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Google Analytics Breakthrough by Feras Alhlou; Shiraz Asif; Eric FettmanA complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool. Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations. Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channel Add event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updates Master Google Tag Manager for greater flexibility and process control in implementation Set up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-off Create audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunities Populate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your language Gain a more complete view of customer behavior with mobile app and cross-device tracking Incorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testing Improve data storytelling and foster analytics adoption in the enterprise Millions of organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis. By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne - and a foreword by Avinash Kaushik - enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.
Call Number: TK5105.885.G66 A43
Publication Date: 2016
Fraud Analytics by Bart Baesens; Wouter Verbeke; Veronique Van VlasselaerDetect 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.
Call Number: HV6691 .B34 2015
Publication Date: 2015
A Practitioner's Guide to Business Analytics by Randy BartlettThe 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 bioPharma 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, PhD, 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, TGaS 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, InfoLogix, 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 itinto actionable business insights. One implication of Big Data is that we need to adapt . . . quickly. 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 youdefine 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 examinationsof success stories and failures in analytics-baseddecision 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 everydaydecision 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''sGuide to Business Analytics.
Call Number: HD38.7 .B3697 2013
Publication Date: 2013
Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning with Real-World Applications by Joshua ChapmannComputers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms. In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry: Supervised Learning Algorithms K-Nearest Neighbour Na#65533;ve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Decision Trees
Call Number: Q325.5 .C43
Publication Date: 2017
Machine Learning: An introduction to supervised and unsupervised learning algorithms by Michael ColinsWe've all heard of AI (artificial intelligence) but what does machine learning really mean? The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages. Going to credit cards, transactions are secured by software that knows when fraudulent activities are going on. We currently have digital cameras that detect faces, personal assistant applications that are intelligent enough to learn voice commands. These are all applications based on machine learning! Cars are becoming equipped with accident prevention systems that are powered by machine learning algorithms. Machine learning is also widely used in scientific fields like bioinformatics and astronomy. In contrast to traditional computing, and due to the complexity of patterns that need to be detected, it is hard for a programmer to provide a fine-detailed specification on the execution of these tasks. So where do we start? How about key machine learning algorithms? These are algorithms that are used in the real world, and they give a wide spectrum of the different learning techniques. There are also different algorithms that are better suited for big data. The world has become increasingly connected, and as a result, and in many business applications, there is a lot of data and computation needed to learn different concepts. As you can imagine, the topic of machine learning, depending on the application, can be contained or wildly complex. This book will give you an overview of what machine learning is capable of and some basic algorithms to help you understand the fundamentals of the technology. Finally, how will the employment landscape going to be affected by machine learning in the near future? In later chapters of this book, we will talk about the skills that a you will need to have to work in a profession related to machine learning, and how each field might be affected by the age of computerization. The future is changing very quickly and professionals will need to adapt to ever-evolving technology if they want to stand a chance in keeping up with the joneses.
Call Number: Q325.5 .C65
Publication Date: 2017
Big Data at Work by Thomas H. DavenportGo ahead, be skeptical about big data. The author was--at first. When the term "big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics,Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind. Now, in clear, conversational language, Davenport explains what big data means--and why everyone in business needs to know about it.Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold. This book will help you understand: * Why big data is important to you and your organization * What technology you need to manage it * How big data could change your job, your company, and your industry * How to hire, rent, or develop the kinds of people who make big data work * The key success factors in implementing any big data project * How big data is leading to a new approach to managing analytics With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities--from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.
Call Number: HD38.7 .D379
Publication Date: 2014
Competing on Analytics: Updated, with a New Introduction by Thomas Davenport; Jeanne HarrisThe New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company's capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney's vacation experience, Google's HR, UPS's logistics, the Chicago Cubs' training methods, and Firewire Surfboards' customization. Additional new topics and research include: Data scientists and what they doBig data and the changes it has wroughtHadoop and other open-source software for managing and analyzing dataData products--new products and services based on data and analyticsMachine learning and other AI technologiesThe Internet of Things and its implicationsNew computing architectures, including cloud computingEmbedding analytics within operational systemsVisual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company's fortunes in the age of analytics and big data.
