20 Best Books On Predictive Analytics (2023 Update)

Learn methods of data analysis and their application to real-world data setsthis updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. the authors apply a unified “white box” approach to data mining methods and models. this approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review.

Learn the art and science of predictive analytics — techniques that get results predictive analytics is what translates big data into meaningful, usable business information. written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. it clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. hands-on examples and case studies are included. the ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today this guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions explains methods, principles, and techniques for conducting predictive analytics projects from start to finish illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios a companion website provides all the data sets used to generate the examples as well as a free trial version of software applied predictive analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

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. offers common use cases to help you get started covers details on modeling, k-means clustering, and more includes information on structuring your data provides tips on outlining business goals and approaches the future starts today with the help of predictive analytics for dummies.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of fundamentals of predictive analytics with jmp bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. first, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in jmp . using jmp 13 and jmp 13 pro, this book offers the following new and enhanced features in an example-driven format: an add-in for microsoft excel graph builder dirty data visualization regression anova logistic regression principal component analysis lasso elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison with today's emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. this book is part of the sas press program.

Use predictive analytics to uncover hidden patterns and correlations and improve decision-making using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. in this thoroughly updated guide, dr. dursun delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. delen provides a holistic approach covering key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. balancing theory and practice, delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies–including lessons from failed projects. it is all designed to help you gain a practical understanding you can apply for profit. * leverage knowledge extracted via data mining to make smarter decisions * use standardized processes and workflows to make more trustworthy predictions * predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * understand predictive algorithms drawn from traditional statistics and advanced machine learning * discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection

This edition presents a broad-based survey of business forecasting methods, including subjective and objective approaches. the authors, keating and wilson, deliver practical how-to forecasting techniques, along with dozens of real-world data sets while holding theory and math to a minimum. – today, most business planning routinely begins with a sales forecast. whether you are an accountant, a marketer, a human resources manager, a data scientist, or a financial analyst, sooner or later, you will have to predict something. this book is designed to lead students through the most helpful techniques to use in any prediction effort. – new to this edition – four new chapters focus on predictive analytics, recognizing the importance of these tools in today's prediction efforts. – examples of predictive analytics are based on historical data, much like what students may encounter in their own forecasts. – techniques are explained as procedures, allowing students to replicate them with their own data. – all chapters now include learning objectives. – the forecast – x software sections are updated and clarified. – retained features – real-world data are used throughout the text, which consists almost entirely of real-world examples, showing readers how to deal with real-life problems and difficulties in a realistic environment. – extended examples are presented in each chapter. two examples are used to demonstrate the progression of techniques needed to solve each issue; these examples provide a realistic illustration of the concepts that are relevant to the student used in the text. one example features sales data from the gap, whereas another example uses domestic car sales data. – by consistently providing all the data in excel, the leading calculation software used in business, students recognize its compatibility with forecast – x software.

So many people dream of becoming their own boss or succeeding in their chosen profession, and with the resources available today, more entrepreneurs and professionals are achieving great success! however, success should be defined for the long term, and as opportunities start to grow, so does the competition. getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. many people make the mistake of making plans but having no follow-through. this is where analytics comes in. don't you wish to have the power to know what your target consumers are thinking? won't you want to have a preview of what future trends to expect in the market you are in? well, this book is just the one you need. this book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time. the contents of this book are designed to help you use data to your advantage to enhance business outcomes!

Detect fraud earlier to mitigate loss and prevent cascading damage fraud analytics using descriptive, predictive, and social network techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. this invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. these techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. it is estimated that a typical organization loses about 5% of its revenue to fraud every year. more effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. examine fraud patterns in historical data utilize labeled, unlabeled, and networked data detect fraud before the damage cascades reduce losses, increase recovery, and tighten security the longer fraud is allowed to go on, the more harm it causes. it expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. fraud analytics using descriptive, predictive, and social network techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

If you’re involved with a startup, analytics help you find your way to the right product and market before the money runs out. but with a flood of information available, where do you start? this book shows you what to measure, how to analyze it, and how to report it, whether you’re evaluating your business model, testing new features, enticing investors, or reporting progress to advisers. written by alistair croll (founder of coradiant) and ben yoskovitz (co-founder of year one labs), lean analytics lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. examples and case studies show entrepreneurs and intrapreneneurs (entrepreneurs inside larger organizations) how to identify and measure a startup’s single most important metric, and how to iterate until you get it right. while the authors primarily cover technology startups, their lessons can be applied far beyond the web. even traditional businesses are embracing a lean, learn-first approach, as demonstrated by owners of a san francisco deli that used a temporary "pop-up" method to optimize their menu and operations before launching a permanent restaurant.

