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Learning Professional Python Volume 2: Advanced

Learning Professional Python Volume 2: Advanced

Volume 2 of Learning Professional Python is a resource for students who want to learn Python even if they don’t have any programming knowledge and for teachers who want a comprehensive introduction to Python to use with their students. This book helps the students achieve their dream job in the IT Industry and teaches the students in an easy understandable manner while strengthening coding skills. Learning Professional Python: Volume 2 Objectives Become familiar with the features of Python programming language Introduce the object-oriented programming concepts Discover how to write Python code by following the object-oriented programming concepts Become comfortable with concepts such as classes objects inheritance dynamic dispatch interfaces and packages Learn the Python generics and collections Develop exception handling and the multithreaded applications Design graphical user interface (GUI) applications | Learning Professional Python Volume 2: Advanced

GBP 52.99
1

Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

Most existing books on evolution equations tend either to cover a particular class of equations in too much depth for beginners or focus on a very specific research direction. Thus the field can be daunting for newcomers to the field who need access to preliminary material and behind-the-scenes detail. Taking an applications-oriented conversational approach Discovering Evolution Equations with Applications: Volume 2-Stochastic Equations provides an introductory understanding of stochastic evolution equations. The text begins with hands-on introductions to the essentials of real and stochastic analysis. It then develops the theory for homogenous one-dimensional stochastic ordinary differential equations (ODEs) and extends the theory to systems of homogenous linear stochastic ODEs. The next several chapters focus on abstract homogenous linear nonhomogenous linear and semi-linear stochastic evolution equations. The author also addresses the case in which the forcing term is a functional before explaining Sobolev-type stochastic evolution equations. The last chapter discusses several topics of active research. Each chapter starts with examples of various models. The author points out the similarities of the models develops the theory involved and then revisits the examples to reinforce the theoretical ideas in a concrete setting. He incorporates a substantial collection of questions and exercises throughout the text and provides two layers of hints for selected exercises at the end of each chapter. Suitable for readers unfamiliar with analysis even at the undergraduate level this book offers an engaging and accessible account of core theoretical results of stochastic evolution equations in a way that gradually builds readers’ intuition. | Discovering Evolution Equations with Applications Volume 2-Stochastic Equations

GBP 69.99
1

Mathematical Principles of the Internet Volume 2 Mathematics

Mathematical Principles of the Internet Volume 2 Mathematics

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead they cover a partial panorama and the key principles. Volume 1 explores Internet engineering while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes and an effort has been made to make this work succinct yet self-contained. Elements of information theory algebraic coding theory cryptography Internet traffic dynamics and control of Internet congestion and queueing theory are discussed. In addition stochastic networks graph-theoretic algorithms application of game theory to the Internet Internet economics data mining and knowledge discovery and quantum computation communication and cryptography are also discussed. In order to study the structure and function of the Internet only a basic knowledge of number theory abstract algebra matrices and determinants graph theory geometry analysis optimization theory probability theory and stochastic processes is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering. | Mathematical Principles of the Internet Volume 2 Mathematics

GBP 44.99
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Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach Volume 2 covers several important topics that were not included in the first volume. The second volume complements the first providing detailed solutions to the exercises in both volumes thereby greatly enhancing its appeal for use in advanced statistics programs. This volume can serve as a valuable reference. It can also serve as a resource in a mathematical statistics course for use in illustrating various theoretical concepts in the context of a relatively complex setting of great practical importance. Together with the first volume this volume provides a largely self-contained treatment of an important area of statistics and should prove highly useful to graduate students and others. Key Features: Includes solutions to the exercises from both the first and second volumes Includes coverage of several topics not covered in the first volume Highly valuable as a reference book for graduate students and researchers | Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

GBP 89.99
1

A Handbook of Statistical Analyses using R

A Handbook of Statistical Analyses using R

Like the best-selling first two editions A Handbook of Statistical Analyses using R Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression missing values and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3) which includes some slides that can be used in introductory statistics courses Whether you‘re a data analyst scientist or student this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples it emphasizes the practical application and interpretation of results.

