Search results for "Python Programming for Mathematics"
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Introduction to Python and Computer Programming
This book introduces Python programming language and fundamental concepts in algorithms and computing. Its target audience includes students and engineers with little or no background in programming, who need to master a practical programming language and learn the basic thinking in computer science/programming. The main contents come from lecture notes for engineering students from all disciplines, and has received high ratings. Its materials and ordering have been adjusted repeatedly according to classroom reception. Compared to alternative textbooks in the market, this book introduces the underlying Python implementation of number, string, list, tuple, dict, function, class, instance and module objects in a consistent and easy-to-understand way, making assignment, function definition, function call, mutability and binding environments understandable inside-out. By giving the abstraction of implementation mechanisms, this book builds a solid understanding of the Python programming language.
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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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Python in easy steps
Python in easy steps, 3rd edition teaches you to program in the powerful Python programming language – the world’s favorite coding language. It gives complete examples that illustrate each aspect with colorized source code.Python in easy steps, 3rd edition begins by explaining how to install the free Python interpreter so that you can quickly begin to create your own executable programs by copying the book's examples. It demonstrates all the Python language basics before moving on to provide examples of Object-Oriented Programming (OOP). A new chapter for this edition then demonstrates how to process web form data with the Flask web framework for Python. The book concludes by demonstrating how you can use your acquired knowledge to create and deploy graphical windowed applications.Python in easy steps, 3rd edition has an easy-to-follow style, and makes no assumption you have previous knowledge of any programming language, so it's ideal for: The newcomer to computer programming. Programmers moving from another programming language.Students studying Python programming at school or college.Those seeking a career in computing who need a fundamental understanding of computer programming.
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Python for Beginners
Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily, there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python.Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python toolsOffers practical advice for each major area of development with both Python 3.x and Python 2.xBased on the latest research in cognitive science and learning theoryHelps the reader learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—featuresThis book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars.
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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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Introduction to Recursive Programming
Recursion is one of the most fundamental concepts in computer science and a key programming technique that allows computations to be carried out repeatedly. Despite the importance of recursion for algorithm design, most programming books do not cover the topic in detail, despite the fact that numerous computer programming professors and researchers in the field of computer science education agree that recursion is difficult for novice students. Introduction to Recursive Programming provides a detailed and comprehensive introduction to recursion. This text will serve as a useful guide for anyone who wants to learn how to think and program recursively, by analyzing a wide variety of computational problems of diverse difficulty.It contains specific chapters on the most common types of recursion (linear, tail, and multiple), as well as on algorithm design paradigms in which recursion is prevalent (divide and conquer, and backtracking). Therefore, it can be used in introductory programming courses, and in more advanced classes on algorithm design. The book also covers lower-level topics related to iteration and program execution, and includes a rich chapter on the theoretical analysis of the computational cost of recursive programs, offering readers the possibility to learn some basic mathematics along the way.It also incorporates several elements aimed at helping students master the material. First, it contains a larger collection of simple problems in order to provide a solid foundation of the core concepts, before diving into more complex material. In addition, one of the book's main assets is the use of a step-by-step methodology, together with specially designed diagrams, for guiding and illustrating the process of developing recursive algorithms. Furthermore, the book covers combinatorial problems and mutual recursion. These topics can broaden students' understanding of recursion by forcing them to apply the learned concepts differently, or in a more sophisticated manner.The code examples have been written in Python 3, but should be straightforward to understand for students with experience in other programming languages. Finally, worked out solutions to over 120 end-of-chapter exercises are available for instructors.
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Microcomputers and Mathematics
The interaction between computer and mathematics is becoming more and more important at all levels as computers become more sophisticated. This book shows how simple programs can be used to do significant mathematics. The purpose of this book is to give those with some mathematical background a wealth of material with which to appreciate both the power of the microcomputer and its relevance to the study of mathematics. The authors cover topics such as number theory, approximate solutions, differential equations and iterative processes, with each chapter self-contained. Many exercises and projects are included giving ready made material for demonstrating mathematical ideas. Only a fundamental knowledge of mathematics is assumed and programming is restricted to 'basic BASIC' which will be understood by any microcomputer. The book may be used as a textbook for algorithmic mathematics at several levels, with all the topics covered appearing in any undergraduate mathematics course.
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Concise Introduction to Programming in Python
A Concise Introduction to Programming in Python, Second Edition provides a hands-on and accessible introduction to writing software in Python, with no prior programming experience required.The Second Edition was thoroughly reorganized and rewritten based on classroom experience to incorporate: A spiral approach, starting with turtle graphics, and then revisiting concepts in greater depth using numeric, textual, and image data Clear, concise explanations written for beginning students, emphasizing core principles A variety of accessible examples, focusing on key concepts Diagrams to help visualize new concepts New sections on recursion and exception handling, as well as an earlier introduction of lists, based on instructor feedbackThe text offers sections designed for approximately one class period each, and proceeds gradually from procedural to object-oriented design. Examples, exercises, and projects are included from diverse application domains, including finance, biology, image processing, and textual analysis. It also includes a brief "How-To" sections that introduce optional topics students may be interested in exploring.The text is written to be read, making it a good fit in flipped classrooms. Designed for either classroom use or self-study, all example programs and solutions to odd-numbered exercises (except for projects) are available at: http://www.central.edu/go/conciseintro/.
