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R 813.90
R 1,373.00
Predictive Analytics For Dummies
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get startedCovers details on modeling, k-means clustering, and moreIncludes information on structuring your dataProvides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
R 684.00
Data Science, Analytics and Machine Learning with R
Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.
R 4,414.00
Applying Predictive Analytics
The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.
R 2,846.36
Predictive Analytics
Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
R 2,759.71
Business Analytics
This exciting new textbook offers an accessible, business-focused overview of the key theoretical concepts underpinning modern data analytics. It provides engaging and practical advice on using the key software tools, including SAS Visual Analytics, R and DataRobot, that are used in organisations to help make effective data-driven decisions. Combining theory with hands-on practical examples, this essential text includes cutting edge coverage of new areas of interest including social media analytics, design thinking and the ethical implications of using big data. A wealth of learning features including exercises, cases, online resources and data sets help students to develop analytic problem-solving skills.With its management perspective on analytics and its coverage of a range of popular software tools, this is an ideal essential text for upper-level undergraduate, postgraduate and MBA students. It is also ideal for practitioners wanting to understand the broader organisational context of big data analysis and to engage critically with the tools and techniques of business analytics.Accompanying online resources for this title can be found at bloomsburyonlineresources.com/business-analytics. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.
R 2,647.00
Computational Business Analytics
Learn How to Properly Use the Latest Analytics Approaches in Your OrganizationComputational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagramsAugments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.
R 9,354.79
Predictive Analytics for the Modern Enterprise
The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics in the cloud. Author Nooruddin Abbas Ali, principal solutions architect at MongoDB, brings you up to speed through industry use cases and end-to-end hands-on examples. This book helps business technology leaders: Implement and operationalize predictive analytics in your organization Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow
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R 244.00
Foundations of Predictive Analytics
Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory.An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.Web ResourceThe book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
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Business Analytics
Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making capabilities of an organization. Covering the key areas of business analytics, the book explores the concepts, techniques, applications, and emerging trends that professionals across a wide range of industries need to be aware of. Better detection of fraud through visual analytics or better prediction of the likelihood of someone getting an infection while in the hospital are just a few examples of where analytics can play a positive role. As the field of business analytics continues to emerge rapidly, there is a need for a reliable textbook and reference on the subject. Filling this need, this book is suitable for graduate-level students and undergraduate seniors. It maintains a focus on only the key areas so the material can be covered adequately in a one-semester or one-quarter course. Each chapter includes software-generic exercises, labs, and associated answers to the exercises/labs. Author Jay Liebowitz recently had an article published in The World Financial Review. www.worldfinancialreview.com/?p=1904
R 5,090.00
ISE Predictive Analytics for Business Strategy
Designed for courses that provide a conceptual and broad-based introduction to econometrics and business analytics, Predictive Analytics for Business Strategy, 1st edition provides future managers with a basic understanding of what data can do in forming business strategy without getting into a taxonomy of models and their statistical properties. Through engaging questions, explanations, and applications, students develop a deeper understanding of the fundamental reasoning behind how and why analysis can generate actionable knowledge and learn to think critically about whether a given analysis has merit or not.
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R 2,013.00
Computing Predictive Analytics, Business Intelligence, and Economics
This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume.One of BookAuthority’s Best New Analytics Books to Read in 2020!
R 3,988.99
Introduction to NFL Analytics with R
It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavour, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free mannerKey Coverage:• Installing R, RStudio, and necessary packages• Working and becoming fluent in the tidyverse• Finding meaning in NFL data with examples from all the functions in the nflverse family of packages• Using NFL data to create eye-catching data visualizations• Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient BoostingThe book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike, will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.
R 8,977.87
Applied Marketing Analytics Using R
Marketing has become increasingly data-driven in recent years as a result of new emerging technologies such as AI, granular data availability and ever-growing analytics tools. With this trend only set to continue, it’s vital for marketers today to be comfortable in their use of data and quantitative approaches and have a thorough grounding in understanding and using marketing analytics in order to gain insights, support strategic decision-making, solve marketing problems, maximise value and achieve success.Taking a very hands-on approach with the use of real-world datasets, case studies and R (a free statistical package), this book supports students and practitioners to explore a range of marketing phenomena using various applied analytics tools, with a balanced mix of technical coverage alongside marketing theory and frameworks. Chapters include learning objectives, figures, tables and questions to help facilitate learning.Also included online with the datasets are software codes and solutions (R Markdowns, HTML files) to use with the book, as well as PowerPoint slides, a teaching guide and a testbank for instructors teaching a marketing analytics course.This book is essential reading for advanced level marketing students and marketing practitioners who want to become cutting-edge marketers.Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, London.Dr. Raoul V. Kübler is an Associate Professor of Marketing at ESSEC Business School, Paris.
R 5,885.00
Introduction to NFL Analytics with R
It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavour, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free mannerKey Coverage:• Installing R, RStudio, and necessary packages• Working and becoming fluent in the tidyverse• Finding meaning in NFL data with examples from all the functions in the nflverse family of packages• Using NFL data to create eye-catching data visualizations• Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient BoostingThe book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike, will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.
R 3,170.10
Hands-On Predictive Analytics with Python
Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook DescriptionPredictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is forThis book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
R 1,661.00
Foundations of Programming, Statistics, and Machine Learning for Business Analytics
Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage.This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice.Key features:· Introduces programming fundamentals using R and Python· Covers data structures, data management and manipulation and data visualization· Includes interactive coding notebooks so that you can build up your programming skills progressivelySuitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science.Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick.Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies.Tillman Weyde is Senior Lecturer at City, University of London.