Call Number: HD38.7 .D38
Publication Date: 2017
Only Humans Need Apply by Thomas H. Davenport; Julia KirbyAn invigorating, thought-provoking, and positive look at the rise of automation that explores how professionals across industries can find sustainable careers in the near future. Nearly half of all working Americans could risk losing their jobs because of technology. It's not only blue-collar jobs at stake. Millions of educated knowledge workers--writers, paralegals, assistants, medical technicians--are threatened by accelerating advances in artificial intelligence. The industrial revolution shifted workers from farms to factories. In the first era of automation, machines relieved humans of manually exhausting work. Today, Era Two of automation continues to wash across the entire services-based economy that has replaced jobs in agriculture and manufacturing. Era Three, and the rise of AI, is dawning. Smart computers are demonstrating they are capable of making better decisions than humans. Brilliant technologies can now decide, learn, predict, and even comprehend much faster and more accurately than the human brain, and their progress is accelerating. Where will this leave lawyers, nurses, teachers, and editors? In Only Humans Need Apply, Thomas Hayes Davenport and Julia Kirby reframe the conversation about automation, arguing that the future of increased productivity and business success isn't either human or machine. It's both. The key is augmentation, utilizing technology to help humans work better, smarter, and faster. Instead of viewing these machines as competitive interlopers, we can see them as partners and collaborators in creative problem solving as we move into the next era. The choice is ours.
Call Number: HC79.A9 D38
Publication Date: 2016
Business analytics : methods, models, and decisions by James R. EvansBusiness Analytics, Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today's organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Call Number: HD30.28 .E824
Publication Date: 2014
Business Analytics by Jay Liebowitz (Editor)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.
Call Number: Ebook
Publication Date: 2013
Effective Data Visualization by Stephanie D. H. EvergreenThis comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. The book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel. Available with Perusall--an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.
Call Number: P93.5 E937
Publication Date: 2016
Machine learning : the art and science of algorithms that make sense of data by Peter A. FlachAs one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
Call Number: Q325.5 .F53
Publication Date: 2012
Getting Started with Business Analytics by David Roi Hardoon; Galit ShmueliAssuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Makingexplores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.
Call Number: HD30.23 .H3656
Publication Date: 2013
Web Analytics 2. 0 by Avinash KaushikAdeptly 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. It provides specific recommendations for creating an actionable strategy, applying analytical techniques correctly, solving challenges such as measuring social media and multichannel campaigns, achieving optimal success by leveraging experimentation, and employing tactics for truly listening to your customers. 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.
Call Number: ZA4235 .K382
Publication Date: 2009
The inevitable : understanding the 12 technological forces that will shape our future by Kevin KellyA New York Times Bestseller From one of our leading technology thinkers and writers, a guide through the twelve technological imperatives that will shape the next thirty years and transform our lives Much of what will happen in the next thirty years is inevitable, driven by technological trends that are already in motion. In this fascinating, provocative new book, Kevin Kelly provides an optimistic road map for the future, showing how the coming changes in our lives--from virtual reality in the home to an on-demand economy to artificial intelligence embedded in everything we manufacture--can be understood as the result of a few long-term, accelerating forces. Kelly both describes these deep trends--interacting, cognifying, flowing, screening, accessing, sharing, filtering, remixing, tracking, and questioning--and demonstrates how they overlap and are codependent on one another. These larger forces will completely revolutionize the way we buy, work, learn, and communicate with each other. By understanding and embracing them, says Kelly, it will be easier for us to remain on top of the coming wave of changes and to arrange our day-to-day relationships with technology in ways that bring forth maximum benefits. Kelly's bright, hopeful book will be indispensable to anyone who seeks guidance on where their business, industry, or life is heading--what to invent, where to work, in what to invest, how to better reach customers, and what to begin to put into place--as this new world emerges. From the Hardcover edition.
