Data science and analysis Books
Taylor & Francis Ltd Social Networks
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£736.25
Taylor & Francis Ltd Qualitative Analysis Practice and Innovation Social Research Today
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£39.99
Taylor & Francis Data Analysis in Sport
Book SynopsisTable of Contents1. Principles of data analysis 2. Analysis facilities of commercial packages 3. Microsoft Excel 4. Visualisation 5. Statistical windows 6. Player tracking data 7. Matlab 8. Statistical analysis 9. Reliability
£45.59
Taylor & Francis Sports Analytics
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£128.25
Taylor & Francis Ltd Designing and Using Organizational Surveys
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£114.00
Taylor & Francis Ltd Preventing Workplace Incidents in Construction
Book SynopsisThe construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents. The unacceptably high rates of incidents in construction have huge socio-economic consequences for the victims, their families and friends, co-workers, employers and society at large. Construction safety researchers have introduced numerous strategies, models and tools through scientific inquiries involving primary data collection and analyses. While these efforts are commendable, there is a huge potential to create new knowledge and predictive models to improve construction safety by utilising already existing data about workplace incidents. In this new book, Imriyas Kamardeen argues that more sophisticated approaches need to be deployed to enable improved analyses of incident data sets and the extraction of more valuable insights, patterns and knowledge to prevent work injuries and illnesses.The book aims to apply data mining and analytic tecTable of ContentsPreface iAcknowledgements iiFigures iiiTables vAbbreviations vi Introduction Curtailing construction fatalities using analytics Reducing uncertainties in compensation for occupational diseases in construction using analytics Curbing psychological injuries in construction using analytics Predicting and preventing secondary psychological injuries in construction using analytics Conclusion Index
£147.25
Cambridge University Press Quantitative Models in Marketing Research
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£31.34
Cambridge University Press A Guide to Experimental Algorithmics
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£44.64
Cambridge University Press Analysis of Variance and Covariance How to Choose and Construct Models for the Life Sciences
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£85.50
Cambridge University Press Applied Longitudinal Data Analysis for Medical Science
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£95.00
Cambridge University Press InsightDriven Problem Solving
£76.50
Cambridge University Press Inverse Problems and Data Assimilation
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£66.50
Cambridge University Press Designing Empirical Social Networks Research
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£66.50
Cambridge University Press A Prolegomenon to Differential Equations and Variational Methods on Graphs
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£47.49
Cambridge University Press Ceramic Analysis
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£52.25
Cambridge University Press Data Analysis for Business Economics and Policy
Book SynopsisEquips future data analysts with the skills they need to answer questions in business, economics, and public policy. Covering methods of exploratory, predictive, and causal analysis, it includes case studies that use real-world data and related data exercises supported by code (Stata, R, Python) and data available online.Trade Review'This exciting new text covers everything today's aspiring data scientist needs to know, managing to be comprehensive as well as accessible. Like a good confidence interval, the Gabors have got you almost completely covered!' Joshua Angrist, Massachusetts Institute of Technology, winner of the Nobel Memorial Prize in Economic Sciences'This is an excellent book for students learning the art of modern data analytics. It combines the latest techniques with practical applications, replicating the implementation side of classroom teaching that is typically missing in textbooks. For example, they used the World Management Survey data to generate exercises on firm performance for students to gain experience in handling real data, with all its quirks, problems, and issues. For students looking to learn data analysis from one textbook, this is a great way to proceed.' Nicholas Bloom, Stanford University'I know of few books about data analysis and visualization that are as comprehensive, deep, practical, and current as this one; and I know of almost none that are as fun to read. Gábor Békés and Gábor Kézdi have created a most unusual and most compelling beast: a textbook that teaches you the subject matter well and that, at the same time, you can enjoy reading cover to cover.' Alberto Cairo, University of Miami'A beautiful integration of econometrics and data science that provides a direct path from data collection and exploratory analysis to conventional regression modeling, then on to prediction and causal modeling. Exactly what is needed to equip the next generation of students with the tools and insights from the two fields.' David Card, University of California, Berkeley, winner of the Nobel Memorial Prize in Economic Sciences'This textbook is excellent at dissecting and explaining the underlying process of data analysis. Békés and Kézdi have masterfully woven into their instruction a comprehensive range of case studies. The result is a rigorous textbook grounded in real-world learning, at once accessible and engaging to novice scholars and advanced practitioners alike. I have every confidence it will be valued by future generations.' Kerwin K. Charles, Yale School of Management'This book takes you by the hand in a journey that will bring you to understand the core value of data in the fields of machine learning and economics. The large amount