Handling Data And Measures
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| Author | : Mel Lever |
| Publisher | : Routledge |
| Total Pages | : 79 |
| Release | : 2013-01-11 |
| Genre | : Education |
| ISBN | : 1136635602 |
First Published in 2003. This book offers practical advice to those students of maths who don’t understand it and don’t like it. The author asks ‘So what shall we do about it?’ This is what makes her books so helpful; they give parents and teachers practical ideas they can use. First addressing the question of the types of difficulty encountered, she then moves on to overcoming the difficulty.
| Author | : |
| Publisher | : Heinemann |
| Total Pages | : 60 |
| Release | : 2001-04-05 |
| Genre | : Mathematics |
| ISBN | : 0435174207 |
NHM Organising and Planning Guide is an excellent teacher resource. It gives you all the support you need to implement the programme and plan your lessons.
| Author | : Fred Davidson |
| Publisher | : SAGE Publications, Incorporated |
| Total Pages | : 344 |
| Release | : 1996-04-09 |
| Genre | : Education |
| ISBN | : |
Principles of Statistical Data Handling is designed to help readers understand the principles of data handling so that they can make better use of computer data in research or study.
| Author | : |
| Publisher | : Elsevier |
| Total Pages | : 802 |
| Release | : 2019-09-29 |
| Genre | : Science |
| ISBN | : 0444639780 |
Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields. - Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed - Covers state-of-the-art applications in different scientific fields - Discusses the implementation of hyperspectral devices in different environments
| Author | : United States. Defense Industrial Resources Support Office |
| Publisher | : |
| Total Pages | : 296 |
| Release | : 1977 |
| Genre | : Production management |
| ISBN | : |
This volume of Miscellaneous Occupations Standard Time Data is one of ten volumes of standard time data in the 11 volume series included in DWMSTDP. Miscellaneous Occupations as categorized by the Department of Labor includes those occupations concerned with transportation services (surface, water and air); materials handling, packaging and warehousing; utilities; amusement, recreation and motion picture services; mining and logging; graphic arts; and various other activities. This volume provides a single DoD source for Standard Time Data elements which can be used in the development of labor standards for: 1.1.1 Organizations, activities or functional areas whose primary mission correlates to miscellaneous occupations, e.g., surface, water and air transport and terminal operations; packaging or packing; materials handling, warehousing. 1.1.2 Miscellaneous type operations which are accomplished in organizations, activities or functional areas with primary missions not correlated to Miscellaneous Occupations, e.g., packagers or material handlers assigned to maintenance or machine shops. 1.1.3 Elements of work performed by personnel whose primary job is other than Miscellaneous but who may actually do that type work as part of their job, e.g., a machinist attaching and operating a hoist to a part being machined, (materials handling), a mechanic unpacking a part to be installed, (packaging), or a construction worker handling materials, (materials handling).
| Author | : Chris Albon |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 285 |
| Release | : 2018-03-09 |
| Genre | : Computers |
| ISBN | : 1491989335 |
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models
| Author | : Richard E. Thomson |
| Publisher | : Elsevier |
| Total Pages | : 654 |
| Release | : 2001-04-03 |
| Genre | : Science |
| ISBN | : 0080477003 |
Data Analysis Methods in Physical Oceanography is a practical referenceguide to established and modern data analysis techniques in earth and oceansciences. This second and revised edition is even more comprehensive with numerous updates, and an additional appendix on 'Convolution and Fourier transforms'. Intended for both students and established scientists, the fivemajor chapters of the book cover data acquisition and recording, dataprocessing and presentation, statistical methods and error handling,analysis of spatial data fields, and time series analysis methods. Chapter 5on time series analysis is a book in itself, spanning a wide diversity oftopics from stochastic processes and stationarity, coherence functions,Fourier analysis, tidal harmonic analysis, spectral and cross-spectralanalysis, wavelet and other related methods for processing nonstationarydata series, digital filters, and fractals. The seven appendices includeunit conversions, approximation methods and nondimensional numbers used ingeophysical fluid dynamics, presentations on convolution, statisticalterminology, and distribution functions, and a number of importantstatistical tables. Twenty pages are devoted to references. Featuring:• An in-depth presentation of modern techniques for the analysis of temporal and spatial data sets collected in oceanography, geophysics, and other disciplines in earth and ocean sciences.• A detailed overview of oceanographic instrumentation and sensors - old and new - used to collect oceanographic data.• 7 appendices especially applicable to earth and ocean sciences ranging from conversion of units, through statistical tables, to terminology and non-dimensional parameters. In praise of the first edition: "(...)This is a very practical guide to the various statistical analysis methods used for obtaining information from geophysical data, with particular reference to oceanography(...)The book provides both a text for advanced students of the geophysical sciences and a useful reference volume for researchers." Aslib Book Guide Vol 63, No. 9, 1998 "(...)This is an excellent book that I recommend highly and will definitely use for my own research and teaching." EOS Transactions, D.A. Jay, 1999 "(...)In summary, this book is the most comprehensive and practical source of information on data analysis methods available to the physical oceanographer. The reader gets the benefit of extremely broad coverage and an excellent set of examples drawn from geographical observations." Oceanography, Vol. 12, No. 3, A. Plueddemann, 1999 "(...)Data Analysis Methods in Physical Oceanography is highly recommended for a wide range of readers, from the relative novice to the experienced researcher. It would be appropriate for academic and special libraries." E-Streams, Vol. 2, No. 8, P. Mofjelf, August 1999
| Author | : Max Kuhn |
| Publisher | : CRC Press |
| Total Pages | : 266 |
| Release | : 2019-07-25 |
| Genre | : Business & Economics |
| ISBN | : 1351609467 |
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
| Author | : L. Andries van der Ark |
| Publisher | : Springer Nature |
| Total Pages | : 461 |
| Release | : 2023-03-17 |
| Genre | : Education |
| ISBN | : 303110370X |
This book 'Essays on Contemporary Psychometrics' provides an overview of contemporary psychometrics, the science devoted to the advancement of quantitative measurement practices in psychology, education and the social sciences. The volume consists of four parts, each having several chapters on cutting-edge work in the field. Part I, General Perspectives on Psychometrics, includes expert views on topics such as psychological models vs. measurement models, using tests in decision making, artificial intelligence, and psychometric network models. Part II, Factor Analysis and Classical Test Theory, the type of psychometrics that is still used most often in the social and behavioral sciences, includes state-of-the-art contributions on test-score reliability, change-score reliability, handling missing data in principal component analysis, test equating, and conditional standard errors of measurement. Part III, Item Response Theory, the leading form of psychometrics in modern educational measurement, includes discussions of sampling from many conditional distributions, transparent score reporting, nonparametric item response theory, and targeted testing. Part IV, New Psychometrics, discusses recently developed ideas beyond classical test theory and item response theory, including topics related to computer adaptive testing, response-time modelling, validity indices, diagnostic classification models, and the sparse latent class model for ordinal measurements. Together, these four parts provide an overview of the current state-of-the-art in psychometrics in educational measurement. They are a valuable source of information for graduate students who (intend to) study psychometrics and need an overview of the field, and for researchers interested in the current developments in the field. Chapters [3], [5], [8], [16] and [19] are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
| Author | : Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma |
| Publisher | : BPB Publications |
| Total Pages | : 580 |
| Release | : 2020-09-03 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 938984567X |
Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.
