THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy Analytical opportunities to solve key managerial problems in global enterprises Written for working managers: packed with realistic, useful examples This guide helps global managers use modern analytics to gain reliable, actionable, and timely business intelligence–and use it to manage risk, build winning strategies, and solve urgent problems. Dr. Hokey Min offers a practical, easy-to-understand overview of business analytics in a global context, focusing especially on managerial and strategic implications. After demystifying today’s core quantitative tools, he demonstrates them at work in a wide spectrum of global applications. You’ll build models to help segment global markets, forecast demand, assess risk, plan financing, optimize supply chains, and more. Along the way, you’ll find practical guidance for developing analytic thinking, operationalizing Big Data in global environments, and preparing for future analytical innovations. Whether you’re a global executive, strategist, analyst, marketer, supply chain professional, student or researcher, this book will help you drive real value from analytics–in smarter decisions, improved strategy, and better management. In today’s global business environments characterized by growing complexity, volatility, and uncertainty, business analytics has become an indispensable tool for managing these challenges. Specifically, global managers need analytics expertise to solve problems, identify opportunities, shape strategy, mitigate risk, and improve their day-to-day operational efficiency. Now, for the first time, there’s an analytics guide designed specifically for decision-makers in global organizations. Leveraging his experience teaching a number of students and training hundreds of managers and executives, Dr. Hokey Min demystifies the principles and tools of modern business analytics, and demonstrates their real-world use in global business. First, Dr. Min identifies key success factors and mindsets, helping you establish the preconditions for effective analysis. Next, he walks you through the practicalities of collecting, organizing, and analyzing Big Data, and developing models to transform them into actionable insight. Building on these foundations, he illustrates core analytical applications in finance, healthcare, and global supply chains. He concludes by previewing emerging trends in analytics, including the newest tools for automated decision-making. Compare today’s key quantitative tools Stats, data mining, OR, and simulation: how they work, when to use them Get the right data… …and get the data right Predict the future… …and sense its arrival sooner than others can Implement high-value analytics applications… …in finance, supply chains, healthcare, and beyond

Today's Complete, Focused, Up-to-Date Guide to Analytics for Ecommerce Profit from analytics throughout the entire customer experience and lifecycle Make the most of all the fast-changing data sources now available to you For all ecommerce executives, strategists, entrepreneurs, marketers, analysts, and data scientists Ecommerce Analytics is the only complete single-source guide to analytics for your ecommerce business. It brings together all the knowledge and skills you need to solve your unique problems, and transform your data into better decisions and customer experiences. Judah Phillips shows how to use analysis to improve ecommerce marketing and advertising, understand customer behavior, increase conversion rates, strengthen loyalty, optimize merchandising and product mix, streamline transactions, optimize product mix, and accurately attribute sales. Drawing on extensive experience leading large-scale analytics programs, he also offers expert guidance on building successful analytical teams; surfacing high-value insights via dashboards and visualization; and managing data governance, security, and privacy. Here are the answers you need to make the most of analytics in ecommerce: throughout your organization, across your entire customer lifecycle.

Dive into the world of regression analysis guided by Python in this comprehensive book. From simple linear regression to complex models, you'll gain a deep understanding of how to analyze data and predict outcomes. By the end of this book, you will be equipped with the skills to tidy data, build models, and apply regression techniques to real-world problems. What this Book will help me do Understand and format datasets to prepare them for regression analysis efficiently. Build and implement various regression models, such as linear and logistic regression, to solve data science problems. Develop techniques to combat overfitting and ensure predictive accuracy. Learn to scale and adapt regression models to large datasets and apply incremental learning. Apply the skills gained to make informed business decisions using predictive insights from regression models. Author(s) Luca Massaron and Alberto Boschetti are seasoned data professionals with years of expertise in data science, regression analysis, and Python programming. They are passionate about teaching and have crafted this book to demystify regression for learners interested in predictive analytics. Their approachable style ensures concepts are accessible yet comprehensive. Who is it for? This book is ideal for Python developers and data scientists who have a foundational knowledge of math and statistics. Whether you're looking to delve deeper into predictive modeling or efficiently analyze datasets, this book provides step-by-step guidance. If you've dabbled in data science and wish to expand your skillset to include regression analysis, this book is for you!