of accessible examples combined with the intuitive explanation of foundational concepts is an ideal mix for anyone who wants to do data analysis. It is highly recommended to anyone interested in the new way in which data will be analyzed in the social sciences in the next years.' Christian Fons-Rosen, Barcelona Graduate School of Economics'This sophisticatedly simple book is ideal for undergraduate- or Master's-level Data Analytics courses with a broad audience. The authors discuss the key aspects of examining data, regression analysis, prediction, Lasso, and random forests, and more, with using elegant prose instead of algebra. Using well-chosen case studies, they illustrate the techniques and discuss all of them patiently and thoroughly.' Carter Hill, Louisiana State University'This is not an econometrics textbook. It is a data analysis textbook. And a highly unusual one - written in plain English, based on simplified notation, and full of case studies. An excellent starting point for future data analysts or anyone interested in finding out what data can tell us.' Beata Javorcik, University of Oxford'A multifaceted book that considers many sides of data analysis, all of them important for the contemporary student and practitioner. It brings together classical statistics, regression, and causal inference, sending the message that awareness of all three aspects is important for success in this field. Many 'best practices' are discussed in accessible language, and illustrated using interesting datasets.' llya Ryzhov, University of Maryland'This is a fantastic book to have. Strong data skills are critical for modern business and economic research, and this text provides a thorough and practical guide to acquiring them. Highly recommended.' John van Reenen, MIT Sloan'Energy and climate change is one of the most important public policy challenges, and high- quality data and its empirical analysis is a foundation of solid policy. Data Analysis for Business, Economics, and Policy will make an important contribution to this with its innovative approach. In addition to the comprehensive treatment of modern econometric techniques, the book also covers the less glamorous but crucial aspects of procuring and cleaning data, and drawing useful inferences from less-than-perfect datasets. As the center of gravity of the energy system shifts to developing economies where data quality is still an issue, this will provide an important and practical combination for both academic and policy professionals.' Laszlo Varro, Chief Economist, International Energy AgencyTable of ContentsPart I. Data Exploration: 1. Origins of data; 2. Preparing data for analysis; 3. Exploratory data analysis; 4. Comparison and correlation; 5. Generalizing from data; 6. Testing hypotheses; Part II. Regression Analysis: 7. Simple regression; 8. Complicated patterns and messy data; 9. Generalizing results of a regression; 10. Multiple linear regression; 11. Modeling probabilities; 12. Regression with time series data; Part III. Prediction: 13. A framework for prediction; 14. Model building for prediction; 15. Regression trees; 16. Random forest and boosting; 17. Probability prediction and classification; 18. Forecasting from time series data; Part IV. Causal Analysis: 19. A framework for causal analysis; 20. Designing and analyzing experiments; 21. Regression and matching with observational data; 22. Difference-in-differences; 23. Methods for panel data; 24. Appropriate control groups for panel data; Bibliography; Index.
£133.00
Cambridge University Press The Uncounted
Book SynopsisIn the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author''s experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.Trade Review'Davis vividly shows that not everything that counts can be counted, and not everything that can be counted counts. As an anthropologist, a human rights activist and a former Global Fund official, Davis is an insider and an outsider, drawing a rich, nuanced and compelling portrait of the HIV response today.' Joseph Amon, Director of Global Health, Drexel University, Dornsife School of Public Health'In The Uncounted, Davis has successfully synthesized the complex decisions guiding bilateral and multilateral funding agencies in the HIV response. Given her own experience and that the book is informed by systematic reviews and key informant interviews, it is accurate while managing to provide a humanized narrative to international development.' Stefan Baral, Director of the Key Populations Program at the Center for Public Health and Human Rights'[The Uncounted] pushes those in global health governance to reflect on how data are selected and examined … offering not a neatly packaged set of solutions, but instead an inclusive opportunity to contest and remake data to be people-centered.' Hanna Huffstetler and Benjamin Mason Meier, Global Public Health'Davis provides a highly readable account of not only what these messy realities look like but, crucially, how tools of data governance work.' Sophie Harman, International Affairs'… must-read book for those academics and activists willing to get their head around the system of AIDS knowledge as well as the social and semantic spaces and new sets of relations that it sets in motion.' Julie Billaud, PoLAR: Political and Legal Anthropology Review'The book is wide in scope and deep in breadth … The Uncounted offers an important window into AIDS governance, opening up ways to study power and the construction of regimes of truth that shape our contemporary world. It should become a must-read book for those academics and activists willing to get their head around the system of AIDS knowledge as well as the social and semantic spaces and new sets of relations that it sets in motion.' Julie Billaud, PoLAR OnlineTable of Contents1. Contested indicators; 2. The uncounted: Key populations; 3. "Something more than data”; 4. Cost-effectiveness and human rights; 5. Modeling the end of AIDS; 6. Sustainability, transition and crisis; 7. Listening to women; 8. "So many hurdles just to leave the house"; 9. The Panopticon and the Potemkin; 10. Data from the ground up.