GBP 180.00
1

Grid Computing Techniques and Applications

Grid Computing Techniques and Applications

Designed for senior undergraduate and first-year graduate students Grid Computing: Techniques and Applications shows professors how to teach this subject in a practical way. Extensively classroom-tested it covers job submission and scheduling Grid security Grid computing services and software tools graphical user interfaces workflow editors and Grid-enabling applications. The book begins with an introduction that discusses the use of a Grid computing Web-based portal. It then examines the underlying action of job submission using a command-line interface and the use of a job scheduler. After describing both general Internet security techniques and specific security mechanisms developed for Grid computing the author focuses on Web services technologies and how they are adopted for Grid computing. He also discusses the advantages of using a graphical user interface over a command-line interface and presents a graphical workflow editor that enables users to compose sequences of computational tasks visually using a simple drag-and-drop interface. The final chapter explains how to deploy applications on a Grid. The Grid computing platform offers much more than simply running an application at a remote site. It also enables multiple geographically distributed computers to collectively obtain increased speed and fault tolerance. Illustrating this kind of resource discovery this practical text encompasses the varied and interconnected aspects of Grid computing including how to design a system infrastructure and Grid portal. Supplemental Web ResourcesThe author’s Web site offers various instructional resources including slides and links to software for programming assignments. Many of these assignments do not require access to a Grid platform. Instead the author provides step-by-step instructions for installing open-source software to deploy and test Web and Grid services a Grid computing workflow editor to design and test workflows and a Grid computing portal to deploy portlets. | Grid Computing Techniques and Applications

GBP 69.99
1

Design and Analysis of Experiments and Observational Studies using R

Design and Analysis of Experiments and Observational Studies using R

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials A/B testing and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown a new R package will be developed to be used with book and a bookdown version of the book will be freely available. The proposed book will emphasize ethics communication and decision making as part of design data analysis and statistical thinking. | Design and Analysis of Experiments and Observational Studies using R

GBP 82.99
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Applied Stochastic Modelling

Applied Stochastic Modelling

Highlighting modern computational methods Applied Stochastic Modelling Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures this edition offers numerous updates throughout. New to the Second Edition An extended discussion on Bayesian methods A large number of new exercises A new appendix on computational methods The book covers both contemporary and classical aspects of statistics including survival analysis Kernel density estimation Markov chain Monte Carlo hypothesis testing regression bootstrap and generalised linear models. Although the book can be used without reference to computational programs the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB and R programs found in the text as well as lecture slides and other ancillary material are available for download at www. crcpress. com Continuing in the bestselling tradition of its predecessor this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

GBP 175.00
1

Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments provides the basics of bioinformatics and in-depth coverage of NCBI databases sequence alignment and NCBI Sequence Local Alignment Search Tool (BLAST). As bioinformatics has become essential for life sciences the book has been written specifically to address the need of a large audience including undergraduates graduates researchers healthcare professionals and bioinformatics professors who need to use the NCBI databases retrieve data from them and use BLAST to find evolutionarily related sequences sequence annotation construction of phylogenetic tree and the conservative domain of a protein to name just a few. Technical details of alignment algorithms are explained with a minimum use of mathematical formulas and with graphical illustrations. Key Features Provides readers with the most-used bioinformatics knowledge of bioinformatics databases and alignments including both theory and application via illustrations and worked examples. Discusses the use of Windows Command Prompt Linux shell R and Python for both Entrez databases and BLAST. The companion website (http://www. hamiddi. com/instructors/) contains tutorials R and Python codes instructor materials including slides exercises and problems for students. This is the ideal textbook for bioinformatics courses taken by students of life sciences and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research. | Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

GBP 82.99
1

A Primer on Linear Models

A Primer on Linear Models

A Primer on Linear Models presents a unified thorough and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact finite sample theory supporting common statistical methods. With coverage steadily progressing in complexity the text first provides examples of the general linear model including multiple regression models one-way ANOVA mixed-effects models and time series models. It then introduces the basic algebra and geometry of the linear least squares problem before delving into estimability and the Gauss Markov model. After presenting the statistical tools of hypothesis tests and confidence intervals the author analyzes mixed models such as two-way mixed ANOVA and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers. This book enables complete comprehension of the material by taking a general unifying approach to the theory fundamentals and exact results of linear models.

GBP 175.00
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Correspondence Analysis in Practice

Correspondence Analysis in Practice

Drawing on the author’s 45 years of experience in multivariate analysis Correspondence Analysis in Practice Third Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants subset CA multiple CA and joint CA translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social environmental and health sciences as well as marketing economics linguistics archaeology and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra Barcelona Spain where he teaches a course amongst others on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters mostly on correspondence analysis the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists sociologists data scientists and marketing professionals and has specialized in statistics in ecology and social science.

GBP 48.99
1

Geographic Data Science with Python

Geographic Data Science with Python

This book provides the tools the methods and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive large frequent and rapid data there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis by using geographical and computational reasoning it shows the reader how to unlock new insights hidden within data. Key Features: ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate adapt extend and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks covering spatial data mapping and spatial statistics whilst covering concepts such as clusters and outliers as geographic concepts. Intended for data scientists GIScientists and geographers the material provided in this book is of interest due to the manner in which it presents geospatial data methods tools and practices in this new field.