While big data has already made significant advances in business and government, data analytics is also beginning to transform education. This O’Reilly report explores how the use of analytics has already helped several educational programs, such as personalized learning and massive open online courses (MOOCs), for students of all ages. Of course, that’s only part of the story. As author Taylor Martin explains, researchers, educators, and private practitioners in the field have also run into several challenges in bringing the education field up to speed. Issues such as building data infrastructures, integrating data sources, and assuring student privacy still need to be resolved—as does the problem of teaching a new generation of data scientists about the challenges and opportunities unique to education. Download this report and find out what educators and analysts have accomplished so far, and how they hope data analytics will help improve outcomes for students, parents, schools, and teachers in the near future. Taylor Martin is a professor of Instructional Technology and Learning Sciences at Utah State University. She researches how people learn from active participation, both physical and social. Currently on rotation at the National Science Foundation, Dr. Martin focuses on a variety of efforts to understand how big data is impacting research in education and across the STEM disciplines.

Companies are collecting more data than ever. But, given how difficult it is to unify the many internal and external data streams they’ve built, more data doesn’t necessarily translate into better analytics. The real challenge is to provide deep and broad access to “a single source of truth” in their data that the typically slow ETL process for data warehousing cannot achieve. More than just fast access, analysts need the ability to explore data at a granular level. In this O’Reilly report, author Courtney Webster presents a roadmap to data centralization that will help your organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on your company’s ability to quickly act upon new findings. This report explains how. Identify stakeholders: build a culture of trust and awareness among decision makers, data analysts, and quality management Create a data plan: define your needs, specify your metrics, identify data sources, and standardize metric definitions Centralize the data: evaluate each data source for existing common fields and, if you can, minor variances, and standardize data references Find the right tool(s) for the job: choose from legacy architecture tools, managed and cloud-only services, and data visualization or data exploration platforms Courtney Webster is a reformed chemist in the Washington, D.C. metro area. She spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.

Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimise their internal processes and identify cost savings and growth opportunities.

This book dives into the practical application of R's power combined with Shiny's simplicity to build web-based analytics and interactive data summary tools. By following this step-by-step guide, you'll go from the basics of building with R and Shiny to creating sophisticated custom dashboards and interactive web apps. What this Book will help me do Create interactive web apps and dashboards using Shiny with impressive user interfaces. Integrate Shiny applications into custom HTML and CSS-based web pages for enhanced flexibility. Produce user-friendly Shiny applications extended with JavaScript and jQuery for added functionality. Develop web solutions that include interactive graphics, maps, and data analysis summaries. Deliver and deploy web apps securely using cloud solutions or self-hosted servers. Author(s) Chris Beeley, an experienced R developer and teacher, has a robust background in statistical programming and data analysis. Chris is passionate about sharing knowledge through practical examples and hands-on exercises. As the author of this book, Chris ensures that readers receive a clear and approachable entry into web application development using Shiny. Who is it for? This book is ideal for data enthusiasts, analysts, and developers looking to transition their analytic skills to the web. It caters to readers with basic programming knowledge but does not require prior experience with R or Shiny. It is perfect for professionals and learners wanting to create interactive analytics tools, dashboards, or data-driven web applications.

Transform your organization's data into actionable insights with Tableau Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data! is your Tableau companion, helping you get the most out of this invaluable business toolset. Tableau Your Data! shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You'll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sharing with the Tableau Server. Analyze data more effectively with Tableau Desktop Customize Tableau's settings for your organization's needs with detailed real-world examples on data security, scaling, syntax, and more Deploy visualizations to consumers throughout the enterprise - from sales to marketing, operations to finance, and beyond Understand Tableau functions and calculations and leverage Tableau across every link in the value chain Learn from actual working models of the book's visualizations and other web-based resources via a companion website Tableau helps you unlock the stories within the numbers, and Tableau Your Data! puts the software's full functionality right at your fingertips.

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

Aimed at econometricians who have completed at least one course in time series modeling, Multiple Time Series Modeling Using the SAS VARMAX Procedure will teach you the time series analytical possibilities that SAS offers today. Estimations of model parameters are now performed in a split second. For this reason, working through the identifications phase to find the correct model is unnecessary. Instead, several competing models can be estimated, and their fit can be compared instantaneously.

