Articles by "Data Analysis"

Showing posts with label Data Analysis. Show all posts

Thursday, September 20, 2018 , , , ,
Patrick Siarry ... 489 pages - Publisher: Springer; 1st edition (December, 2016) ... Language: English - ASIN: B01N7LMXV6 by Amazon ...

Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explanations of the main metaheuristics techniques, including simulated annealing, tabu search, evolutionary algorithms, artificial ants, and particle swarms, followed by chapters that demonstrate their applications to problems such as multiobjective optimization, logistics, vehicle routing, and air traffic management. The authors are leading researchers in this domain, with considerable teaching and applications experience, and the book will be of value to industrial practitioners, graduate students, and research academics.
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Wednesday, September 19, 2018 , , ,
Nataraj Dasgupta ... 412 pages - Publisher: Packt Publishing; (January, 2018) ... Language: English - ISBN-10: 9781783554393 - ISBN-13: 978-1783554393 ...

Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book.
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Tuesday, September 18, 2018 , , ,
Richard F. Gunst, Robert L. Mason ... 424 pages - Publisher: CRC Press; 1st edition (July, 1980) ... Language: English - ISBN-10: 0824769937 - ISBN-13: 978-0824769932 ...

Regression Analysis and Its Application: A Data-Oriented Approach answers the need for researchers and students who would like a better understanding of classical regression analysis. Useful either as a textbook or as a reference source, this book bridges the gap between the purely theoretical coverage of regression analysis and its practical application. The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates. Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.
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Thursday, September 13, 2018 , , ,
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal ... 655 pages - Publisher: Morgan Kaufmann; 4th edition (October, 2016) ... Language: English - ASIN: B01MG31RL3 by Amazon ...

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
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Tuesday, September 11, 2018 , , ,
Hossein Riazoshams, Habshah Midi, Gebrenegus Ghilagaber ... 264 pages - Publisher: Wiley; 1st edition (June, 2018) ... Language: English - ASIN: B07DP92FBT by Amazon ...

The first book to discuss robust aspects of nonlinear regression—with applications using R software: Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression
Addresses some commonly mishandled aspects of modeling. R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying websiteRobust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics.
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Tuesday, September 04, 2018 , ,
R. Lyman Ott, Micheal T. Longnecker ... 1296 pages - Publisher: Cengage Learning; 7th edition (June, 2015) ... Language: English - ISBN-10: 1305269470 - ISBN-13: 978-1305269477 ...

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.
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Thursday, August 30, 2018 , , , ,
Conrad Carlberg ... 272 pages - Publisher: Que Publishing; 1st edition (November, 2016) ... Language: English - ISBN-10: 0789757850 - ISBN-13: 978-0789757852 ...

Microsoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis—if you can get over its learning curve. In R for Microsoft® Excel Users, Conrad Carlberg shows exactly how to get the most from both programs. Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides you through reaching the same outcome in R—including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool. Writing in clear, understandable English, Carlberg combines essential statistical theory with hands-on examples reflecting real-world challenges. By the time you’ve finished, you’ll be comfortable using R to solve a wide spectrum of problems—including many you just couldn’t handle with Excel. Contents: • Smoothly transition to R and its radically different user interface • Leverage the R community’s immense library of packages • Efficiently move data between Excel and R • Use R’s DescTools for descriptive statistics, including bivariate analyses • Perform regression analysis and statistical inference in R and Excel • Analyze variance and covariance, including single-factor and factorial ANOVA • Use R’s mlogit package and glm function for Solver-style logistic regression • Analyze time series and principal components with R and Excel.
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Sunday, August 26, 2018 , , , ,
Giuseppe Ciaburro, Balaji Venkateswaran ... 270 pages - Publisher: Packt Publishing; (September, 2017) ... Language: English - ISBN-10: 1788397878 - ISBN-13: 978-1788397872 ...

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
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Wednesday, July 18, 2018 , , , ,
Stephen J. Chapman ... 592 pages - Publisher: CL Engineering; 4th edition (November, 2007) ... Language: English - ISBN-10: 049524449X - ISBN-13: 978-0495244493 ...