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Linear Algebra With Python And Ai
This book is a compelling and accessible introduction to linear algebra, ideal for learners and educators across mathematics, data science, engineering, and the physical sciences. It begins with core topics such as systems of linear equations, matrix operations, and Row-Reduced Echelon Form (RREF), and builds a coherent pathway toward advanced concepts like eigenvalues, inner product spaces, and singular value decomposition (SVD). With a clear pedagogical structure and consistent notation, the text supports both conceptual understanding and practical application.What sets this textbook apart is its seamless integration of theory and computation. Python (NumPy/SymPy) is used throughout to implement algorithms, visualize geometric ideas, and connect abstract mathematics to real-world problems. Carefully designed TikZ diagrams enhance geometric intuition, while applications such as least squares, data compression, and principal component analysis (PCA) demonstrate the relevance of linear algebra in modern fields. Integrated coding examples and computational tools encourage hands-on exploration and foster algorithmic thinking.Whether you're mastering Gaussian elimination, exploring the Rank-Nullity Theorem, or experimenting with Python notebooks, Linear Algebra with Python and AI offers a unified and forward-looking approach. With its dual emphasis on mathematical rigor and computational practice, this book equips students with the tools to navigate the mathematical foundations of today's data-driven world.
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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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Learning Python: A Course in 16 Lessons for GCSE
This is an introductory text book that teaches Python programming. It is intended mainly for GCSE and IGCSE Computer Science students. It is a very practical book and assumes that the student knows little. The book starts by looking at the Python programming environment, then entering commands at the command prompt. Later on short programs,followed by longer programs. By the time the last chapter has been reached longer programs suitable for coursework are discussed. If used as a text book to learn programming, it is suggested that one lesson (chapter) from the book is studied each week. That way a student can learn Python in 4 months.
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Python for non-Pythonians
The book uses a very simple and accessible language. All the descriptions of Python functionalities come with intuitive examples to make you learn by doing. This is not a theoretical book and does not cover some of the most internal features of Python. The intention of the authors is to allow business oriented people to start using Python. The main reason for such a choice of style is due to the increasing number of requests by non-technical professionals to solve daily problems and tasks. Whether we want to append multiple spreadsheets or profile the customer base, being able to use a solid infrastructure which enables to collect, check, process, analyze data, and report results has become a basic requirement in most industries. The structure of this book replicates the structure of a grammar book. It first introduces the building blocks of Python - i.e., objects - which can be compared to the different types of words (e.g., verbs, nouns) of a language. It introduces readers to the syntax rules of Python - i.e., statements, functions, and classes. It then provides introduces how to use this new language for managing data. One thing that we found particularly helpful for our students is to provide wrong Python codes with the error message that Python provides, and ask them to fix the code in order to make it work. As making mistakes is very common, we would say normal, in programming, learning how to interpret the error messages and fix codes is extremely important from a pedagogical perspective. The book always challenges students with a series of "fix the code" questions at the end of each a topic. We adopt this structure because we found that students learn how to successfully write algorithms in Python when they understand its logic and the meaning of every single element of the algorithm. We have experimented that this natural progression, from the most basic elements to very complex scripts, raises students' self-confidence and proficiency in writing their own algorithms. Hence, the structure of the textbook greatly helps students and instructors to gradually acquire the "programming" mindset. Also, our teaching experience indicates that, as with any other language, students learn by practicing. For this reason our textbook, is rich in exercises of increasing complexity.
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Competitive Programming in Python
Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book and on the companion website.
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Oxford Mathematics Primary Years Programme Student Book 6
Build mathematical knowledge and skills and explore the PYP transdisciplinary themes via this colourful, inquiry-based Student Book.· Fully support the PYP approach with comprehensive coverage of the PYP Mathematics Scope and Sequence· Progressively build understanding via guided, independent and extended learning activities· Cover the five strands of Number, Pattern and Function, Measurement, Shape and Space, and Data Handling using the PYP methodology of constructing, transferring and applying meaning· Encourage learners to explore and apply knowledge using real-world problems
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Data Structures for Engineers and Scientists Using Python
The text covers the fundamentals of Python programming and the implementation of data structures using Python programming with the help of worked-out examples. It provides a learning tool for engineers as well as for researchers and scientists of advanced level. The text further discusses important concepts such as polynomial manipulation, sparse matrices, implementation of stack using the queue model and topological sorting.This book:Discusses the implementation of various data structures such as an array, stack, queue, tree and graph along with sorting and searching algorithms. Includes programming tips to highlight important concepts and help readers avoid common programming errors. Presents each concept of data structure with a different approach and implements the same using Python programming. Offers rich chapter-end pedagogy including objective-type questions (with answers), review questions and programming exercises to facilitate review. Covers fundamentals of Python up to object-oriented concepts including regular expression. It is primarily written for senior undergraduate, graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering and information technology.
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Primes and Programming
Numbers are part of our everyday experience and their properties have fascinated mankind since ancient times. Deciding whether a number is prime and if not, what its factors are, are both fundamental problems. In recent years analysis and solution of these problems have assumed commercial significance since large primes are an essential feature of secure methods of information transmission. The purely mathematical fascination that led to the development of methods for primality testing has been supplemented by the need to test within reasonable timescales, and computational methods have entered at all levels of number theory. In this book, Peter Giblin describes, in the context of an introduction to the theory of numbers, some of the more elementary methods for factorization and primality testing; that is, methods independent of a knowledge of other areas of mathematics. Indeed everything is developed from scratch so the mathematical prerequisites are minimal. An essential feature of the book is the large number of computer programs (written in Pascal) and a wealth of computational exercises and projects (in addition to more usual theory exercises). The theoretical development includes continued fractions and quadratic residues, directed always towards the two fundamental problems of primality testing and factorization. There is time, all the same, to include a number of topics and projects of a purely 'recreational' nature.
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