Consequently, for time series analysis, most of the Box and Jenkins analysis process for univariate series is now obsolete. The former days of looking at cross-correlations and pre-whitening are over, because distributed lag models are easily fitted by an automatic lag identification method. The same goes for bivariate and even multivariate models, for which PROC VARMAX models are automatically fitted. For these models, other interesting variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and error correction are easily addressed by PROC VARMAX.

One problem with multivariate modeling is that it includes many parameters, making parameterizations unstable. This instability can be compensated for by application of Bayesian methods, which are also incorporated in PROC VARMAX. Volatility modeling has now become a standard part of time series modeling, because of the popularity of GARCH models. Both univariate and multivariate GARCH models are supported by PROC VARMAX. This feature is especially interesting for financial analytics in which risk is a focus.

This book teaches with examples. Readers who are analyzing a time series for the first time will find PROC VARMAX easy to use; readers who know more advanced theoretical time series models will discover that PROC VARMAX is a useful tool for advanced model building.

"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 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. unleashes the power of data. With this technology Predictive Analytics , 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 — and Hillary for America 2016 plans to calculate — 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

Capitalise on big data to add value to your small business Written by bestselling author and big data expert Bernard Marr, Big Data For Small Business For Dummies helps you understand what big data actually is—and how you can analyse and use it to improve your business. Free of confusing jargon and complemented with lots of step-by-step guidance and helpful advice, it quickly and painlessly helps you get the most from using big data in a small business. Business data has been around for a long time. Unfortunately, it was trapped away in overcrowded filing cabinets and on archaic floppy disks. Now, thanks to technology and new tools that display complex databases in a much simpler manner, small businesses can benefit from the big data that's been hiding right under their noses. With the help of this friendly guide, you'll discover how to get your hands on big data to develop new offerings, products and services; understand technological change; create an infrastructure; develop strategies; and make smarter business decisions. Shows you how to use big data to make sense of user activity on social networks and customer transactions Demonstrates how to capture, store, search, share, analyse and visualise analytics Helps you turn your data into actionable insights Explains how to use big data to your advantage in order to transform your small business If you're a small business owner or employee, Big Data For Small Business For Dummies helps you harness the hottest commodity on the market today in order to take your company to new heights.

In The Patient Revolution, author Krisa Tailor—a noted expert in health care innovation and management—explores, through the lens of design thinking, how information technology will take health care into the experience economy. In the experience economy, patients will shift to being empowered consumers who are active participants in their own care. Tailor explores this shift by creating a vision for a newly designed health care system that's focused on both sickness and wellness, and is driven by data and analytics. The new system seamlessly integrates health into our daily lives, and delivers care so uniquely personalized that no two people are provided identical treatments. Connected through data, everyone across the health care ecosystem, including clinicians, insurers, and researchers, will be able to meet individuals wherever they are in their health journey to reach the ultimate goal of keeping people healthy. The patient revolution has just begun and an exciting journey awaits us. Praise for the patient revolution "A full 50% of the US population has at least one chronic disease that requires ongoing monitoring and treatment. Our current health care system is woefully inadequate in providing these individuals with the treatment and support they need. This disparity can only be addressed through empowering patients to better care for themselves and giving providers better tools to care for their patients. Both of those solutions will require the development and application of novel technologies. In Krisa Tailor's book The Patient Revolution, a blueprint is articulated for how this could be achieved, culminating in a vision for a learning health system within 10 years." —Ricky Bloomfield, MD, Director, Mobile Technology Strategy; Assistant Professor, Duke Medicine "In The Patient Revolution, Krisa Tailor astutely points out that 80% of health is impacted by factors outside of the health care system. Amazon unfortunately knows more about our patients than we do. The prescriptive analytics she describes will allow health care providers to use big data to optimize interventions at the level of the individual patient. The use of analytics will allow providers to improve quality, shape care coordination, and contain costs. Advanced analytics will lead to personalized care and ultimately empowered patients!" —Linda Butler, MD, Vice President of Medical Affairs/Chief Medical Officer/Chief Medical Information Officer, Rex Healthcare " The Patient Revolution provides a practical roadmap on how the industry can capture value by making health and care more personalized, anticipatory, and intuitive to patient needs." —Ash Damle, CEO, Lumiata "Excellent read. For me, health care represents a unique economy—one focused on technology, but requiring a deep understanding of humanity. Ms. Tailor begins the exploration of how we provide care via the concepts of design thinking, asking how we might redesign care with an eye toward changing the experience. She does an excellent job deconstructing this from the patient experience. I look forward to a hopeful follow-up directed at changing the provider culture." —Alan Pitt, MD, Chief Medical Officer, Avizia "Whether you're a health care provider looking to gain an understanding of the health care landscape, a health data scientist, or a seasoned business pro, you'll come away with a deeper, nuanced understanding of today's evolving health care system with this book. Krisa Tailor ties together—in a comprehensive, unique way—the worlds of health care administration, clinical practice, design thinking, and business strategy and innovation." —Steven Chan, MD, MBA, University of California, Davis

