Search results for "Python Programming for Mathematics"
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Text Analysis in Python for Social Scientists
Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
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R 397.00
Python for MBAs
From the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. An increasing number of MBAs are choosing to pursue careers in tech. For them and other professionals, having some basic coding knowledge is a must.This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. One of the most popular programming languages, Python is used for tasks such as building and running websites, data analysis, machine learning, and natural-language processing. Drawing on years of experience providing instruction in this material at Columbia Business School as well as extensive backgrounds in technology, entrepreneurship, and consulting, Mattan Griffel and Daniel Guetta teach the basics of programming from scratch. Beginning with fundamentals such as variables, strings, lists, and functions, they build up to data analytics and practical ways to derive value from large and complex datasets. They focus on business use cases throughout, using the real-world example of a major restaurant chain to offer a concrete look at what Python can do. Written for business students with no previous coding experience and those in business roles that include coding or working with coding teams, Python for MBAs is an indispensable introduction to a versatile and powerful programming language.
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Mathematical Programming
This book serves as an introductory text in mathematical programming and optimization for students having a mathematical background that includes one semester of linear algebra and a complete calculus sequence. It includes computational examples to aid students develop computational skills.
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Absolute Beginner's Guide to Python Programming
Written as an illustrated, step-by-step guide, this book will introduce you to Python with examples using the latest version of the language. You'll begin by learning to set up your Python environment. The next few chapters cover the basics of Python such as language classifications, Python language syntax, and how to write a program. Next, you will learn how to work with variables, basic data types, arithmetic, companion, and Boolean operators, followed by lab exercises. Further, the book covers flow control, using functions, and exception handling, as well as the principles of object-oriented programming and building an interface design. The last section explains how to develop a game by installing PyGame and how to use basic animation, and concludes with coverage of Python web development with web servers and Python web frameworks. The Absolute Beginners Guide to Python Programming will give you the tools, confidence, and inspiration to start writing Python programs. If you are a programmer, developer, or a student, or someone who wants to learn on their own, this book is for you.What You Will LearnGain an understanding of computer programming Understand different data and data typesWork with Classes and OOPBuild interfaces, simple games, and web development with PythonWho This Book Is ForSoftware programmers, developers or students, or anyone who wants to learn Python programming on their own.
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Oxford Mathematics Primary Years Programme Practice and Mastery Book 3
Provide opportunities to consolidate mathematical knowledge and skills and help learners to grow in confidence and independence with this colourful, inquiry-based Practice and Mastery Book.· Cover the five strands of Number, Pattern and Function, Measurement, Shape and Space, and Data Handling· Provide comprehensive coverage of the PYP Mathematics Scope and Sequence· Support and extend learning through reinforcement activities· Encourage learners to explore and apply knowledge using real-world problems
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Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
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Solid Mechanics With Python: Non-cartesian Coordinates
This comprehensive volume covers the fundamental principles of solid mechanics, focusing on the mechanical behavior of solids in two and three dimensions under various types of loads. The solids are defined in non-Cartesian coordinate systems, such as polar, cylindrical, and spherical coordinate systems.The methodology and techniques to solve solid mechanics problems are explained in detail, formulated and demonstrated with numerous examples and illustrations. Two major approaches, the stress method and the displacement method, are introduced. Python is used for formulation derivation, solving problems symbolically or numerically, and plotting results graphically.Written in Jupyter notebook format, this useful reference text offers a unified environment for theory description, code execution, and formulation, for easy reading and practicing.
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Practical Programming 2e
This book is for anyone who wants to understand computer programming. You'll learn to program in a language that' s used in millions of smartphones, tablets, and PCs. You'll code along with the book, writing programs to solve real-world problems as you learn the fundamentals of programming using Python 3. You'll learn about design, algorithms, testing, and debugging, and come away with all the tools you need to produce quality code. In this second edition, we've updated almost all the material, incorporating the lessons we've learned over the past five years of teaching Python to people new to programming. You don't need any programming experience to get started. First, you'll get a detailed introduction to Python and to programming. You'll find out exactly what happens when your programs are executed. Through real-world examples, you'll learn how to work with numbers, text, big data sets, and files. Then you'll see how to create and use your own data types. The incremental examples show you the steps and missteps that happen while developing programs, so you know what to expect when you tackle a problem on your own.Inspired by "How to Design Programs" (HtDP), you'll learn a six-step recipe for designing functions, which helps you as you start to learn the concepts--and becomes an integral part of writing programs by the end. As you learn to use the fundamental programming tools in the first half of the book, you'll see how to document and organize your code so that you and other programmers can more easily read and understand it. Beyond the basics, you'll learn how to ensure that your programs are reliable, and how to work with databases, download data from the web automatically, and build user interfaces. Most importantly, you'll learn how to think like a professional programmer. You'll need to download Python 3, available from "python.org":https://python.org. With that download comes IDLE, the editor we use for writing and running Python programs. (If you use Linux, you may need to install Python 3 and IDLE separately.)
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R 684.53
Python Scripting for Computational Science
With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.
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Conceptual Programming with Python
Thorsten and Isaac have written this book based on a programming course we teach for Master's Students at the School of Computer Science of the University of Nottingham. The book is intended for students with little or no background in programming coming from different backgrounds educationally as well as culturally. It is not mainly a Python course but we use Python as a vehicle to teach basic programming concepts. Hence, the words conceptual programming in the title. We cover basic concepts about data structures, imperative programming, recursion and backtracking, object-oriented programming, functional programming, game development and some basics of data science.
R 786.03
R 306.00
Practical Maya Programming with Python
"Practical Maya Programming with Python" is a practical tutorial packed with plenty of examples and sample projects which guides you through building reusable, independent modules and handling unexpected errors. If you are a developer looking to build a powerful system using Python and Maya's capabilities, then this book is for you. Practical Maya Programming with Python is perfect for intermediate users with basic experience in Python and Maya who want to better their knowledge and skills.
R 1,661.00
Mathematica GuideBook for Programming
This comprehensive, detailed reference provides readers with both a working knowledge of Mathematica in general and a detailed knowledge of the key aspects needed to create the fastest, shortest, and most elegant implementations possible. It gives users a deeper understanding of Mathematica by instructive implementations, explanations, and examples from a range of disciplines at varying levels of complexity. The three volumes -- Programming, Graphics, and Mathematics, total 3,000 pages and contain more than 15,000 Mathematica inputs, over 1,500 graphics, 4,000+ references, and more than 500 exercises.This first volume begins with the structure of Mathematica expressions, the syntax of Mathematica, its programming, graphic, numeric and symbolic capabilities. It then covers the hierarchical construction of objects out of symbolic expressions, the definition of functions, the recognition of patterns and their efficient application, program flows and program structuring, and the manipulation of lists.An indispensible resource for students, researchers and professionals in mathematics, the sciences, and engineering.
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Practical Healthcare Statistics with Examples in Python and R
Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis.The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data.These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare.
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