Search results for "python programming for mathematics"
Items we need to order for you
R 3,114.00
R 428.00
R Programming for Actuarial Science
Professional resource providing basic to intermediate levels of R coding in respect of actuarial applications, with real-life examples R Programming for Actuarial Science provides a grounding in R programming applied to the mathematical and statistical methods that are of relevance for actuarial work, equipping the student with knowledge of statistical distributions and methods to summarize data. The authors have a combined experience of 20 years in actuarial consultancies and insurance companies, as well as over 20 years in university teaching and research. In R Programming for Actuarial Science, readers will find: Basic theory for each chapter—the length matching the complexity of the topic—to complement other actuarial textbooks which provide foundational theory in depthInformation on compound interest, statistical inference, asset-liability matching, time series, loss distributions, contingencies, mortality models, and option pricingExercises to write code, to enable students to gain a better understanding of underlying mathematical and statistical principlesAn overall basic to intermediate level of coverage in respect of numerous actuarial applications, and real-life examples included with every topic Providing a highly useful combination of practical discussion and basic theory, R Programming for Actuarial Science is an essential reference for BSc/MSc students in actuarial science, trainee actuaries studying privately, and qualified actuaries with little programming experience, along with undergraduate students studying finance, business, and economics.
R 2,343.00
R 1,069.00
Interior Point Methods of Mathematical Programming
One has to make everything as simple as possible but, never more simple. Albert Einstein Discovery consists of seeing what every body has seen and thinking what nobody has thought. Albert S. entGyorgy; The primary goal of this book is to provide an introduction to the theory of Interior Point Methods (IPMs) in Mathematical Programming. At the same time, we try to present a quick overview of the impact of extensions of IPMs on smooth nonlinear optimization and to demonstrate the potential of IPMs for solving difficult practical problems. The Simplex Method has dominated the theory and practice of mathematical pro gramming since 1947 when Dantzig discovered it. In the fifties and sixties several attempts were made to develop alternative solution methods. At that time the prin cipal base of interior point methods was also developed, for example in the work of Frisch (1955), Caroll (1961), Huard (1967), Fiacco and McCormick (1968) and Dikin (1967). In 1972 Klee and Minty made explicit that in the worst case some variants of the simplex method may require an exponential amount of work to solve Linear Programming (LP) problems. This was at the time when complexity theory became a topic of great interest. People started to classify mathematical programming prob lems as efficiently (in polynomial time) solvable and as difficult (NP-hard) problems. For a while it remained open whether LP was solvable in polynomial time or not. The break-through resolution ofthis problem was obtained by Khachijan (1989).
R 9,005.06
R 306.00
R 2,127.20
R 1,221.00
R 517.74
R 624.00
Computer Coding Python Games for Kids
Learn how to make your own games using the UK's No 1 coding language, PythonBuild and play your own computer games, from creative quizzes to perplexing puzzles, by coding them in the Python programming language!Whether you're a seasoned programmer or a beginner hoping to learn Python, you'll find Computer Coding Python Games for Kids fun to read and easy to follow. Each chapter shows you how to construct a complete working game in simple numbered steps. Using freely available resources such as Pygame, Pygame Zero, and a downloadable pack of images and sounds, you can add animations, music, scrolling backgrounds, scenery, and other exciting professional touches. After building the game, find out how to adapt it to create your own personalised version with secret hacks and cheat codes! You'll master the key concepts that programmers need to write code - not just in Python, but in all programming languages. Find out what bugs, loops, flags, strings, and turtles are. Learn how to plan and design the ultimate game, and then play it to destruction as you test and debug it. Before you know it, you'll be a coding genius!
R 526.00
Advanced Computational Techniques in Mathematics
Mathematical modeling and computational techniques are crucial for solving complex real-world problems across diverse domains. This book presents cutting-edge research in applied mathematics, covering topics such as fractional differential equations, fuzzy numerical techniques, nonlinear programming, disease modeling, social network analysis, deep learning, and optimization algorithms. It provides rigorous mathematical formulations, algorithmic implementations, and real-world applications in agriculture, medicine, environmental sciences, and engineering. Readers will gain insights into innovative problem-solving approaches, from stability analysis and bifurcations to AI-driven disease detection and forensic analysis. With a balance of theoretical foundations and practical applications, this volume is an essential resource for researchers, academics, and professionals seeking to advance their understanding of modern mathematical techniques and computational advancements.