GBP 48.99
1

Introductory Analysis An Inquiry Approach

Introductory Analysis An Inquiry Approach

Introductory Analysis: An Inquiry Approach aims to provide a self-contained inquiry-oriented approach to undergraduate-level real analysis. The presentation of the material in the book is intended to be inquiry-oriented' in that as each major topic is discussed details of the proofs are left to the student in a way that encourages an active approach to learning. The book is self-contained in two major ways: it includes scaffolding (i. e. brief guiding prompts marked as Key Steps in the Proof) for many of the theorems. Second it includes preliminary material that introduces students to the fundamental framework of logical reasoning and proof-writing techniques. Students will be able to use the guiding prompts (and refer to the preliminary work) to develop their proof-writing skills. FeaturesStructured in such a way that approximately one week of class can be devoted to each chapterSuitable as a primary text for undergraduates or as a supplementary text for some postgraduate coursesStrikes a unique balance between enquiry-based learning and more traditional approaches to teaching | Introductory Analysis An Inquiry Approach

GBP 44.99
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Learn R As a Language

Learn R As a Language

Learning a computer language like R can be either frustrating fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward on overcoming them. This book is designed so that it includes smaller and bigger challenges in what I call playgrounds in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. Although rarely mentioned separately fluency in a computer programming language involves both writing and reading. The parallels between natural and computer languages are many but differences are also important. For students and professionals in the biological sciences humanities and many applied fields recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2020 and beyond. Features R as it is currently used Few prescriptive rules—mostly the author’s preferences together with alternatives Explanation of the R grammar emphasizing the R way of doing things Tutoring for programming in the small using scripts The grammar of graphics and the grammar of data described as grammars Examples of data exchange between R and the foreign world using common file formats Coaching for becoming an independent R user capable of both writing original code and solving future challenges What makes this book different from others: Tries to break the ice and help readers from all disciplines feel at home with R Does not make assumptions about what the reader will use R for Attempts to do only one thing well: guide readers into becoming fluent in the R language Pedro J. Aphalo is a PhD graduate from the University of Edinburgh and is currently a lecturer at the University of Helsinki. A plant biologist and agriculture scientist with a passion for data electronics computers and photography in addition to plants Dr. Aphalo has been a user of R for 25 years. He first organized an R course for MSc students 18 years ago and is the author of 13 R packages currently in CRAN. | Learn R As a Language

GBP 56.99
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Applied Surrogate Endpoint Evaluation Methods with SAS and R

Applied Surrogate Endpoint Evaluation Methods with SAS and R

An important factor that affects the duration complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice using SAS and R. | Applied Surrogate Endpoint Evaluation Methods with SAS and R

GBP 44.99
1

Metamodeling for Variable Annuities

Metamodeling for Variable Annuities

This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students graduate students and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end the book will not only describe the theory of these mathematical approaches but also present the implementations. | Metamodeling for Variable Annuities

GBP 44.99
1

Algebraic Number Theory A Brief Introduction

Algebraic Number Theory A Brief Introduction

This book offers the basics of algebraic number theory for students and others who need an introduction and do not have the time to wade through the voluminous textbooks available. It is suitable for an independent study or as a textbook for a first course on the topic. The author presents the topic here by first offering a brief introduction to number theory and a review of the prerequisite material then presents the basic theory of algebraic numbers. The treatment of the subject is classical but the newer approach discussed at the end provides a broader theory to include the arithmetic of algebraic curves over finite fields and even suggests a theory for studying higher dimensional varieties over finite fields. It leads naturally to the Weil conjecture and some delicate questions in algebraic geometry. About the Author Dr. J. S. Chahal is a professor of mathematics at Brigham Young University. He received his Ph. D. from Johns Hopkins University and after spending a couple of years at the University of Wisconsin as a post doc he joined Brigham Young University as an assistant professor and has been there ever since. He specializes and has published several papers in number theory. For hobbies he likes to travel and hike. His book Fundamentals of Linear Algebra is also published by CRC Press. | Algebraic Number Theory A Brief Introduction

GBP 52.99
1

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
1

Mathematics of The Big Four Casino Table Games Blackjack Baccarat Craps & Roulette

Mathematics of The Big Four Casino Table Games Blackjack Baccarat Craps & Roulette