Adeptly address today’s business challenges with this powerful new book from web analytics thought leader avinash kaushik. web analytics 2.0 presents a new framework that will permanently change how you think about analytics. the book will help your organization become more data driven while you become a super analysis ninja! note: cd-rom/dvd and other supplementary materials are not included as part of ebook file.

Hr metrics and organizational people-related data are an invaluable source of information from which to identify trends and patterns in order to make effective business decisions. but hr practitioners often lack the statistical and analytical know-how to fully harness the potential of this data. predictive hr analytics provides a clear, accessible framework for understanding and working with people analytics and advanced statistical techniques. using the statistical package spss (with r syntax included), it takes readers step by step through worked examples, showing them how to carry out and interpret analyses of hr data in areas such as employee engagement, performance and turnover. readers are shown how to use the results to enable them to develop effective evidence-based hr strategies.this second edition has been updated to include the latest material on machine learning, biased algorithms, data protection and gdpr considerations, a new example using survival analyses, and up-to-the-minute screenshots and examples with spss version 25. it is supported by a new appendix showing main r coding, and online resources consisting of spss and excel data sets and r syntax with worked case study examples.

"turn yourself into a data head. you'll become a more valuable employee and make your organization more successful." thomas h. davenport, research fellow, author of competing on analytics, big data @ work, and the ai advantage you've heard the hype around data—now get the facts. in becoming a data head: how to think, speak, and understand data science, statistics, and machine learning, award-winning data scientists alex gutman and jordan goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. you'll learn how to: think statistically and understand the role variation plays in your life and decision making speak intelligently and ask the right questions about the statistics and results you encounter in the workplace understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence avoid common pitfalls when working with and interpreting data becoming a data head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. the authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. anyone can become a data head—an active participant in data science, statistics, and machine learning. whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

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.

The definitive guide to using analytics for better business decisions "a must-read for anyone who is directly or indirectly leading or managing an analytics function–and anyone who wants to make better decisions based on analytics, not just intuition or an 'overemphasis on industry knowledge, which crowds out good analytics.'" — charlotte e. sibley, president, sibley associates, a bio – pharma consulting company "over the long term, those who show the greatest imagination, grow the right skills, build the deepest organizations, and follow rigorous statistical practice will reap the greatest rewards from their analytics efforts. a practitioner's guide to business analytics lights the way." — thomas c. redman, ph – d, the data doc, navesink consulting group "executives beware. this is not your typical management book. this book contains real information from analytical professionals who are outside the executive bubble. . . . hold on to your seat and be prepared to change the way you think about leaders, leadership qualities, and leadership skills needed for future success in the changing business landscape." — thomas j. scott, director/advisor, marketing sciences solutions, tga – s advisors "randy bartlett has written an important and useful book, filling at least some of the large void between books that exhort managers to think more analytically without explaining how, and overly technical books that only quantitative analysts would appreciate. particular strengths are the recommendations about how to organize to integrate analytical expertise into decision-making and the guidance about how managers can assess whether they are getting good analytical advice." — douglas a. samuelson, d.sc., president and chief scientist, info – logix, inc., annandale, va; quantitative analyst, inventor, entrepreneur and executive about the book: the real tragedy of a company failing while using analytics is the fact that its leaders will have the data to explain the failure, but they won't have the capabilities in place to filter the data and convert it into actionable business insights. a practitioner's guide to business analytics integrates powerful strategies for leveraging analytics inside a business with a how-to playbook of tactics to make it happen. the case for competing based on analytics is clear, but until now, there hasn't been authoritative guidance for inciting a corporate community to evolve into a thriving, analytics-driven environment. this hands-on book gives you the tools, knowledge, and strategies to capture the level of organizational commitment you need to get business analytics up and running in your company. it helps you define what business analytics is, quantify the exponential value it brings to an organization, and show others how to harness its power to gain advantage over competitors. accomplished business information professional randy bartlett brings his comprehensive coverage to life with firsthand accounts of using business analytics at brand-name global companies. through in-depth examinations of success stories and failures in analytics-based decision making and data analyses, he fully prepares you to: assess your company's analytics needs and capabilities, and develop a strategic analytics plan steward the three pillars of best statistical practice and accurately measure the quality of analytics-based decisions and data analyses build and organize a specialized business analytics team to lead infrastructural changes upgrade the foundation that supports business analytics–data collection, data software, and data management create the essential synergy for success between the business analytics team and it effectively integrating analytics into everyday decision making, corporate culture, and business strategy is a multifront exercise in leadership, execution, and support. the specialized tools and skill sets required to succeed are finally in one resource–a practitioner's guide to business 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.