This comprehensive guide to Qlik Sense provides you with everything you need to harness its data visualization capabilities effectively in your business or organization. Covering essential techniques and insights, this book focuses on understanding, implementing, and optimizing Qlik Sense for various data discovery applications. What this Book will help me do Understand the purpose and vision behind Qlik Sense and how it revolutionizes data discovery. Gain practical knowledge to manage, load, and visualize your data effectively using Qlik Sense. Learn how to administer Qlik Sense systems, ensuring secure and efficient usage. Explore extending Qlik Sense capabilities through its Dev Hub and other advanced features. Apply Qlik Sense in practical contexts, such as sales analytics, HR insights, and demographic studies. Author(s) Henric Cronström and co-authors, with vast experience directly from QlikTech International AB, bring authoritative insights into this book. Their expertise in business analytics and direct involvement with the development of Qlik Sense make this book a reliable and insightful resource for learners. Who is it for? This book is ideal for business intelligence professionals, data analysts, and decision-makers looking to maximize the potential of Qlik Sense. Whether you're new to Qlik Sense or have a general understanding of BI concepts, this guide will help you elevate your data discovery skills and apply actionable insights to real-world scenarios.

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

THE DEFINITIVE GUIDE TO NEXTGENERATION DIGITAL MEASUREMENT: INDISPENSABLE INSIGHT FOR BUILDING HIGH-VALUE DIGITAL EXPERIENCES! Helps you capture the knowledge you need to deliver deep personalization at scale Reflects today’s latest insights into digital behavior and consumer psychology For every digital marketer, analyst, and executive who wants to improve performance To win at digital, you must capture the right data, quickly transform it into the right knowledge,and use them both to deliver deep personalization at scale. Conventional digital metrics simply aren’t up to the task. Now, Gary Angel shows how to reinvent digital measurement so it delivers all you need to create richer, more compelling digital experiences. For more than a decade, Angel has helped leading global enterprises succeed at digital. This book reflects all he’s learned. You’ll find valuable guidance on understanding visitor intent… creating customer taxonomies… digital segmentation… integrating VoC research… and using behavioral analysis and controlled experiments to investigate what drives customer choice. Angel will help you measure the value of every digital interaction more accurately, identify specific digital behaviors that predict success, and create a comprehensive measurement paradigm that integrates all your digital spaces. With flawed tools and siloed analytics, you’re blind to what’s really happening online. But you don’t have to be. Gary Angel will help you make the invisible visible… actionable… profitable. Most common digital metrics are virtually useless. They measure the wrong things in the wrong ways. They don’t link digital activity to customer attitudes and behaviors. They don’t work well with today’s powerful analytics tools. Above all, they don’t help you improve your performance. Angel shows how to transform “raw facts” about digital behavior into meaningful knowledge about your visitors… what they were trying to accomplish…how well you helped them… how you can personalize and optimize their digital experiences from now on… how you can use measurement to provide deep personalization at scale. More rigorous, integrated, and usable than any competitive book, Measuring the Digital World will help you create, deliver, and consume digital information with unprecedented sophistication. Whether you’re a digital analyst, marketer, user experience designer, or executive, you’ll find it indispensable. Why conventional digital metrics are arbitrary and misguided Refocus on what you need to know, not what you don’t Capture the “why” Integrate VoC research and behavioral data to build better, richer, more accurate segments Go beyond snapshots: understand your customer’s entire digital experience Understand how your customers’ views and behaviors evolve over time Segment in three dimensions for a multichannel world Treat each channel as part of a larger, integrated, sequential journey