R 3,237.00
R for Marketing Research and Analytics
This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis.With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.
R 3,297.00
Data-Driven Modelling and Predictive Analytics in Business and Finance
Data- driven and AI- aided applications are next- generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data- driven solutions, IoT technologies, AI- aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data- driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data- driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.
R 12,345.48
Applied Marketing Analytics Using R
Marketing has become increasingly data-driven in recent years as a result of new emerging technologies such as AI, granular data availability and ever-growing analytics tools. With this trend only set to continue, it’s vital for marketers today to be comfortable in their use of data and quantitative approaches and have a thorough grounding in understanding and using marketing analytics in order to gain insights, support strategic decision-making, solve marketing problems, maximise value and achieve success.Taking a very hands-on approach with the use of real-world datasets, case studies and R (a free statistical package), this book supports students and practitioners to explore a range of marketing phenomena using various applied analytics tools, with a balanced mix of technical coverage alongside marketing theory and frameworks. Chapters include learning objectives, figures, tables and questions to help facilitate learning.Also included online with the datasets are software codes and solutions (R Markdowns, HTML files) to use with the book, as well as PowerPoint slides, a teaching guide and a testbank for instructors teaching a marketing analytics course.This book is essential reading for advanced level marketing students and marketing practitioners who want to become cutting-edge marketers.Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, London.Dr. Raoul V. Kübler is an Associate Professor of Marketing at ESSEC Business School, Paris.
R 2,184.00
Predictive Analytics
Predictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
R 9,317.75
Spatial Predictive Modeling with R
Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial predictions. This book aims to systematically introduce the entire process of SPM as a discipline. The process contains data acquisition, spatial predictive methods and variable selection, parameter optimization, accuracy assessment, and the generation and visualization of spatial predictions, where spatial predictive methods are from geostatistics, modern statistics, and machine learning. The key features of this book are: •Systematically introducing major components of SPM process.•Novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods.•Novel predictive accuracy-based variable selection techniques for spatial predictive methods.•Predictive accuracy-based parameter/model optimization.•Reproducible examples for SPM of various data types in R.This book provides guidelines, recommendations, and reproducible examples for developing optimal predictive models by considering various components and associated factors for quality-improved spatial predictions. It provides valuable tools for researchers, modelers, and university students not only in SPM field but also in other predictive modeling fields. Dr Li has produced over 100 various publications in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology, developed a number of hybrid methods for SPM, and published four R packages for variable selections as well as SPM.
R 6,960.00
Hands-on Intermediate Econometrics Using R: Templates For Learning Quantitative Methods And R Software
How to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.
R 5,282.16
Data-Driven Modelling and Predictive Analytics in Business and Finance
Data- driven and AI- aided applications are next- generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data- driven solutions, IoT technologies, AI- aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data- driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data- driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers:Data-driven modellingPredictive analyticsData analytics and visualization toolsAI-aided applicationsCybersecurity techniquesCloud computingIoT-enabled systems for developing smart financial systemsThis book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.
R 4,680.66
Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics
Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting.Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.
R 7,965.93
Business Analytics
Analytical skills are in high demand but short supply. Through a unique combination of data visualisation and analytics (both theoretical and applied), this ground-breaking textbook provides you with the expertise to analyse, interpret and communicate data with confidence, to inform real-world decision-making.Key features include:• Difficulty indicators for each chapter signalling introductory, intermediate and advanced content• An entire chapter dedicated to data visualisation, introducing Tableau for storytelling with data• Motivational cases throughout showing how the theory relates to real life• A wide variety of end-of-chapter exercises to test your understanding of the topics covered• Online examples and datasets to help you master your analytics skills through hands-on learningLecturers also have access a range of online resources developed to support teaching, including a Teaching Guide, solutions to end-of-chapter exercises, PowerPoints and a testbank.Business Analytics: Applied Modelling and Prediction is suitable for undergraduate and postgraduate students of Business Analytics.Dr James Abdey is an Associate Professor (Education) in Statistics at The London School of Economics and Political Science (LSE).
R 3,767.00
Big Data Analytics with R
Utilize R to uncover hidden patterns in your Big DataAbout This Book• Perform computational analyses on Big Data to generate meaningful results• Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,• Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketWho This Book Is ForThis book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.What You Will Learn• Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities• Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner• Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage• Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platformIn DetailBig Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.Style and approachThis book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets.
R 2,084.00
Regression and Machine Learning for Education Sciences Using R
This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making.Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning. With each chapter, the discussion and development of each statistical concept and data analytical technique are presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts.Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial. The dataset and examples used in the book will be from the educational setting, and students will find that this text provides good preparation for studying more statistical and data analytical materials.
R 9,332.17
Mastering Predictive Analytics with Python
Exploit the power of data in your business by building advanced predictive modeling applications with PythonAbout This Book• Master open source Python tools to build sophisticated predictive models• Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide• Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.What You Will Learn • Gain an insight into components and design decisions for an analytical application• Master the use Python notebooks for exploratory data analysis and rapid prototyping• Get to grips with applying regression, classification, clustering, and deep learning algorithms• Discover the advanced methods to analyze structured and unstructured data• Find out how to deploy a machine learning model in a production environment• Visualize the performance of models and the insights they produce• Scale your solutions as your data grows using Python• Ensure the robustness of your analytic applications by mastering the best practices of predictive analysisIn DetailThe volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to lifeStyle and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to
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Predictive Analytics for Marketers
Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes.
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Using R for Data Analysis in Social Sciences
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings.Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test,covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and theprocess of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
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R For Marketing Research and Analytics
The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.The 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.
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