Visualization That Means Something
Author: Nathan Yau
Publisher: John Wiley & Sons
A fresh look at visualization from the author of Visualize This Whether it's statistical charts, geographic maps, or the snappy graphical statistics you see on your favorite news sites, the art of data graphics or visualization is fast becoming a movement of its own. In Data Points: Visualization That Means Something, author Nathan Yau presents an intriguing complement to his bestseller Visualize This, this time focusing on the graphics side of data analysis. Using examples from art, design, business, statistics, cartography, and online media, he explores both standard-and not so standard-concepts and ideas about illustrating data. Shares intriguing ideas from Nathan Yau, author of Visualize This and creator of flowingdata.com, with over 66,000 subscribers Focuses on visualization, data graphics that help viewers see trends and patterns they might not otherwise see in a table Includes examples from the author's own illustrations, as well as from professionals in statistics, art, design, business, computer science, cartography, and more Examines standard rules across all visualization applications, then explores when and where you can break those rules Create visualizations that register at all levels, with Data Points: Visualization That Means Something.
Author: Nathan Yau
Publisher: John Wiley & Sons
Category: Statistics / Graphic methods / Data processing
A guide on how to visualise and tell stories with data, providing practical design tips complemented with step-by-step tutorials.
Author: Robert G. Mortimer
Publisher: Academic Press
Mathematics for Physical Chemistry is the ideal textbook for upper-level undergraduates or graduate students who want to sharpen their mathematics skills while they are enrolled in a physical chemistry course. Solved examples and problems, interspersed throughout the presentation and intended to be
13th International Conference, Natal, Brazil, August 29-31, 2012, Proceedings
Author: Hujun Yin,Jose A.F. Costa,Guilherme Barreto
This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
Theories, Algorithms, and Examples
Author: Nong Ye
Publisher: CRC Press
Category: Business & Economics
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.
Complete guide to automating Big Data solutions using Artificial Intelligence techniques
Author: Anand Deshpande,Manish Kumar
Publisher: Packt Publishing Ltd
Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.
13th International Symposium, IDA 2014, Leuven, Belgium, October 30 -- November 1, 2014. Proceedings
Author: Hendrik Blockeel,Matthijs van Leeuwen,Veronica Vinciotti
This book constitutes the refereed conference proceedings of the 13th International Conference on Intelligent Data Analysis, which was held in October/November 2014 in Leuven, Belgium. The 33 revised full papers together with 3 invited papers were carefully reviewed and selected from 70 submissions handling all kinds of modeling and analysis methods, irrespective of discipline. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems.
Author: Patricia Cohen
This innovative volume demonstrates the use of a range of statistical approaches that examine "turning points" (a change in direction, magnitude, or meaning) in real data. Analytic techniques are illustrated with real longitudinal data from a variety of fields. As such the book will appeal to a variety of researchers including: Developmental researchers interested in identifying factors precipitating turning points at various life stages. Medical or substance abuse researchers looking for turning points in disease or recovery. Social researchers interested in estimating the effects of life experiences on subsequent behavioral changes. Interpersonal behavior researchers looking to identify turning points in relationships. Brain researchers needing to discriminate the onset of an experimentally produced process in a participant. The book opens with the goals and theoretical considerations in defining turning points. An overview of the methods presented in subsequent chapters is then provided. Chapter goals include discriminating "local" from long-term effects, identifying variables altering the connection between trajectories at different life stages, locating non-normative turning points, coping with practical distributional problems in trajectory analyses, and changes in the meaning and connections between variables in the transition to adulthood. From an applied perspective, the book explores such topics as antisocial/aggressive trajectories at different life stages, the impact of imprisonment on criminal behavior, family contact trajectories in the transition to adulthood, sustained effects of substance abuse, alternative models of bereavement, and identifying brain changes associated with the onset of a new brain process. Ideal for advanced students and researchers interested in identifying significant change in data in a variety of fields including psychology, medicine, education, political science, criminology, and sociology.