Call Number: T173.8 .K45
Publication Date: 2017-06-06
Jumpstart Tableau by Arshad KhanLearn how to create powerful data visualizations easily and quickly. You will develop reports and queries, and perform data analysis. Jumpstart Tableau covers the basic reporting and analysis functions that most BI users perform in their day-to-day work. These include connecting to a data source, working with dimensions and measures, developing reports and charts, saving workbooks, filtering, swapping, sorting, formatting, grouping, creating hierarchies, forecasting, exporting, distributing, as well developing various chart types. Each exercise in Jumpstart Tableau provides screenshots that cover every step from start to finish. The exercises are based on a comprehensive sample Excel-based data source that Tableau Software (version 9) has provided, which makes it very easy to duplicate the exercises on the real software. This book teaches you to: Execute each function in a step-by-step manner Work up to more advanced and complex Tableau functionality Integrate individual development of content, such as tables/charts and visualizations., onto a dashboard for an effective presentation What You'll Learn Connect to data sources Develop reports Create visualizations Perform analysis functions (e.g., filtering, drilldown, sorting, grouping, forecasting, etc.) Save visualizations in different formats and distribute them Develop dashboards and their content Who This Book Is For Novice Tableau users, BI end users, as well as developers and business analysts. Also, students in university courses on dashboards and data visualization as well as BI and data analysis can quickly get up to speed with Tableau tools and use them for implementing the hands-on projects associated with these courses.
Call Number: QA76.9.I52 K43
Publication Date: 2016
Storytelling with Data: a data visualization guide for business professionals by Cole Nussbaumer KnaflicDon't simply show your data--tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!
Call Number: QA76.9.I52 .K534
Publication Date: 2015
Big Data by Bernard B. MarrConvert the promise of big data into real world results There is so much buzz around big data. We all need to know what it is and how it works - that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results - and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations, Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform. Discusses how companies need to clearly define what it is they need to know Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them Includes many high-profile case studies from the author's work with some of the world's best known brands
Call Number: HD30.2 .M3744
Publication Date: 2015
Key Business Analytics: the 60+ business analysis tools every manager needs to know by Bernard MarrKey Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities. It includes analysis techniques within the following categories: Financial analytics - cashflow, profitability, sales forecasts Market analytics - market size, market trends, marketing channels Customer analytics - customer lifetime values, social media, customer needs Employee analytics - capacity, performance, leadership Operational analytics - supply chains, competencies, environmental impact Bare business analytics - sentiments, text, correlations Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials: What is it? When should I use it? How do I use it? Tips and pitfalls Further reading This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc.
Call Number: HD62.15 .M37433
Publication Date: 2016
Data Science for Business by Foster Provost; Tom FawcettWritten by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization--and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you're to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
Call Number: QA76.9.D343 P76
Publication Date: 2013
Machine learning : the ultimate beginners guide to neural networks, algorithms, random forests and decision trees made simple by Ryan RobertsMachine Learning Sale price. You will save 66% with this offer. Please hurry up! The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple From smart bulbs to self-driving cars, intelligent machines are becoming ever more prevalent in our day to day lives. The underpinning of this technology is called machine learning, and is the same basic concept that is used by marketing experts to target ads on webpages and collect data about their customers.The uses for machine learning in today's world are vast and ever expanding. The technology is poised to revolutionize the way people interact with machines on a daily basis. Understanding just how these programs and processes function can help you to navigate this new technology. Here is a preview of what you'll learn: Just what machine learning is and why it's important Supervised versus unsupervised algorithms and the potential uses of each Description of some of the most popular machine learning algorithms The role of machine learning in programs like Cortana, Alexa, Siri, or Google assist If you're not familiar with the possibilities of machine learning, you'll be surprised to see the variety of ways it can be utilized beyond the much-publicized aspects like speech recognition. This book can be your first step into that larger world.Download your copy of " Machine Learning " by scrolling up and clicking "Buy Now With 1-Click" button. Tags: Machine Learning, Machine Learning Algorithms, Machine Learning Course, Big Data Machine Learning, Machine Learning For Dummies, Machine Learning Big Data, Machine Learning Tools, Machine Learning Basics, Machine Learning Online Course, Learn Machine Learning, Machine Learning As A Service, Cloud Machine Learning, Big Data And Machine Learning, Machine Learning And Big Data, Machine Learning Algorithms For Beginners, Machine Learning Platform, Data Science, Machine Learning Big Data Analytics, Machine Learning Companies, Ai Machine Learning, Machine Learning Cloud, Machine Learning Services
Call Number: Q325.5 .R63
Publication Date: 2017
Key Performance Indicators (KPI) by Bernard MarrBy identifying and describing the most powerful financial and non-financial KPIs, this book will make life easier for you by defining them, explaining how and when they should be used and providing a rich library of KPIs that have been proven to significantly improve performance. The book presents case examples to illustrate the selection and use of the KPIs and provides tools such as KPI selection templates and Key Performance Questions to help you apply the most appropriate KPIs effectively in your business.
