This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an "Introduction to Data Science" course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains "War Stories," offering perspectives on how data science applies in the real world Includes "Homework Problems," providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides "Take-Home Lessons," emphasizing the big-picture concepts to learn from each chapter Recommends exciting "Kaggle Challenges" from the online platform Kaggle Highlights "False Starts," revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show "The Quant Shop" (www.quant-shop.com)

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo's modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations--complete with sample code--of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining--including supervised and unsupervised data mining methods--are treated in detail, as are time series forecasting, text mining, and natural language processing. - Features a quick-learning format with concise tutorials and actionable analytics - Provides command-by-command translations of R to Python and vice versa - Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages - Offers numerous comparative examples and applications in both programming languages - Designed for use for practitioners and students that know one language and want to learn the other - Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first book to explore GMDH using MATLAB (matrix laboratory) language. Readers will learn how to implement GMDH in MATLAB as a method of dealing with big data analytics. Error-free source codes in MATLAB have been included in supplementary material (accessible online) to assist users in their understanding in GMDH and to make it easy for users to further develop variations of GMDH algorithms.

This set contains: 9780471221746 SAS for Linear Models, Fourth Edition by Ramon Littell, Walter W. Stroup, Rudolf Freund and 9780471754985 Linear Models in Statistics, Second Edition by Alvin C. Rencher, G. Bruce Shaalje.

A sequel to Understanding MATLAB: A Textbook for Beginners, this book is an advanced-level guide to MATLAB. Included are a number of solved problems from all important areas of science and engineering. MATLAB integrates computation, visualization and programming in a very user-friendly and easy-to use environment. This book is aimed at those who already know the basics of the language and would like to use MATLAB to solve advanced engineering problems. Key Features: Provides discussion of the history and evolution of MATLAB Discusses the MATLAB compiler and how it works Includes detailed treatment of numbers, matrices and matrix operations

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: * Addresses the theoretical aspects of the field * Advises on how to prepare and perform phylogenetic analysis * Helps with interpreting analyses and visualisation of phylogenies * Describes the software architecture * Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users - from those using phylogenetic tools, to computational biologists and Bayesian statisticians.

The Survivor's Guide to R provides a gentle, but thorough, introduction to R. It is an ideal supplement to any introductory statistics text or a practical field guide for those who want to use the powerful R language for statistical analysis in their own research. The book focuses on providing students with the real-world R skills that are often hard to get to in statistics classes: basic data management and manipulation, and working with R graphics. The book is designed to get students with little or no background in statistics or programming started on R within the context of a statistics class, and to ensure that they have acquired functional R skills that they can continue to use as they move on to their own projects. The book begins with a straightforward approach to understanding R objects, and then moves systematically through the use of R to transform, sort, and aggregate data; to work with complex textual and date/time data; and to effectively build on R's default graphics capabilities to produce highly customized and effective graphics. It focuses on working with real-world data, with - on reading data in different formats and the challenges of missing data. This book is intended for those with little to no statistics or programming experience---students and other new users who are likely to find their first encounter with R more than a little intimidating. It is written in an accessible and sympathetic style that makes minimal assumptions about user skills, and provides frequent warnings about common pitfalls that must be avoided along the road to R mastery.

Biological Inquiry needs knowledge of statistical techniques, mathematical competence and support of computer programmes. This text book has been written in a simple and easy to understand language and presents a broad collection of data analysis techniques. Important features of the book include: Worked out example Exercise at the end of each chapter University question A Chapter on Mathematics The text book serves the need of different Biology courses, Agricultural sciences, Medicine, Pharmacy, Biotechnology, Genetics, Home science and Nutrition.

The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Long presents lessons gained from his experience and demonstrates how to design and implement efficient workflows for both one-person projects and team projects. After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. After a discussion of conventions that greatly simplify data analysis the author covers cleaning, analyzing, and protecting data.

This book is aimed at students and professionals who are trying to learn MATLAB through self-study. In a step-by-step process, the book tries to teach readers how to write MATLAB programmes in order to solve problems. The book will familiarise the reader with a number of inbuilt functions and features of MATLAB with specific illustrations in the areas of science and technology. MATLAB is a high level computing language, which can be used in a wide range of areas. A large number of examples are included. Plots, matrix calculations, vectors, loops, functions, solving linear equations, integration, differentiation, ordinary differential equations, curve fitting, image processing, and animation are all dealt with.

