Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.

Data is constantly increasing and data analysts are in higher demand than ever. This book is an essential guide to the role of data analyst. Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to. Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.

Data Analysis in Criminal Justice and Criminology: History, Concept, and Application breaks down various data analysis techniques to help students build their conceptual understanding of key methods and processes. The information in the text encourages discussion and consideration of how and why data analysis plays an important role in the fields of criminal justice and criminology. The book is divided into three units. Unit 1 discusses how data analysis is used in criminal justice and criminology, various methods of data collection, the importance of identifying the purpose of analysis and key data elements prior to analyzing information, and graphical representation of data. Unit 2 introduces students to samples, distributions, and the central limit theorem as it relates to data analysis. This section provides students with the essential knowledge and skills needed to understand statistical concepts and calculations. The final unit explains how to move beyond statistical description to statistical inference and how sample statistics can be used to estimate population parameters. Highly accessible in nature, Data Analysis in Criminal Justice and Criminology is ideal for undergraduate and graduate courses in criminal justice, criminology, and sociology especially those with emphasis on data analysis.

This book integrates philosophy of science, data acquisition methods, and statistical modeling techniques to present readers with a forward-thinking perspective on clinical science. It reviews modern research practices in clinical psychology that support the goals of psychological science, study designs that promote good research, and quantitative methods that can test specific scientific questions. It covers new themes in research including intensive longitudinal designs, neurobiology, developmental psychopathology, and advanced computational methods such as machine learning. Core chapters examine significant statistical topics, for example missing data, causality, meta-analysis, latent variable analysis, and dyadic data analysis. A balanced overview of observational and experimental designs is also supplied, including preclinical research and intervention science. This is a foundational resource that supports the methodological training of the current and future generations of clinical psychological scientists.

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART. This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART. The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.

Computers increasingly collect, manage, and analyse data for scholarly research. Linked data gives libraries the ability to support this e-research, making it a powerful tool. Libraries are at a tipping point in adoption of linked data, and this issue of Library Technology Reports explores current research in linked open data, explaining concepts and pioneering services, such as Five building blocks of metadata data model, content rules, metadata schema, data serialisation, and data exchange Three case studies Europeana, Digital Public Library of America, and BIBFRAME How libraries, archives and museums are currently addressing such issues as metadata quality, open data and business models, cross community engagement, and implementation

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This second edition, published for the first time in paperback, provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features * Data science and statistical analysis for the business professional * Numerous instantly familiar real-world use cases * Keys to effective data presentations * Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

A complete and comprehensive collaboration providing insight on future approaches to telephone survey methodology

Over the past fifteen years, advances in technology have transformed the field of survey methodology, from how interviews are conducted to the management and analysis of compiled data. Advances in Telephone Survey Methodology is an all--encompassing and authoritative resource that presents a theoretical, methodological, and statistical treatment of current practices while also establishing a discussion on how state--of--the--art developments in telecommunications have and will continue to revolutionize the telephone survey process.

Seventy--five prominent international researchers and practitioners from government, academic, and private sectors have collaborated on this pioneering volume to discuss basic survey techniques and introduce the future directions of the telephone survey. Concepts and findings are organized in four parts--sampling and estimation, data collection, operations, and nonresponse--equipping the reader with the needed practical applications to approach issues such as choice of target population, sample design, questionnaire construction, interviewing training, and measurement error. The book also introduces important topics that have been overlooked in previous literature, including:

The impact of mobile telephones on telephone surveys and the rising presence of mobile--only households worldwide

The design and construction of questionnaires using Computer Assisted Telephone Interviewing (CATI) software

The emerging use of wireless communication and Voice over Internet Protocol (VoIP) versus the telephone

Methods for measuring andimproving interviewer performance and productivity

Privacy, confidentiality, and respondent burden as main factors in telephone survey nonresponse

Procedures for the adjustment of nonresponse in telephone surveys

In--depth reviews of the literature presented along with a full bibliography, assembled from references throughout the world

Advances in Telephone Survey Methodology is an indispensable reference for survey researchers and practitioners in almost any discipline involving research methods such as sociology, social psychology, survey methodology, and statistics. This book also serves as an excellent text for courses and seminars on survey methods at the undergraduate and graduate levels.

The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide( d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

Many people go through life in a rather hit-or-miss fashion, casting about for ideas to explain why their projects improve or decline, why they are successful or why they are not. Guessing and "hunches," however, are not very reliable. And without the knowledge of how to actually investigate situations, good or bad, and get the true facts, a person is set adrift in a sea of unevaluated data. Accurate investigation is, in fact, a rare commodity. Man's tendency in matters he doesn't understand is to accept the first proffered explanation, no matter how faulty. Thus investigatory technology had not actually been practiced or refined. However, L. Ron Hubbard made a breakthrough in the subject of logic and reasoning which led to his development of the first truly effective way to search for and consistently find the actual causes for things. Knowing how to investigate gives one the power to navigate through the random facts and opinions and emerge with the real reasons behind success or failure in any aspect of life. By really finding out why things are the way they are, one is therefore able to remedy and improve a situation-any situation. This is an invaluable technology for people in all walks of life.

"I am truly amazed at how complete this booklet is. What a wealth of material to help in developing questions!"