TO BUILD WINNING TEAMS AND SUCCESSFUL SPORTS BUSINESSES, GUIDE YOUR DECISIONS WITH DATA This up-to-the-minute reference will help you master all three facets of sports analytics – and use it to win! Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations—not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R and Python code, interpreting your results, and more. Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business. Whether you’re a team executive, coach, fan, fantasy player, or data scientist, this guide will be a powerful source of competitive advantage… in any sport, by any measure. All data sets, extensive R and Python code, and additional examples available for download at http://www.ftpress.com/miller/ This exceptionally complete and practical guide to sports data science and modeling teaches through realistic examples from sports industry economics, marketing, management, performance measurement, and competitive analysis. Thomas W. Miller, faculty director of Northwestern University’s pioneering Predictive Analytics program, shows how to use advanced measures of individual and team performance to judge the competitive position of both individual athletes and teams, and to make more accurate predictions about their future performance. Miller’s modeling techniques draw on methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming. Miller illustrates them through realistic case studies, with fully worked examples in both R and Python. Sports Analytics and Data Science will be an invaluable resource for everyone who wants to seriously investigate and more accurately predict player, team, and sports business performance, including students, teachers, sports analysts, sports fans, trainers, coaches, and team and sports business managers. It will also be valuable to all students of analytics and data science who want to build their skills through familiar and accessible sports applications Gain powerful, actionable insights for: Understanding sports markets Assessing players Ranking teams Predicting scores Making game day decisions Crafting marketing messages Promoting brands and products Growing revenues Managing finances Playing what-if games And much more

Whether you’re a marketer with development skills or a full-on web developer/analyst, Practical Google Analytics and Google Tag Manager for Developers shows you how to implement Google Analytics using Google Tag Manager to jumpstart your web analytics measurement. There’s a reason that so many organizations use Google Analytics. Effective collection of data with Google Analytics can reduce customer acquisition costs, provide priceless feedback on new product initiatives, and offer insights that will grow a customer or client base. So where does Google Tag Manager fit in? Google Tag Manager allows for unprecedented collaboration between marketing and technical teams, lightning fast updates to your site, and standardization of the most common tags for on-site tracking and marketing efforts. To achieve the rich data you're really after to better serve your users’ needs, you'll need the tools Google Tag Manager provides for a best-in-class implementation of Google Analytics measurement on your site. Written by data evangelist and Google Analytics expert Jonathan Weber and the team at LunaMetrics, this book offers foundational knowledge, a collection of practical Google Tag Manager recipes, well-tested best practices, and troubleshooting tips to get your implementation in tip-top condition. It covers topics including: • Google Analytics implementation via Google Tag Manager • How to customize Google Analytics for your unique situation • Using Google Tag Manager to track and analyze interactions across multiple devices and touch points • How to extract data from Google Analytics and use Google BigQuery to analyze Big Data questions

More than ever, data drives decisions in organizations—and we have more data, and more ways to analyze it, than ever. Yet strategic initiatives continue to fail as often as they did when computers ran on punch cards. Economist and research scientist Alec Levenson says we need a new approach. The problem, Levenson says, is that the business people who devise the strategies and the human resources people who get employees to implement them use completely different analytics. Business analytics can determine if operational priorities aren't being achieved but can't explain why. HR analytics reveal potentially helpful policy and process improvements but can't identify which would have the greatest strategic impact. This book shows how to use an integrated approach to bring these two pieces together. Levenson presents a thorough and realistic treatment of the reasons for and challenges of taking an integrated approach. He provides details on the different parts of both enterprise and human capital analytics that have to be conducted for integration to be successful and includes specific questions to ask, along with examples of applying integrated analytics to address particular organizational challenges. Effective analytics is a team sport. Levenson's approach allows you to get the deepest insights by bringing people together from both the business and HR perspectives to assess what's going on and determine the right solution.