R 8,121.00
R 610.00
R 2,019.00
Bird's Basic Engineering Mathematics
Now in its eighth edition, Bird’s Basic Engineering Mathematics has helped thousands of students to succeed in their exams. Mathematical theories are explained in a straightforward manner, supported by practical engineering examples and applications to ensure that readers can relate theory to practice. Some 1,000 engineering situations/problems have been ‘flagged-up’ to help demonstrate that engineering cannot be fully understood without a good knowledge of mathematics.The extensive and thorough coverage makes this a great text for introductory level engineering courses – such as for aeronautical, construction, electrical, electronic, mechanical, manufacturing engineering and vehicle technology – including for BTEC First, National and Diploma syllabuses, City & Guilds Technician Certificate and Diploma syllabuses, and even for GCSE revision.Its companion website provides extra materials for students and lecturers, including full solutions for all 1,700 further questions, lists of essential formulae, multiple choice tests, and illustrations, as well as full solutions to revision tests for course instructors.
R 2,161.00
R 183.00
R 916.00
Mathematics for Machine Learning
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
R 2,007.00
Mathematics Before and After Pythagoras
This book provides the reader with a comprehensive account of the contributions of Pythagoras to mathematics and philosophy, using them as a starting point to compare pre-Pythagorean accomplishments with the myriad mathematical developments that followed. It begins with a thorough study of Pythagoreanism and the early Pythagoreans, including the major events in Pythagoras' life and the origins of the mystical significance attributed by Pythagoreans to natural numbers. From Chapter 3 onward, the book describes how mathematical thinking works and prepares the reader for the subsequent chapters, which cover mathematical logic and proofs, their application to the study of natural and prime numbers, the investigation of Pythagorean triples, figurative numbers, and irrational numbers, all interwoven with rich historical context.Aimed at students and teachers at all levels, this work is accessible to non-mathematicians as well, with the main prerequisite being an avid curiosity about some of the ideas and thinkers that helped to forge the mathematical world as we know it. Early praises for “Mathematics Before and After Pythagoras”:“Your book is charming and fun to read. It would be fine to be able to teach from it.” (Steve Krantz, USA)“...your new book, an obvious labor of love... I can see that it will be an inspiration for young students.” (Bruce Berndt, USA)“It is an excellent book, and I am deeply grateful for sending it to me. It is an extraordinary gift, and I am so grateful for this.” (Carlo Cattani, Italy)“I am really impressed by the wealth of interesting material you have collected and presented.” (Rainer Kress, Germany)
R 7,769.61
R 611.00
Mathematics for the IB Diploma: Applications and interpretation HL
Developed in cooperation with the International Baccalaureate®Enable students to construct mathematical models by exploring challenging problems and the use of technology. - Engage and excite students with examples and photos of maths in the real world, plus inquisitive starter activities to encourage their problem-solving skills. - Build mathematical thinking with our 'Toolkit' and mathematical exploration chapter, along with our new toolkit feature of questions, investigations and activities. - Develop understanding with key concepts and applications integrated throughout, along with TOK links for every topic. - Prepare your students for assessment with worked examples, extended essay support and colour-coded questions to highlight the level of difficulty and the different types of questions. - Check understanding with review exercise at the end of the textbook. Follows the new 2019 IB Guide for Mathematics: applications and interpretation Higher LevelAvailable in the seriesMathematics for the IB Diploma: Applications and interpretation SLStudent Book ISBN: 9781510462380Student Book Boost eBook ISBN: 9781398334359Exam Practice Workbook SL: 9781398321892Exam Practice Workbook SL Boost eBook: 9781398342323Mathematics for the IB Diploma: Applications and interpretation HLStudent Book ISBN: 9781510462373Student Book Boost eBook ISBN: 9781398334366Exam Practice Workbook HL: 9781398321885Exam Practice Workbook HL Boost eBook: 9781398342354SL & HL Boost Subscription: 9781398341272
R 2,325.00
Probability and Statistics for Data Science
This self-contained guide introduces two pillars of data science, probability theory, and statistics, side by side, in order to illuminate the connections between statistical techniques and the probabilistic concepts they are based on. The topics covered in the book include random variables, nonparametric and parametric models, correlation, estimation of population parameters, hypothesis testing, principal component analysis, and both linear and nonlinear methods for regression and classification. Examples throughout the book draw from real-world datasets to demonstrate concepts in practice and confront readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.