Minds Alive explores the enduring role and intrinsic value of libraries, archives, and public institutions in the digital age. Featuring international contributors, this volume delves into libraries and archives as institutions and institutional partners, the professional responsibilities of librarians and archivists, and the ways in which librarians and archivists continue to respond to the networked age, digital culture, and digitization. The endless possibilities and robust importance of libraries and archives are at the heart of this optimistic collection. Topics include transformations in the networked digital age; Indigenous issues and challenges in custodianship, ownership, and access; the importance of the harmonization of memory institutions today; and the overarching significance of libraries and archives in the public sphere. Libraries and archives - at once public institutions providing both communal and private havens of discovery - are being repurposed and transformed in intercultural contexts. Only by keeping pace with users' changing needs can they continue to provide the richest resources for an informed citizenry.

Many librarians' job responsibilities increasingly require them to understand and handle data. Learn how to be an effective data librarian-even if you never expected to need data skills. The field of data librarianship is rapidly growing, and some librarians may feel that their training and experience does not cover data questions asked by patrons seeking advice. With this gentle guide for librarians moving-sometimes unexpectedly-into the world of data librarianship, all you need is a willingness to learn the skills required for the rapidly growing number of jobs requiring data librarianship. Working as a Data Librarian focuses on transferable skills and understanding and does not assume extensive knowledge. It introduces tasks and concepts needed to be an effective data librarian, such as best practices for data reference interviewing, finding data sources, data visualization, data literacy, the data lifecycle, metadata design, database design, understanding data management, and preparing data management plans. Additional sections focus on supporting creativity (Makerspaces and Fablabs, 3-D modeling), supporting analysis (GIS, data visualization, text mining, statistical methods), supporting research (digital scholarship, digital preservation, institutional data repositories, scholarly communication), and outreach (data librarian liaisonship, hackathons, developing outreach programs). Acquaints readers with the fast-growing field of data librarianship Teaches librarians the fundamental skills needed to be a data librarian Offers best practices for data reference interviewing, finding data sources, data visualization, data literacy, the data lifecycle, metadata design, database design, understanding data management, and preparing data management plans Helps librarians to support creativity and research and to run outreach programs

NOW IN FULL COLOR! Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate their data findings. This comprehensive how-to guide functions as a set of blueprints-supported by both research and the author's extensive experience with clients in industries all over the world-for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for building the chosen graph in Excel. Now in full color with new examples throughout, the Second Edition includes a revamped chapter on qualitative data, nine new quantitative graph types, new shortcuts in Excel, and an entirely new chapter on Sharing Your Data With the World, which provides advice on using dashboards. New from Stephanie Evergreen! The Data Visualization Sketchbook provides advice on getting started with sketching and offers tips, guidance, and completed sample sketches for a number of reporting formats. Bundle Effective Data Visualization, 2e, and The Data Visualization Sketchbook, using ISBN 978-1-5443-7178-8!

The vast volume of financial data that exists and the globalisation of financial markets create new challenges for researchers and practitioners in economics and finance. Computational data analysis techniques can contribute significantly within this context, by providing a rigorous analytic framework for decision-making and support, in areas such as financial times series analysis and forecasting, risk assessment, trading, asset management, and pricing. The aim of this edited volume is to present, in a unified context, some recent advances in the field, covering the theory, the methodologies, and the applications of computational data analysis methods in economics and finance. The volume consists of papers published in the fifth volume of the Journal of "Computational Optimization in Economics & Finance" (published by Nova Science Publishers). The contents of this volume cover a wide range of topics, including among others stock market applications, corporate finance, corporate performance, as well as macroeconomic issues.

Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography, pattern recognition for satellite images from remote sensing, management of agriculture and forests, assessment of climate change, and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics are covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning, as well as the recent merging of machine learning and physics. End-of-chapter exercises allow readers to develop their problem-solving skills and online data sets allow readers to practise analysis of real data.

This book provides thorough and comprehensive coverage of most of the new and important quantitative methods of data analysis for graduate students and practitioners. In recent years, data analysis methods have exploded alongside advanced computing power, and it is critical to understand such methods to get the most out of data, and to extract signal from noise. The book excels in explaining difficult concepts through simple explanations and detailed explanatory illustrations. Most unique is the focus on confidence limits for power spectra and their proper interpretation, something rare or completely missing in other books. Likewise, there is a thorough discussion of how to assess uncertainty via use of Expectancy, and the easy to apply and understand Bootstrap method. The book is written so that descriptions of each method are as self-contained as possible. Many examples are presented to clarify interpretations, as are user tips in highlighted boxes.

This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.

Written for anyone beginning a research project, this introductory book takes you through the process of analysing your data from start to finish. The author sets out an easy-to-use model for coding data in order to break it down into parts, and then to reassemble it to create a meaningful picture of the phenomenon under study. Full of useful advice, the book guides the reader through the last difficult integrating phase of qualitative analysis including diagramming, memoing, thinking aloud, and using one's feelings, and how to incorporate the use of software where appropriate. Ideal for third year undergraduate students, master students, postgraduates and anybody beginning a research project, the book includes examples covering a wide range of subjects - making the book useful for students across the social science disciplines. Hennie Boeije is currently an Associate Professor with the Department of Methodology and Statistics of the Faculty of Social and Behavioural Sciences at Utrecht University, The Netherlands.