Understanding the Predictive Analytics Lifecycle
Understanding the Predictive Analytics Lifecycle

Author: Alberto Cordoba

Publisher: John Wiley & Sons

Published: 2014-07-30

Total Pages: 202

ISBN-13: 1118938925

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A high-level, informal look at the different stages of the predictive analytics cycle Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects. Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: An overview of all relevant phases: design, prepare, explore, model, communicate, and measure Coverage of the stages of the predictive analytics cycle across different industries and countries A chapter dedicated to each of the phases of the development of a predictive initiative A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide.

The Analytics Lifecycle Toolkit
The Analytics Lifecycle Toolkit

Author: Gregory S. Nelson

Publisher: John Wiley & Sons

Published: 2018-03-07

Total Pages: 468

ISBN-13: 1119425093

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An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.

The Analytics Lifecycle Toolkit
The Analytics Lifecycle Toolkit

Author: Gregory S. Nelson

Publisher: John Wiley & Sons

Published: 2018-04-03

Total Pages: 470

ISBN-13: 1119425069

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Data has become the new currency; organizations are drowning in it, but few are cashing in on its true value. The Analytics Lifecycle Toolkit translates the entire analytics lifecycle into actionable insights, providing a framework for building an effective analytics capability and the processes that turn data into action. Part 1 describes the “who,” “how,” and “why” of modern enterprise analytics, giving leaders clear insight into the value of strategically-aligned capabilities. Part 2 details best practices that include problem framing, data sensemaking, model development, change management, data management, product management, and more. Part 3 rounds out the discussion by providing guidance on sustaining high performance and guiding the analytics function into new phases of business. For organizations who see the value of analytics but lack the depth of knowledge needed to structure appropriate solutions, this book breaks the cycle of frustration and provides a roadmap for putting the right people, processes, and technologies into place. For those who have already implemented analytics, this book serves as a reference for leadership and a “refresher course” to update the team on the latest in practices and processes. Rather than a simple catalogue of analytics models, the discussion emphasizes underlying principles in key process areas to help organizations build analytics capabilities tailored to their specific needs—allowing them to harvest the highest-value information to better inform strategic decisions. In line with the book’s practical focus, the companion website provides downloadable resources, tools, videos, and more to support and streamline implementation. The discussion itself assumes no prior knowledge of analytics and explicitly clarifies complex concepts and terms, using real-world examples to illustrate what effective practice looks like on the ground. With clear guidance, expert insight, and a wealth of practical tools, The Analytics Lifecycle Toolkit is an essential resource for any organization seeking an optimized analytics program.

Predictive Analytics For Dummies
Predictive Analytics For Dummies

Author: Anasse Bari

Publisher: John Wiley & Sons

Published: 2014-03-06

Total Pages: 371

ISBN-13: 1118729412

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Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

Applying Predictive Analytics
Applying Predictive Analytics

Author: Richard V. McCarthy

Publisher: Springer Nature

Published: 2022-01-01

Total Pages: 282

ISBN-13: 3030830705

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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.

Applying Predictive Analytics
Applying Predictive Analytics

Author: Richard V. McCarthy

Publisher: Springer

Published: 2019-03-12

Total Pages: 205

ISBN-13: 3030140385

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This textbook presents a practical approach to predictive analytics for classroom learning. It focuses 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 example of how business analytics have been used in various aspects of organizations to solve issue 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.

Data Science and Big Data Analytics
Data Science and Big Data Analytics

Author: EMC Education Services

Publisher: John Wiley & Sons

Published: 2015-01-05

Total Pages: 432

ISBN-13: 1118876059

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Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Data Analytics Initiatives
Data Analytics Initiatives

Author: Ondřej Bothe

Publisher: CRC Press

Published: 2022-04-20

Total Pages: 169

ISBN-13: 1000629341

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The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.

Network Data Analytics
Network Data Analytics

Author: K. G. Srinivasa

Publisher: Springer

Published: 2018-04-26

Total Pages: 406

ISBN-13: 3319778005

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In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

Business Analytics Using R - A Practical Approach
Business Analytics Using R - A Practical Approach

Author: Umesh R Hodeghatta

Publisher: Apress

Published: 2016-12-27

Total Pages: 291

ISBN-13: 1484225147

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Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.