Call Number: HF 5549.5 P37 M37
Publication Date: 2012
NoSQL Distilled: a brief guide to the emerging world of polyglot persistence by Martin J. Fowler; Pramod J. SadalageThe need to handle increasingly larger data volumes is one factor driving the adoption of a new class of nonrelational "NoSQL" databases. Advocates of NoSQL databases claim they can be used to build systems that are more performant, scale better, and are easier to program. NoSQL Distilled is a concise but thorough introduction to this rapidly emerging technology. Pramod J. Sadalage and Martin Fowler explain how NoSQL databases work and the ways that they may be a superior alternative to a traditional RDBMS. The authors provide a fast-paced guide to the concepts you need to know in order to evaluate whether NoSQL databases are right for your needs and, if so, which technologies you should explore further. The first part of the book concentrates on core concepts, including schemaless data models, aggregates, new distribution models, the CAP theorem, and map-reduce. In the second part, the authors explore architectural and design issues associated with implementing NoSQL. They also present realistic use cases that demonstrate NoSQL databases at work and feature representative examples using Riak, MongoDB, Cassandra, and Neo4j. In addition, by drawing on Pramod Sadalage's pioneering work, NoSQL Distilled shows how to implement evolutionary design with schema migration: an essential technique for applying NoSQL databases. The book concludes by describing how NoSQL is ushering in a new age of Polyglot Persistence, where multiple data-storage worlds coexist, and architects can choose the technology best optimized for each type of data access.
Call Number: QA76.9.D32 S228
Publication Date: 2012
Predictive Analytics: the power to predict who will click, buy, lie, or die by Eric Siegel; Thomas H. Davenport (Foreword by)"Mesmerizing & fascinating..." --The Seattle Post-Intelligencer "The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you''re going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world''s most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction -- now in its Revised and Updated edition -- former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death -- including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM''s Watson computer used predictive modeling to answer questions and beat the human champs on TV''s Jeopardy! How companies ascertain untold, private truths -- how Target figures out you''re pregnant and Hewlett-Packard deduces you''re about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics.
Call Number: H61.4 .S54
Publication Date: 2016
The Signal and the Noise: why so many predictions fail-- but some don't by Nate SilverOne of Wall Street Journal's Best Ten Works of Nonfiction in 2012 New York Times Bestseller "Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War...could turn out to be one of the more momentous books of the decade." --New York Times Book Review "Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century." --Rachel Maddow, author of Drift "A serious treatise about the craft of prediction--without academic mathematics--cheerily aimed at lay readers. Silver's coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism." --New York Review of Books Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger--all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good--or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary--and dangerous--science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver's insights are an essential read.
Call Number: CB158 .S54
Publication Date: 2015
Everybody Lies: big data, new data, and what the Internet can tell us about who we really are by Seth Stephens-DavidowitzNew York Times Bestseller Foreword by Steven Pinker, author of The Better Angels of our Nature Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world--provided we ask the right questions. By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information--unprecedented in history--can tell us a great deal about who we are--the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable. Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn't vote for Barack Obama because he's black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who's more self-conscious about sex, men or women? Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential--revealing biases deeply embedded within us, information we can use to change our culture, and the questions we're afraid to ask that might be essential to our health--both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.