Emphasizing problem-solving skills throughout this very successful book, Stephen Chapman introduces the MATLAB language and shows how to use it to solve typical technical problems. The book teaches MATLAB as a technical programming language showing students how to write clean, efficient, and well-documented programs. It makes no pretense at being a complete description of all of MATLAB's hundreds of functions. Instead, it teaches students how to locate any desired function with MATLAB's extensive on line help facilities. Overall, students develop problem-solving skills and are equipped for future courses and careers using the power of MATLAB.
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Saturday, July 07, 2018 , , ,
Machine Learning Classification Algorithms using MatLab [Size: 580 MB] ... This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Table of Contents: - Segment 1: Instructor and Course Introduction - Segment 2: MATLAB Crash Course - Segment 3: Grabbing and Importing Dataset - Segment 4: K-Nearest Neighbor - Segment 5: Naive Bayes - Segment 6: Decision Trees - Segment 7: Discriminant Analysis - Segment 8: Support Vector Machines - Segment 9: Error Correcting Output Codes - Segment 10: Classification with Ensembles - Segment 11: Validation Methods - Segment 12: Evaluating Performance. This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience.
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Wednesday, July 04, 2018 , , ,
Amos Gilat ... 416 pages - Publisher: Wiley; 5th edition (January, 2014) ... Language: English- ISBN-10: 1118629868 - ISBN-13: 978-1118629864 ...

More college students use Amos Gilat’s MATLAB: An Introduction with Applications than any other MATLAB textbook. This concise book is known for its just-in-time learning approach that gives students information when they need it. The new edition gradually presents the latest MATLAB functionality in detail. Equally effective as a freshmen-level text, self-study tool, or course reference, the book is generously illustrated through computer screen shots and step-by-step tutorials, with abundant and motivating applications to problems in mathematics, science, and engineering.
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Saturday, June 30, 2018 , , ,
Statistician v2.00.01.79 [Size: 4.32 MB] ... Statistician is a comprehensive yet simple to use add-in for Microsoft Excel 2007 or later. The software performs a high quality statistical analysis based on a series of easy-to-use forms, activated via the Excel tab. Statistician works in a unique way when compared to other Excel based statistical analysis software. Most importantly, it allows the user to store a data set and perform multiple analyzes on it, a method used by all high end statistical software, but lacking in many other Excel add-ins that require the user to re-select data over and over again when they return to the spreadsheet. Many customers, having used alternative statistical data analysis in the past, are amazed by not only how powerful and reliable Statistician is, but how fast and easy it is to use.

Features: – conduct statistical analyzes different – a set of standard tests – tests of variance – test for parametric – Test normalization – the Mac is to sort and grade data – a logistic regression model and Probiotic regression model – Sampling – Optimization – Provide a summary of statistical results. Calculations: ANOVA - Multiple Regression Analysis - Variance Tests - Normality Tests - Non Parametric Tests - Chi Square Tests - Binary Models - Count Model - Multivariate Analysis - Time Series Analysis.
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Monday, April 16, 2018 , ,
George E. P. Box,‎ George C. Tiao ... 588 pages - Publisher: Addison-Wesley Publishing; 1st edition (June, 1973) ... Language: English - ISBN-10: 0201006227 - ISBN-13: 978-0201006223 ...

Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a prior. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly non-informative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.
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Friday, April 06, 2018 , ,
Roger Fletcher ... 456 pages - Publisher: Wiley; 2nd edition (May, 2000) ... Language: English - ISBN-10: 0471494631 - ISBN-13: 978-0471494638 ...

Fully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasi-Newton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions. * Introduction * Structure of Methods * Newton-like Methods * Conjugate Direction Methods * Restricted Step Methods * Sums of Squares and Nonlinear Equations * Introduction * Linear Programming * The Theory of Constrained Optimization * Quadratic Programming * General Linearly Constrained Optimization * Nonlinear Programming * Other Optimization Problems.
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Wednesday, March 28, 2018 , , ,
Brian C. Cronk ... 136 pages - Publisher: Routledge; 5th edition (January, 2008) ... Language: English - ISBN-10: 1884585795 - ISBN-13: 978-1884585791 ...

Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. • All major statistical techniques covered in beginning statistics classes are included: descriptive statistics * graphing data * prediction and association * parametric inferential statistics * nonparametric inferential statistics * statistics for test construction. • Each section starts with a brief description of the statistic that is covered and important underlying assumptions, which help students select appropriate statistics. • Each section describes how to interpret results and express them in a research report after the data are analyzed. For example, students are shown how to phrase the results of a significant and an insignificant t test. • More than 200 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. • A glossary of statistical terms is included, which makes a handy reference for students who need to review the meanings of basic statistical terms. • Practice exercises throughout the book give students stimulus material to use as they practice to achieve mastery of the program. • Thoroughly field-tested; your students are certain to appreciate this book.
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Monday, March 26, 2018 , , ,
Narasimha Karumanchi ... 434 pages - Publisher: CareerMonk Plublications; 5th edition (August, 2016) ... Language: English - ISBN-10: 819324527X - ISBN-13: 978-8193245279 ...

A handy guide of sorts for any computer science professional, Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues,Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems.
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Sunday, March 25, 2018 , ,
Nataraj Venkataramanan, Ashwin Shriram ... 232 pages - Publisher: Chapman and Hall/CRC; (August, 2016) ... Language: English - ISBN-10: 1498721044 - ISBN-13: 978-1498721042

The book covers data privacy in depth with respect to data mining, test data management, synthetic data generation etc. It formalizes principles of data privacy that are essential for good anonymization design based on the data format and discipline. The principles outline best practices and reflect on the conflicting relationship between privacy and utility. From a practice standpoint, it provides practitioners and researchers with a definitive guide to approach anonymization of various data formats, including multidimensional, longitudinal, time-series, transaction, and graph data. In addition to helping CIOs protect confidential data, it also offers a guideline as to how this can be implemented for a wide range of data at the enterprise level.
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Thursday, March 22, 2018 , ,
F. M. Dekking, C. Kraaikamp ... 488 pages - Publisher: Springer; (February, 2007) ... Language: English - ISBN-10: 1852338962 - ISBN-13: 978-1852338961 ...

Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap. This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects.
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Wednesday, March 21, 2018 , , ,
Rafael A. Irizarry, Michael I. Love ... 376 pages - Publisher: Chapman and Hall/CRC; 1st edition (August, 2016) ... Language: English - ISBN-10: 1498775675 - ISBN-13: 978-1498775670 ...

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.
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Tuesday, March 20, 2018 , , ,
John J. Kinney ... 480 pages - Publisher: Wiley; 2nd edition (December, 2014) ... Language: English - ISBN-10: 1118947088 - ISBN-13: 978-1118947081.

Thoroughly updated, Probability: An Introduction with Statistical Applications, Second Edition features a comprehensive exploration of statistical data analysis as an application of probability. The new edition provides an introduction to statistics with accessible coverage of reliability, acceptance sampling, confidence intervals, hypothesis testing, and simple linear regression. Encouraging readers to develop a deeper intuitive understanding of probability, the author presents illustrative geometrical presentations and arguments without the need for rigorous mathematical proofs. The Second Edition features interesting and practical examples from a variety of engineering and scientific fields, as well as: Over 880 problems at varying degrees of difficulty allowing readers to take on more challenging problems as their skill levels increase + Chapter-by-chapter projects that aid in the visualization of probability distributions + New coverage of statistical quality control and quality production + An appendix dedicated to the use of Mathematica® and a companion website containing the referenced data sets. Featuring a practical and real-world approach, this textbook is ideal for a first course in probability for students majoring in statistics, engineering, business, psychology, operations research, and mathematics. Probability: An Introduction with Statistical Applications, Second Edition is also an excellent reference for researchers and professionals in any discipline who need to make decisions based on data as well as readers interested in learning how to accomplish effective decision making from data.
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