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Hands-On Data Science for Librarians

Hands-On Data Science for Librarians

Librarians understand the need to store use and analyze data related to their collection patrons and institution and there has been consistent interest over the last 10 years to improve data management analysis and visualization skills within the profession. However librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping working with maps creating interactive reports machine learning and others. While there’s a place for theory ethics and statistical methods librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work no matter what type of library they work at (academic public or special). By walking through each skill and its application to library work before walking the reader through each line of code this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public academic or special) as well as graduate students in library and information science (LIS). Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public academic special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications

GBP 52.99
1

Python for Beginners

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 tools Offers practical advice for each major area of development with both Python 3. x and Python 2. x Based on the latest research in cognitive science and learning theory Helps the reader learn how to write effective idiomatic Python code by leveraging its best—and possibly most neglected—features This 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.

GBP 120.00
1

C++ for Financial Mathematics

Python for Bioinformatics

Geometry for the Artist

Metamodeling for Variable Annuities

Artificial Intelligence for Autonomous Networks

Numerical Methods for Finance

Statistical Reasoning for Surgeons

Mathematics for Engineers and Scientists

Image Processing for Cinema

Algorithms for Next-Generation Sequencing

Introductory Concepts for Abstract Mathematics

AI for Cars

Graphical Methods for Data Analysis

Software Engineering for Science

Software Engineering for Science

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software including not only computational modeling but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains. About the EditorsJeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He is one of the primary organizers of the workshop series on Software Engineering for Science (http://www. SE4Science. org/workshops). Neil P. Chue Hong is Director of the Software Sustainability Institute at the University of Edinburgh. His research interests include barriers and incentives in research software ecosystems and the role of software as a research object. George K. Thiruvathukal is Professor of Computer Science at Loyola University Chicago and Visiting Faculty at Argonne National Laboratory. His current research is focused on software metrics in open source mathematical and scientific software.

GBP 44.99
1

Introduction to Python for Humanists

Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown providing students with a strong practical foundation in statistics. Using a color format throughout the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences including medicine nursing pharmacy dentistry and physical therapy but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program. The book begins by explaining the research context for statistics in the health sciences which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout giving a substantive context for descriptive statistics graphs probability inferential statistics and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios. Web ResourceThe authors have partnered with William Howard Beasley who created the illustrations in the book to offer all of the data sets graphs and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors’ electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.

GBP 44.99
1

Mixed Effects Models for Complex Data

Mixed Effects Models for Complex Data

Although standard mixed effects models are useful in a range of studies other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts missing data measurement errors censoring and outliers. For each class of mixed effects model the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data the book introduces linear mixed effects (LME) models generalized linear mixed models (GLMMs) nonlinear mixed effects (NLME) models and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values measurement errors censoring and outliers. Self-contained coverage of specific topicsSubsequent chapters delve more deeply into missing data problems covariate measurement errors and censored responses in mixed effects models. Focusing on incomplete data the book also covers survival and frailty models joint models of survival and longitudinal data robust methods for mixed effects models marginal generalized estimating equation (GEE) models for longitudinal or clustered data and Bayesian methods for mixed effects models. Background materialIn the appendix the author provides background information such as likelihood theory the Gibbs sampler rejection and importance sampling methods numerical integration methods optimization methods bootstrap and matrix algebra. Failure to properly address missing data measurement errors and other issues in statistical analyses can lead

GBP 59.99
1

Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists

Praise for the Second Edition: The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course. Computing Reviews Probability and Statistics for Computer Scientists Third Edition helps students understand fundamental concepts of Probability and Statistics general methods of stochastic modeling simulation queuing and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis including estimation and testing Bayesian approach multivariate regression chi-square tests for independence and goodness of fit nonparametric statistics and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science software engineering telecommunications and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability the text takes students through topics heavily featured in modern computer science computer engineering software engineering and associated fields such as computer simulations Monte Carlo methods stochastic processes Markov chains queuing theory statistical inference and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping change-point detection Bayesian inference and applications of statistics in epidemiology clinical trials semiconductor manufacturing and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph. D. in statistics from the University of Maryland. In his turn he supervised twelve doctoral students mostly employed on academic and research positions.

GBP 99.99
1

Data Science for Wind Energy

Statistics for Finance

Innovative Methods for Rare Disease Drug Development

Innovative Methods for Rare Disease Drug Development

In the United States a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200 000 persons. For the approval of orphan drug products for rare diseases the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development focuses on biostatistical applications in terms of design and analysis in pharmaceutical research and development from both regulatory and scientific (statistical) perspectives. Key Features: Reviews critical issues (e. g. endpoint/margin selection sample size requirements and complex innovative design). Provides better understanding of statistical concepts and methods which may be used in regulatory review and approval. Clarifies controversial statistical issues in regulatory review and approval accurately and reliably. Makes recommendations to evaluate rare diseases regulatory submissions. Proposes innovative study designs and statistical methods for rare diseases drug development including n-of-1 trial design adaptive trial design and master protocols like platform trials. Provides insight regarding current regulatory guidance on rare diseases drug development like gene therapy.

GBP 44.99
1

Introductory Mathematical Analysis for Quantitative Finance

Numerical Methods for Engineers

Numerical Methods for Engineers

Although pseudocodes Mathematica® and MATLAB® illustrate how algorithms work designers of engineering systems write the vast majority of large computer programs in the Fortran language. Using Fortran 95 to solve a range of practical engineering problems Numerical Methods for Engineers Second Edition provides an introduction to numerical methods incorporating theory with concrete computing exercises and programmed examples of the techniques presented. Covering a wide range of numerical applications that have immediate relevancy for engineers the book describes forty-nine programs in Fortran 95. Many of the programs discussed use a sub-program library called nm_lib that holds twenty-three subroutines and functions. In addition there is a precision module that controls the precision of calculations. Well-respected in their field the authors discuss a variety of numerical topics related to engineering. Some of the chapter features include…The numerical solution of sets of linear algebraic equationsRoots of single nonlinear equations and sets of nonlinear equations Numerical quadrature or numerical evaluation of integralsAn introduction to the solution of partial differential equations using finite difference and finite element approachesDescribing concise programs that are constructed using sub-programs wherever possible this book presents many different contexts of numerical analysis forming an excellent introduction to more comprehensive subroutine libraries such as the numerical algorithm group (NAG).

GBP 59.99
1