Call Number: QA76.9.D343 S685155
Publication Date: 2017
Delivering Business Analytics: practical guidelines for best practice by Evan Stubbs; James FosterAVOID THE MISTAKES THAT OTHERS MAKE - LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist's Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue's solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist's Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.
Call Number: HD30.28 .S785
Publication Date: 2013
The Value of Business Analytics by Evan StubbsTURN YOUR CHALLENGES INTO SUCCESSES - LEARN HOW AND WHY SOME TEAM STRUGGLE AND SOME SUCCEED This groundbreaking resource defines what business analytics is, the immense value it brings to an organization, and how to harness its power to gain a competitive edge in the marketplace. Author Evan Stubbs provides managers with the tools, knowledge, and strategies to get the organizational commitment you need to get business analytics up and running in your company. Drawing from numerous practical examples, The Value of Business Analytics provides an overview of how business analytics maps to organizational strategy and through examining the mistakes teams commonly make that prevent their success, author Evan Stubbs uncovers a four-step framework which helps improve the odds of success. Built on field-tested experience, The Value of Business Analytics explains the importance of and how to: Define the Value: Link analytics outcomes to business value, thereby helping build a sense of urgency and a need for change. Communicate the Value: Persuade the right people by understanding what motivates them. Deliver the Value: Link tactical outcomes to long-term strategic differentiation. Measure the Value: Validate wins and deliver continuous improvement to help drive ongoing transformation. Translating massive amounts of data into real insight is beyond magic-it's competitive advantage distilled. Nothing else offers an equivalent level of agility, productivity improvement, or renewable value. Whether you're looking to quantify the value of your work or generate organizational support, learn how to leverage advanced business analytics with the hands-on guidance found in The Value of Business Analytics. Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a reference rich in content that spans everything from hiring the right people, understanding technical maturity, assessing culture, and structuring strategic planning. A must-read for any business analytics leader and an essential reference in shifting the perspective of business analytics away from algorithms towards outcomes. Learn how to increase the odds of successful value creation with The Value of Business Analytics.
Call Number: HD30.28 .S787
Publication Date: 2011
Envisioning Information by Edward R. TufteProvides practical advice about how to explain complex material by visual means, uses extraordinary examples to illustrate the fundamental principles of information display.
Call Number: P93.5 .T84
Publication Date: 1990
The Big Book of Dashboards by Steve Wexler; Andy Cotgreave; Jeffrey ShafferThe definitive reference book with real-world solutions you won't find anywhere else The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards. Comprising dozens of examples that address different industries and departments (healthcare, transportation, finance, human resources, marketing, customer service, sports, etc.) and different platforms (print, desktop, tablet, smartphone, and conference room display) The Big Book of Dashboards is the only book that matches great dashboards with real-world business scenarios. By organizing the book based on these scenarios and offering practical and effective visualization examples, The Big Book of Dashboards will be the trusted resource that you open when you need to build an effective business dashboard. In addition to the scenarios there's an entire section of the book that is devoted to addressing many practical and psychological factors you will encounter in your work. It's great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard 'cooler' by adding packed bubbles and donut charts? The expert authors have a combined 30-plus years of hands-on experience helping people in hundreds of organizations build effective visualizations. They have fought many 'best practices' battles and having endured bring an uncommon empathy to help you, the reader of this book, survive and thrive in the data visualization world. A well-designed dashboard can point out risks, opportunities, and more; but common challenges and misconceptions can make your dashboard useless at best, and misleading at worst. The Big Book of Dashboards gives you the tools, guidance, and models you need to produce great dashboards that inform, enlighten, and engage.
