: Springer Nature
Release Date : 2021-05-04
Python Programming For Data Analysis written by José Unpingco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-04 with Technology & Engineering categories.
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
Introduction To Scientific Programming With Python
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Author : Pankaj Jayaraman
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Introduction To Scientific Programming With Python written by Pankaj Jayaraman and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
"Introduction to Scientific Programming with Python" offers an immersive exploration into the dynamic field of scientific programming using Python. We cater to a diverse audience, serving as an entry point for novices and a valuable resource for seasoned practitioners in scientific computing. Python's popularity in scientific circles stems from its readability, versatility, and extensive libraries for numerical computing, data analysis, and visualization. We cover fundamental programming concepts and gradually introduce advanced techniques specific to scientific applications. From mastering Python basics to exploring advanced topics like machine learning and symbolic mathematics, each chapter provides a structured and hands-on learning experience. Real-world case studies, practical examples, and exercises ensure readers grasp theoretical concepts and gain practical skills. Throughout the book, Python becomes a tool of empowerment, enabling readers to unravel complex scientific data, model intricate phenomena, and contribute meaningfully to their fields. "Introduction to Scientific Programming with Python" is an invaluable companion for harnessing Python's potential in scientific inquiry and discovery. By the end, readers will have a robust foundation in Python and the confidence to apply scientific programming methodologies to real-world problems. This book unlocks the door to a world where Python drives exploration, discovery, and innovation in science.
Utilizing Rapidminer Python And R For Data Mining Applications
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Author : Ramjan, Sarawut
language : en
Publisher: IGI Global
Release Date : 2025-05-02
Utilizing Rapidminer Python And R For Data Mining Applications written by Ramjan, Sarawut and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Computers categories.
In data mining, powerful tools like RapidMiner, Python, and R revolutionize how organizations gain valuable insights from large amounts of data. RapidMiner offers a visual interface for designing data workflows, making it ideal for both beginners and advanced practitioners. Python provides an environment for automating and customizing data mining tasks, while R is used for its statistical capabilities and packages for advanced analytics. Together, these tools empower data scientists and analysts to apply machine learning algorithms, statistical models, and data preprocessing techniques efficiently, facilitating deeper understanding and data-driven decision-making across industries. Utilizing RapidMiner, Python, and R for Data Mining Applications explores the integration and application of these three powerful tools in the context of real-world data mining tasks. It delves into the strengths and features of each tool, showcasing how they can be leveraged individually or in combination to handle various stages of the data mining pipeline. This book covers topics such as data clustering, software installation, and programming languages, and is a useful resource for engineers, business owners, academicians, researchers, and data scientists.
Advanced Applications Of Python Data Structures And Algorithms
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Author : Galety, Mohammad Gouse
language : en
Publisher: IGI Global
Release Date : 2023-07-05
Advanced Applications Of Python Data Structures And Algorithms written by Galety, Mohammad Gouse and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-05 with Computers categories.
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision. Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
A Student S Guide To Python For Physical Modeling
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Author : Jesse M. Kinder
language : en
Publisher: Princeton University Press
Release Date : 2021-08-03
A Student S Guide To Python For Physical Modeling written by Jesse M. Kinder and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-03 with Science categories.
A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.
Introduction To Engineering And Scientific Computing With Python
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Author : David E. Clough
language : en
Publisher: CRC Press
Release Date : 2022-09-07
Introduction To Engineering And Scientific Computing With Python written by David E. Clough and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-07 with Computers categories.
As more and more engineering departments and companies choose to use Python, this book provides an essential introduction to this open-source, free-to-use language. Expressly designed to support first-year engineering students, this book covers engineering and scientific calculations, Python basics, and structured programming. Based on extensive teaching experience, the text uses practical problem solving as a vehicle to teach Python as a programming language. By learning computing fundamentals in an engaging and hands-on manner, it enables the reader to apply engineering and scientific methods with Python, focusing this general language to the needs of engineers and the problems they are required to solve on a daily basis. Rather than inundating students with complex terminology, this book is designed with a leveling approach in mind, enabling students at all levels to gain experience and understanding of Python. It covers such topics as structured programming, graphics, matrix operations, algebraic equations, differential equations, and applied statistics. A comprehensive chapter on working with data brings this book to a close. This book is an essential guide to Python, which will be relevant to all engineers, particularly undergraduate students in their first year. It will also be of interest to professionals and graduate students looking to hone their programming skills, and apply Python to engineering and scientific contexts.
Materials Data Science
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Author : Stefan Sandfeld
language : en
Publisher: Springer Nature
Release Date : 2024-05-08
Materials Data Science written by Stefan Sandfeld and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-08 with Technology & Engineering categories.
This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented “from scratch” using Python and NumPy. The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes’ theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers. The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced. The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a “black box”. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented “from scratch” using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.
An Object Oriented Python Cookbook In Quantum Information Theory And Quantum Computing
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Author : M.S. Ramkarthik
language : en
Publisher: CRC Press
Release Date : 2022-09-30
An Object Oriented Python Cookbook In Quantum Information Theory And Quantum Computing written by M.S. Ramkarthik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Computers categories.
This first-of-a-kind textbook provides computational tools in state-of-the-art OOPs Python that are fundamental to quantum information, quantum computing, linear algebra and one-dimensional spin half condensed matter systems. Over 104 subroutines are included, and the codes are aided by mathematical comments to enhance clarity. Suitable for beginner and advanced readers alike, students and researchers will find this textbook to be a helpful guide and a compendium which they can readily use. Features Includes over 104 codes in OOPs Python, all of which can be used either as a standalone program or integrated with any other main program without any issues. Every parameter in the input, output and execution has been provided while keeping both beginner and advanced users in mind. The output of every program is explained thoroughly with detailed examples. Detailed mathematical commenting is done alongside the code which enhances clarity about the flow and working of the code.