£31.90
Abrams Invisible Women
Book Synopsis
£27.00
Manning Publications Practical Data Science with R
Book SynopsisThis 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.
£999.99
ISTE Ltd and John Wiley & Sons Inc Data Analysis
Book SynopsisThe first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.Trade Review"The first part of this book is devoted to methods seeking relevantdimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data." (Zentralblatt MATH 2016)Table of ContentsPreface xiii Chapter 1. Principal Component Analysis: Application to Statistical Process Control 1 Gilbert SAPORTA, Ndèye NIANG 1.1. Introduction 1 1.2. Data table and related subspaces 2 1.3. Principal component analysis 8 1.4. Interpretation of PCA results 11 1.5. Application to statistical process control 18 1.6. Conclusion 22 1.7. Bibliography 23 Chapter 2. Correspondence Analysis: Extensions and Applications to the Statistical Analysis of Sensory Data 25 Jérôme PAGÈS 2.1. Correspondence analysis 25 2.2. Multiple correspondence analysis 39 2.3. An example of application at the crossroads of CA and MCA 50 2.4. Conclusion: two other extensions 63 2.5. Bibliography 64 Chapter 3. Exploratory Projection Pursuit 67 Henri CAUSSINUS, Anne RUIZ-GAZEN 3.1. Introduction 67 3.2. General principles 68 3.3. Some indexes of interest: presentation and use 71 3.4. Generalized principal component analysis 76 3.5. Example 81 3.6. Further topics 86 3.7. Bibliography 89 Chapter 4. The Analysis of Proximity Data 93 Gerard D’AUBIGNY 4.1. Introduction 93 4.2. Representation of proximity data in a metric space 97 4.3. Isometric embedding and projection 103 4.4. Multidimensional scaling and approximation 108 4.5. Afielded application 122 4.6. Bibliography 139 Chapter 5. Statistical Modeling of Functional Data 149 Philippe BESSE, Hervé CARDOT 5.1. Introduction 149 5.2. Functional framework152 5.3. Principal components analysis 156 5.4. Linear regression models and extensions 161 5.5. Forecasting 169 5.6. Concluding remarks 176 5.7. Bibliography 177 Chapter 6. Discriminant Analysis 181 Gilles CELEUX 6.1. Introduction 181 6.2. Main steps in supervised classification 182 6.3. Standard methods in supervised classification 190 6.4. Recent advances 204 6.5. Conclusion 211 6.6. Bibliography 212 Chapter 7. Cluster Analysis 215 Mohamed NADIF, Gérard GOVAERT 7.1. Introduction 215 7.2. General principles 217 7.3. Hierarchical clustering 224 7.4. Partitional clustering: the k-means algorithm 233 7.5. Miscellaneous clustering methods 239 7.6. Block clustering 245 7.7. Conclusion 251 7.8. Bibliography 251 Chapter 8. Clustering and the Mixture Model 257 Gérard GOVAERT 8.1. Probabilistic approaches in cluster analysis 257 8.2. The mixture model 261 8.3. EM algorithm 263 8.4. Clustering and the mixture model 267 8.5.Gaussian mixture model 271 8.6. Binary variables 275 8.7. Qualitative variables 279 8.8. Implementation 282 8.9. Conclusion 284 8.10. Bibliography 284 Chapter 9. Spatial Data Clustering 289 Christophe AMBROISE, Mo DANG 9.1. Introduction 289 9.2. Non-probabilistic approaches 293 9.3. Markov random fields as models 295 9.4. Estimating the parameters for a Markov field 305 9.5. Application to numerical ecology 313 9.6. Bibliography 316 List of Authors 319 Index 323
£145.30