R 5,940.16
Introduction to Linear and Convex Programming
This introduction to optimization emphasizes the need for both a pure and an applied mathematical point of view. Beginning with a chapter on linear algebra and Euclidean geometry, the author then applies this theory with an introduction to linear programming. There follows a discussion of convex analysis, which finds application in non-linear programming. The book ends with an extensive commentary to the exercises that are given at the end of each chapter. The author's straightforward, geometrical approach makes this an attractive textbook for undergraduate students of mathematics, engineering, operations research and economics.
R 2,243.46
Mathematical Summary for Digital Signal Processing Applications with Matlab
Mathematical summary for Digital Signal Processing Applications with Matlab consists of Mathematics which is not usually dealt in the DSP core subject, but used in DSP applications. Matlab programs with illustrations are given for the selective topics such as generation of Multivariate Gaussian distributed sample outcomes, Bacterial foraging algorithm, Newton’s iteration, Steepest descent algorithm, etc. are given exclusively in the separate chapter. Also Mathematical summary for Digital Signal Processing Applications with Matlab is written in such a way that it is suitable for Non-Mathematical readers and is very much suitable for the beginners who are doing research in Digital Signal Processing.
R 4,643.87
R 611.00
Engineering Mathematics by Example
This textbook is a complete, self-sufficient, self-study/tutorial-type source of mathematical problems. It serves as a primary source for practicing and developing mathematical skills and techniques that will be essential in future studies and engineering practice. Rigor and mathematical formalism is drastically reduced, while the main focus is on developing practical skills and techniques for solving mathematical problems, given in forms typically found in engineering and science. These practical techniques are split into three separate books: the topics of algebra, complex algebra, and linear algebra (Vol. I), calculus of single and multiple argument functions (Vol. II), continues and discrete Convolution and Fourier integrals/sums of typical functions used in signal processing, and Laplace transform examples (Vol. III).
R 4,762.00
R 611.00
Beginnings of Greek Mathematics
When this book was first published, more than five years ago, I added an appendix on How the Pythagoreans discovered Proposition 11.5 of the 'Elements'. I hoped that this appendix, although different in some ways from the rest of the book, would serve to illustrate the kind of research which needs to be undertaken, if we are to acquire a new understanding of the historical development of Greek mathematics. It should perhaps be mentioned that this book is not intended to be an introduction to Greek mathematics for the general reader; its aim is to bring the problems associated with the early history of deductive science to the attention of classical scholars, and historians and philos ophers of science. I should like to conclude by thanking my translator, Mr. A. M. Ungar, who worked hard to produce something more than a mechanical translation. Much of his work was carried out during the year which I spent at Stanford as a fellow of the Center for Advanced Study in the Behavioral Sciences. This enabled me to supervise the work of transla tion as it progressed. I am happy to express my gratitude to the Center for providing me with this opportunity. Arpad Szabo NOTE ON REFERENCES The following books are frequently referred to in the notes. Unless otherwise stated, the editions are those given below. Burkert, W. Weisheit und Wissensclzaft, Studien zu Pythagoras, Philo laos und Platon, Nuremberg 1962.
R 6,011.86
Comprehensive Guide to Creating Graphics with MATLAB Programming
This textbook teaches you how to use MATLAB to create high-quality graphs using student-friendly statistical data at a level of mathematics accessible to middle or high school students. With more than 500 illustrated examples, this book quickly guides you to the essential information you need to create and customize high-quality graphs without requiring advanced mathematical knowledge. MATLAB gives you complete control over the appearance of each component in your graph. For example, in addition to creating the main graph, such as a scatter plot, line graph, bar graph, or histogram, you can also customize lines, legends, captions, titles, axis labels, gridlines, and many other elements. Whether you use this book for teaching and research or not, you will have the incredible power of MATLAB graphics at your fingertips.
R 5,129.00
R 672.00
R 599.00
R 757.00
Introduction to Mathematics for Economics with R
This book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.
R 2,198.00
R 700.00
R 305.00
Building Statistical Models in Python
Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insightsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesGain expertise in identifying and modeling patterns that generate successExplore the concepts with Python using important libraries such as stats modelsLearn how to build models on real-world data sets and find solutions to practical challengesBook DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learnExplore the use of statistics to make decisions under uncertaintyAnswer questions about data using hypothesis testsUnderstand the difference between regression and classification modelsBuild models with stats models in PythonAnalyze time series data and provide forecastsDiscover Survival Analysis and the problems it can solveWho this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.
R 1,693.00
R 916.00
R 428.00