This book is aimed at undergraduate students, users of MATLAB programmes, and those who want to be in pace with developing technology. As C is used in a variety of areas from software development to hardware programming, including embedded systems and driver writing or system programming, it is essential to have knowledge of this particular language. This book will meets the needs of a range of programmers, as well as MATLAB users in various fields, both in academia and scientific research. This book is made up of two sections - C11 and MATLAB. The C11 section begins with an explanation of programme structure, before moving on to programme development using the various basic building materials of the language. The MATLAB section offers comprehensive coverage of MATLAB so that readers can use this language for research purposes.

Learn how to use PTC(r) Mathcad Prime(r) 3.0, one of the world s leading tools for technical computing, in the context of engineering, science, and math applications. Quickly harness the power of PTC Mathcad Prime 3.0 to solve both simple and complex problems. "Essential PTC(r) Mathcad Prime(r) 3.0" is perfect for college students, first-time users, and experienced Mathcad 15 users who are moving to PTC Mathcad Prime 3.0.

Updated from Maxfield s popular "Essential Mathcad," this book introduces the most powerful functions and features of the new PTC Mathcad Prime 3.0 software and teaches how to apply them to create comprehensive calculations for any quantitative subject. Examples from several fields demonstrate the power and utility of PTC Mathcad s tools while also demonstrating how users can eff ectively incorporate Microsoft(r) Excel spreadsheets into the software.
Learn the basics faster: Chapter 1 introduces many fundamentals of Mathcad, allowing the reader to begin using the program in less time.Learn PTC Mathcad tools in context: Incorporates many applied examples and problems from a wide
variety of disciplines.Thorough discussion of many PTC Mathcad tools: Units, arrays, plotting, solving, symbolic calculations, programming, algebra, calculus, differential equations, reading from files, writing to files, and incorporating MS Excel spreadsheets.Each copy of the book includes a code to download PTC(r) Mathcad Prime(r) 3.0 Student Edition, giving
the user a full year of use from the time of installation. (The Student Edition software is intended for educational purposes only.)

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A practical, professional guide to MATLABcomputational techniques and engineering applicationsMATLAB Numerical Methods with Chemical Engineering Applications shows you, step by step, how to use MATLAB (R) to model and simulate physical problems in the chemical engineering realm. Written for MATLAB 7.11, this hands-on resource contains concise explanations of essential MATLAB commands, as well as easy-to-follow instructions for using the programming features, graphical capabilities, and desktop interface. Every step needed toward the final solution is algorithmically explained via snapshots of the MATLAB platform in parallel with the text. End-of-chapterproblems help you practice what you've learned. Master this powerful computational tool using this detailed, self-teaching guide. COVERAGE INCLUDES: MATLAB basics Matrices MATLAB scripting language: M-file Image and image analysis Curve-fitting Numerical integration Solving differential equations A system of algebraic equations Statistics Chemical engineering applications MATLAB Graphical User Interface Design Environment (GUIDE)

PAMIR (Parameterized Adaptive Multidimensional Integration Routines) is a suite of Fortran programs for multidimensional numerical integration over hypercubes, simplexes, and hyper-rectangles in general dimension p, intended for use by physicists, applied mathematicians, computer scientists, and engineers. The programs, which are available on the internet at www.pamir-integrate.com and are free for non-profit research use, are capable of following localized peaks and valleys of the integrand. Each program comes with a Message-Passing Interface (MPI) parallel version for cluster use as well as serial versions.The first chapter presents introductory material, similar to that on the PAMIR website, and the next is a "manual" giving much more detail on the use of the programs than is on the website. They are followed by many examples of performance benchmarks and comparisons with other programs, and a discussion of the computational integration aspects of PAMIR, in comparison with other methods in the literature. The final chapter provides details of the construction of the algorithms, while the Appendices give technical details and certain mathematical derivations.

PAMIR (Parameterized Adaptive Multidimensional Integration Routines) is a suite of Fortran programs for multidimensional numerical integration over hypercubes, simplexes, and hyper-rectangles in general dimension p, intended for use by physicists, applied mathematicians, computer scientists, and engineers. The programs, which are available on the internet at www.pamir-integrate.com and are free for non-profit research use, are capable of following localized peaks and valleys of the integrand. Each program comes with a Message-Passing Interface (MPI) parallel version for cluster use as well as serial versions.The first chapter presents introductory material, similar to that on the PAMIR website, and the next is a "manual" giving much more detail on the use of the programs than is on the website. They are followed by many examples of performance benchmarks and comparisons with other programs, and a discussion of the computational integration aspects of PAMIR, in comparison with other methods in the literature. The final chapter provides details of the construction of the algorithms, while the Appendices give technical details and certain mathematical derivations.