Mathematics is the basis of casino games which are the bedrock of a $100 billion/year industry. Mathematics of the Big Four Casino Table Games: Blackjack Baccarat Craps & Roulette takes an in-depth look at the four biggest table games in casinos: blackjack baccarat craps and roulette. It guides readers through the mathematical principles that underpin these games and their different variations providing insights that will be of huge interest to gamblers casino managers researchers and students of mathematics. Features A valuable teaching resource replete with exercises for any course on gambling mathematics Suitable for a wide audience of professionals researchers and students Many practical applications for the gambling industry Mark Bollman is Professor of Mathematics and chair of the Department of Mathematics & Computer Science at Albion College in Albion Michigan and has taught 116 different courses in his career. Among these courses is Mathematics of the Gaming Industry where mathematics majors carefully study the math behind games of chance and travel to Las Vegas Nevada in order to compare theory and practice. He has also taken those ideas into Albion’s Honors Program in Great Issues in Humanities: Perspectives on Gambling which considers gambling from literary philosophical and historical points of view as well as mathematically. Mark has also authored Basic Gambling Mathematics: The Numbers Behind the Neon Mathematics of Keno and Lotteries and Mathematics of Casino Carnival Games. | Mathematics of The Big Four Casino Table Games Blackjack Baccarat Craps & Roulette

GBP 28.99
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A First Course in Functional Analysis

A First Course in Functional Analysis

Written as a textbook A First Course in Functional Analysis is an introduction to basic functional analysis and operator theory with an emphasis on Hilbert space methods. The aim of this book is to introduce the basic notions of functional analysis and operator theory without requiring the student to have taken a course in measure theory as a prerequisite. It is written and structured the way a course would be designed with an emphasis on clarity and logical development alongside real applications in analysis. The background required for a student taking this course is minimal; basic linear algebra calculus up to Riemann integration and some acquaintance with topological and metric spaces.

GBP 46.99
1

Fundamentals of Causal Inference With R

Fundamentals of Causal Inference With R

Overall this textbook is a perfect guide for interested researchers and students who wish to understand the rationale and methods of causal inference. Each chapter provides an R implementation of the introduced causal concepts and models and concludes with appropriate exercises. An-Shun Tai & Sheng-Hsuan Lin in BiometricsOne of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models including standardization difference-in-differences estimation the front-door method instrumental variables estimation and propensity score methods. It also covers effect-measure modification precision variables mediation analyses and time-dependent confounding. Several real data examples simulation studies and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability regression and R and is suitable for seniors or graduate students in statistics biostatistics and data science as well as PhD students in a wide variety of other disciplines including epidemiology pharmacy the health sciences education and the social economic and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required but a willingness to tackle mathematical notation difficult concepts and intricate logical arguments is essential. While many real data examples are included the book also features the Double What-If Study based on simulated data with known causal mechanisms in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets R code and solutions to odd-numbered exercises are available on the book's website at www. routledge. com/9780367705053. Instructors can also find slides based on the book and a full solutions manual under 'Instructor Resources'. | Fundamentals of Causal Inference With R

GBP 56.99
1

Mathematics and Statistics for the Quantitative Sciences

Mathematics and Statistics for the Quantitative Sciences

Mathematics and Statistics for the Quantitative Sciences was born from a radical reimagining of first-year mathematics. While calculus is often seen as the foundational mathematics required for any scientist this often leads to mathematics being seen as some ultimately useless hoop that needs to be jumped through in order to do what someone really wants to do. This sentiment is everywhere at every level of education. It even shows up in how people stereotype mathematics courses. What this book aims to do therefore is serve as a foundational text in everyday mathematics in a way that is both engaging and practically useful. The book seeks to teach the mathematics needed to start to answer fundamental questions like ‘why’ or ‘how’. Why do we only need to take census data once every few years? How do we determine the optimal dosing of a new pharmaceutical without killing people in the process? Or more generally what does it even mean to be average? Or what does it mean for two things to actually be different? These questions require a different way of thinking — a quantitative intuition that goes beyond rote memorization and equips readers to meet the quantitative challenges inherent in any applied discipline. Features Draws from a diverse range of fields to make the applications as inclusive as possible Would be ideal as a foundational mathematical and statistical textbook for any applied quantitative science course

GBP 74.99
1

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

This book describes in a basic way the most useful and effective iterative solvers and appropriate preconditioning techniques for some of the most important classes of large and sparse linear systems. The solution of large and sparse linear systems is the most time-consuming part for most of the scientific computing simulations. Indeed mathematical models become more and more accurate by including a greater volume of data but this requires the solution of larger and harder algebraic systems. In recent years research has focused on the efficient solution of large sparse and/or structured systems generated by the discretization of numerical models by using iterative solvers. | Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

GBP 150.00
1

Python for Bioinformatics

Python for Bioinformatics

In today's data driven biology programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience Python for Bioinformatics Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of programming-related concepts the book focuses on the easy-to-use yet powerful Python computer language. This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases the Anaconda Python distribution graphical libraries like Bokeh and the use of Github for collaborative development.

GBP 74.99
1