Call Number: HD30.213 .W43
Publication Date: 2017
Naked Statistics: stripping the dread from the data by Charles WheelanOnce considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called "sexy." From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you'll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan's trademark style, there's not a dull page in sight. You'll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let's Make a Deal--and you'll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
Call Number: QA276 .W458
Publication Date: 2014
Data Analytics: Practical guide to leveraging the power of algorithms, data science, data mining, statistics, big data, and predictive analysis to improve business, work, and life by Arthur ZhangThe Ultimate Guide to Data Science and Analytics This practical guide is accessible for the reader who is relatively new to the field of data analytics, while still remaining robust and detailed enough to function as a helpful guide to those already experienced in the field. Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater. RIGHT NOW you can get ahead of the pack! This coherent guide covers everything you need to know on the subject of data science, with numerous concrete examples, and invites the reader to dive further into this exciting field. Students from a variety of academic backgrounds, including computer science, business, engineering, statistics, anyone interested in discovering new ideas and insights derived from data can use this as a textbook. At the same time, professionals such as managers, executives, professors, analysts, doctors, developers, computer scientists, accountants, and others can use this book to make a quantum leap in their knowledge of big data in a matter of only a few hours. Learn how to understand this field and uncover actionable insights from data through analytics. UNDERSTAND the following key insights when you grab your copy today: WHY DATA IS IMPORTANT TO YOUR BUSINESS DATA SOURCES HOW DATA CAN IMPROVE YOUR BUSINESS HOW BIG DATA CREATES VALUE DEVELOPMENT OF BIG DATA CONSIDERING THE PROS AND CONS OF BIG DATA BIG DATA FOR SMALL BUSINESSES THE COST EFFECTIVENESS OF DATA ANALYTICS WHAT TO CONSIDER WHEN PREPARING FOR A NEW BIG DATA SOLUTION DATA GATHERING DATA SCRUBBING DESCRIPTIVE ANALYTICS INFERENTIAL STATISTICS PREDICTIVE ANALYTICS PREDICTIVE MODELS DESCRIPTIVE MODELING DECISION MODELING PREDICTIVE ANALYSIS METHODS MACHINE LEARNING TECHNIQUES DATA ANALYSIS WITH "R" ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT (CRM) THE USE OF PREDICTIVE ANALYTICS IN HEALTHCARE THE USE OF PREDICTIVE ANALYTICS IN THE FINANCIAL SECTOR PREDICTIVE ANALYTICS & BUSINESS MARKETING STRATEGIES FRAUD DETECTION SHIPPING BUSINESS CONTROLLING RISK FACTORS THE REVOLUTION OF PREDICTIVE ANALYSIS ACROSS A VARIETY OF INDUSTRIES DESCRIPTIVE AND PREDICTIVE ANALYSIS CRUCIAL FACTORS FOR DATA ANALYSIS RESOURCES AND FLEXIBLE TECHNICAL STRUCTURE BUSINESS INTELLIGENCE HYPER TARGETING WHAT IS DATA SCIENCE? DATA MUNGING DEMYSTIFYING DATA SCIENCE SECURITY RISKS TODAY BIG DATA AND IMPACTS ON EVERYDAY LIFE FINANCE AND BIG DATA APPLYING SENTIMENT ANALYSIS RISK EVALUATION AND THE DATA SCIENTIST THE FINANCE INDUSTRY AND REAL-TIME ANALYTICS HOW BIG DATA IS BENEFICIAL TO THE CUSTOMER CUSTOMER SEGMENTATION IS GOOD FOR BUSINESS USE OF BIG DATA BENEFITS IN MARKETING GOOGLE TRENDS THE PROFILE OF A PERFECT CUSTOMER LEAD SCORING IN PREDICTIVE ANALYSIS EVALUATING THE WORTH OF LIFETIME VALUE BIG DATA ADVANTAGES AND DISADVANTAGES MAKING COMPARISONS WITH COMPETITORS DATA SCIENCE IN THE TRAVEL SECTOR SAFETY ENHANCEMENTS THANKS TO BIG DATA BIG DATA AND AGRICULTURE BIG DATA AND LAW ENFORCEMENT THE USE OF BIG DATA IN THE PUBLIC SECTOR BIG DATA AND GAMING PRESCRIPTIVE ANALYTICS GOOGLE'S "SELF-DRIVING CAR" AND MUCH MORE! WANT MORE? Scroll up and grab this helpful guide toady!