"predictive analytics (pa) is now a mainstream business tool. predictive analytics in 56 minutes provides a superb introduction to the fascinating world of predictive analytics which can be read in about an hour. in this time you will gain an understanding of what predictive analytics is, how it's applied and how organizations benefit from its application"–back cover

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.

Over the past decade, big data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. they have led to striking technological advances, affecting all human experiences. our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. there are three reasons for this shortfall. first, the volume of data is increasing much faster than the corresponding rise of our computational processing power (kryder’s law > moore’s law). second, traditional discipline-bounds inhibit expeditious progress. third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. there are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. the textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. exposing the enormous opportunities presented by the tsunami of big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. the content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. the two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). this can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. – a geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. to determine if the patient may suffer from parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (big data). most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. a learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. the results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. – to improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (big data). the organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. this system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., r&d allocation, that maximizes the company’s bottom line. a reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. the results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.

Over the past decade, big data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. they have led to striking technological advances, affecting all human experiences. our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. there are three reasons for this shortfall. first, the volume of data is increasing much faster than the corresponding rise of our computational processing power (kryder’s law > moore’s law). second, traditional discipline-bounds inhibit expeditious progress. third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. there are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. the textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap. exposing the enormous opportunities presented by the tsunami of big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics. the content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies. the two examples below demonstrate the powerful need for scientific knowledge, computational abilities, interdisciplinary expertise, and modern technologies necessary to achieve desired outcomes (improving human health and optimizing future return on investment). this can only be achieved by appropriately trained teams of researchers who can develop robust decision support systems using modern techniques and effective end-to-end protocols, like the ones described in this textbook. – a geriatric neurologist is examining a patient complaining of gait imbalance and posture instability. to determine if the patient may suffer from parkinson’s disease, the physician acquires clinical, cognitive, phenotypic, imaging, and genetics data (big data). most clinics and healthcare centers are not equipped with skilled data analytic teams that can wrangle, harmonize and interpret such complex datasets. a learner that completes a course of study using this textbook will have the competency and ability to manage the data, generate a protocol for deriving biomarkers, and provide an actionable decision support system. the results of this protocol will help the physician understand the entire patient dataset and assist in making a holistic evidence-based, data-driven, clinical diagnosis. – to improve the return on investment for their shareholders, a healthcare manufacturer needs to forecast the demand for their product subject to environmental, demographic, economic, and bio-social sentiment data (big data). the organization’s data-analytics team is tasked with developing a protocol that identifies, aggregates, harmonizes, models and analyzes these heterogeneous data elements to generate a trend forecast. this system needs to provide an automated, adaptive, scalable, and reliable prediction of the optimal investment, e.g., r&d allocation, that maximizes the company’s bottom line. a reader that complete a course of study using this textbook will be able to ingest the observed structured and unstructured data, mathematically represent the data as a computable object, apply appropriate model-based and model-free prediction techniques. the results of these techniques may be used to forecast the expected relation between the company’s investment, product supply, general demand of healthcare (providers and patients), and estimate the return on initial investments.