Six Sigma statistical methodology using Minitab

"Problem Solving and Data Analysis using Minitab "presents example-based learning to aid readers in understanding how to use MINITAB 16 for statistical analysis and problem solving. Each example and exercise is broken down into the exact steps that must be followed in order to take the reader through key learning points and work through complex analyses. Exercises are featured at the end of each example so that the reader can be assured that they have understood the key learning points.

"Key features: "Provides readers with a step by step guide to problem solving and statistical analysis using Minitab 16 which is also compatible with version 15.Includes fully worked examples with graphics showing menu selections and Minitab outputs.Uses example based learning that the reader can work through at their pace.Contains hundreds of screenshots to aid the reader, along with explanations of the statistics being performed and interpretation of results.Presents the core statistical techniques used by Six Sigma Black Belts. Contains examples, exercises and solutions throughout, and is supported by an accompanying website featuring the numerous example data sets.

Making Six Sigma statistical methodology accessible to beginners, this book is aimed at numerical professionals, students or academics who wish to learn and apply statistical techniques for problem solving, process improvement or data analysis whilst keeping mathematical theory to a minimum.

Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms. Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity. It uses the open-source and now widely used R software for statistical computing to create a tutorial-style manual for independent study, review, and reference. The text is written around the code that when copied will reproduce the graphs, tables, and maps. The approach is different from other books that use R. It focuses on a single topic and explains how to make use of R to better understand the topic. The book is organized into two parts, the first of which provides material on software, statistics, and data. The second part presents methods and models used in hurricane climate research.

A comprehensive manual on the efficient modeling and analysis of photonic devices through building numerical codes, this book provides graduate students and researchers with the theoretical background and MATLAB programs necessary for them to start their own numerical experiments. Beginning by summarizing topics in optics and electromagnetism, the book discusses optical planar waveguides, linear optical fiber, the propagation of linear pulses, laser diodes, optical amplifiers, optical receivers, finite-difference time-domain method, beam propagation method and some wavelength division devices, solitons, solar cells and metamaterials. Assuming only a basic knowledge of physics and numerical methods, the book is ideal for engineers, physicists and practising scientists. It concentrates on the operating principles of optical devices, as well as the models and numerical methods used to describe them.

Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to provide simple instructions for using R. This concise book: *Teaches essential concepts in statistics, assuming little background knowledge on the part of the reader *Introduces students to R with as few sub-commands as possible for ease of use *Provides practical examples from the educational, behavioral, and social sciences With clear explanations of statistical processes and step-by-step commands in R, Basic Statistics will appeal to students and professionals across the social and behavioral sciences.

Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to provide simple instructions for using R. This concise book: .Teaches essential concepts in statistics, assuming little background knowledge on the part of the reader .Introduces students to R with as few sub-commands as possible for ease of use .Provides practical examples from the educational, behavioral, and social sciences With clear explanations of statistical processes and step-by-step commands in R, Basic Statistics will appeal to students and professionals across the social and behavioral sciences.

Christine Duller (Universitat Linz) gibt in diesem Buch eine leicht verstandliche Einfuhrung in die nichtparametrische Statistik, insbesondere fur Studierende der Wirtschafts- und Sozialwissenschaften und alle interessierten Leserinnen und Leser, die (nur) uber Grundkenntnisse der Statistik verfugen. Dabei beschreibt sie nicht nur die statistischen Verfahren, sondern setzt diese auch in SAS und R um. In beide Programmiersprachen fuhrt die Autorin kurz ein, sodass keine Vorkenntnisse notwendig sind. Das Buch eignet sich zum Selbststudium und ist auch als Nachschlagewerk fur einfache statistische Analysen geeignet. Zahlreiche Beispiele mit Loesungen erganzen die Darstellung.

Bridging an understanding of Statistics and SPSS. "The text is written in a user-friendly language and illustrates concepts that would otherwise be confusing to beginning students and those with limited computer skills." -Justice Mbizo, University of West Florida This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students' understanding of theoretical concepts by teaching them in a practical context.

This is the first book of its kind which teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R. Instead of a calculator, R is a vastly more powerful free software and graphics system.

The book provides a comprehensive overview of matrix theory without being bogged down in proofs or tedium. The reader can check each matrix result with numerical examples of exactly what they mean and understand their implications. The book does not shy away from advanced topics, especially the ones with practical applications.