Integrated Analysis and Causal Inference
Author: Momiao Xiong
Publisher: CRC Press
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. ? FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks ? The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Author: William Nugent
Publisher: Oxford University Press
Category: Social Science
Single system, or single case, design studies are a convenient method for evaluating practice, allowing professionals to track clients' response to treatment and change over time. They also allow researchers to gather data where it might be difficult to conduct a study involving treatment and control groups; in a school setting, or a community mental health agency, for example, random assignment may be impossible, whereas individual student or client progress across time can be more easily monitored. This pocket guide reviews a wide range of techniques for analyzing single system design data, including visual analysis methods, graphical methods, and statistical methods. From basic visual observation to complex ARIMA statistical models for use with interrupted time series designs, numerous data analysis methods are described and illustrated in this unique and handy book. The author frankly describes limitations and strengths of the data analysis methods so that readers can select an appropriate method and use the results responsibly in order to improve practice and client well-being. This accessible yet in-depth introduction will serve as a highly practical resource for doctoral students and researchers alike.
How Learning Makes Sense
Author: Chris Thornton
Publisher: MIT Press
Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner.
Models and Algorithms
Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
Discusses issues related to the mining aspects of data streams. Each chapter in this book contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. It is intended for a professional audience composed of researchers and practitioners in industry.
Animations, Simulations, and Calculations Using MathematicaÂ
Author: Charles Pidgeon
Publisher: John Wiley & Sons
This unique book and computer disk package will help researchers, instructors, and students in pharmacy, medicinal chemistry, biochemistry, or other biomedical sciences reach a deeper understanding of the more advanced chemical and physicochemical processes as they relate to drug action, drug discovery, and biomedical science in general. Mathematica software permits rapid numerical, symbolic, and graphic calculations that allow complex concepts to be displayed, animated, and discussed in the same document. In "Advanced Tutorials for the Biomedical Sciences," Mathematica is used as a tool to display, animate, and calculate various physical phenomena: No programming by the instructor or the reader is needed to activate these functions. The Tutorials are "interactive" in that the user not only enters but may also change the values of parameters within the code in order to better understand difficult concepts. The computer disk will continue to serve the researcher as a computational "toolbox" for the common calculations needed to perform a variety of chromatographic and spectroscopic analyses. While the Mathematica software is needed to run the Tutorials, it can be applied to any number of additional mathematical or scientific applications.
Tools and Techniques for Scientific Computing
Author: Ben Klemens
Publisher: Princeton University Press
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.
7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings
Author: Jie Tang,Irwin King,Ling Chen,Jianyong Wang
Publisher: Springer Science & Business Media
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.
Methods for Complex Systems & Big Data
Author: J. Nathan Kutz
Publisher: OUP Oxford
Category: Language Arts & Disciplines
The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: · statistics, · time-frequency analysis, and · low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.
Publications, slides, posters, overhead projections, tape-slides, television Principles and practices for authors and teachers
Author: L. Reynolds,D. Simmonds
Publisher: Springer Science & Business Media
Basic essentials for black-and-white artwork 137 11. 2. 11. 2. 1. Paper 137 11. 2. 2. Pencils 141 11. 2. 3. Inks 142 11. 2. 4. Pens 142 11. 2. 5. Rulers and set squares 143 11. 2. 6. Templates and stencils 143 11. 2. 7. Erasers 144 Scalpels 144 11. 2. S. 11. 2. 9. Burnishers 144 11. 2. 10. Adhesives 152 11. 2. 11. Drafting tape 152 11. 2. 12. Drawing boards 153 11. 3. Other useful items for black-and-white artwork 153 11. 3. 1. Self-adhesive tapes 153 11. 3. 2. Dry-transfer symbols and lettering 154 11. 3. 3. Tone sheets 155 11. 3. 4. 'Pounce' powder 156 11. 3. 5. Fixatives and varnishes 156 11. 4. Additional materials for colour and OHP work 157 11. 4. 1. Self-adhesive colour sheets 157 11. 4. 2. Paints 157 11. 4. 3. Brushes 157 11. 4. 4. Cells and foils (acetate sheets) 158 11. 5. Working comfort 158 11. 5. 1. Organisation 158 11. 5. 2. Furniture 158 11. 5. 3. Lighting 159 12. Basic techniques 160 12. 1. Care and preparation of paper 160 12. 2. Ink work 160 12. 2. 1. Use of technical drawing pens 160 12. 2. 2. Blotting 163 12. 2. 3. Drawing ink lines 163 12. 2. 4. Finishing-off ink lines 165 12. 2. 5. Use of templates and stencils 165 12. 2. 6. Short cuts 165 12. 3. Correcting errors in ink work 167 12. 3. 1.