Marketing Data Science

Modeling Techniques in Predictive Analytics with R and Python
Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887340
Category: Business & Economics
Page: 225
View: 1118

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Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science
Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Category: Computers
Page: 448
View: 3740

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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R
Author: Thomas W. Miller
Publisher: Pearson Education
ISBN: 0133886018
Category: Business & Economics
Page: 359
View: 7609

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Today, successful firms win by understanding their data more deeply than competitors do. In short, they compete based on analytics. Now, in Modeling Techniques in Predictive Analytics, the leader of Northwestern University's prestigious analytics program brings together all the concepts, techniques, and R code you need to excel in analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike.--

Sports Analytics and Data Science

Winning the Game with Methods and Models
Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887413
Category: Computers
Page: 225
View: 3313

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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. 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. 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.

Web and Network Data Science

Modeling Techniques in Predictive Analytics
Author: Thomas W. Miller
Publisher: Pearson Education
ISBN: 0133886441
Category: Business & Economics
Page: 355
View: 4163

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Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University's prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

R for Marketing Research and Analytics


Author: Chris Chapman,Elea McDonnell Feit
Publisher: Springer
ISBN: 3319144367
Category: Business & Economics
Page: 454
View: 7649

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This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

Predictive Marketing

Easy Ways Every Marketer Can Use Customer Analytics and Big Data
Author: Omer Artun,Dominique Levin
Publisher: John Wiley & Sons
ISBN: 1119037336
Category: Business & Economics
Page: 272
View: 4964

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Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience.

Sports Performance Measurement and Analytics

The Science of Assessing Performance, Predicting Future Outcomes, Interpreting Statistical Models, and Evaluating the Market Value of Athletes
Author: Lorena Martin
Publisher: FT Press
ISBN: 0134193881
Category: Computers
Page: 320
View: 6836

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A PRACTICAL, REAL-WORLD GUIDE TO ANALYTICS FOR THE 5 MAJOR SPORTS: FOOTBALL, BASKETBALL, BASEBALL, SOCCER, AND TENNIS GAIN A COMPETITIVE EDGE! This is the first real-world guide to building and using analytical models for measuring and assessing performance in the five major sports: football, basketball, baseball, soccer, and tennis. Unlike books that focus strictly on theory, this book brings together sports measurement and statistical analyses, demonstrating how to examine differences across sports as well as between player positions. This book will provide you with the tools for cutting-edge approaches you can extend to the sport of your choice. Expert Northwestern University data scientist, UC San Diego researcher, and competitive athlete, Lorena Martin shows how to use measures and apply statistical models to evaluate players, reduce injuries, and improve sports performance. You’ll learn how to leverage a deep understanding of each sport’s principles, rules, attributes, measures, and performance outcomes. Sports Performance Measurement and Analytics will be an indispensable resource for anyone who wants to bring analytical rigor to athletic competition: students, professors, analysts, fans, physiologists, coaches, managers, and sports executives alike. All data sets, extensive code, and additional examples are available for download at http://www.ftpress.com/martin/ What are the qualities a person must have to become a world-class athlete? This question and many more can be answered through research, measurement, statistics, and analytics. This book gives athletes, trainers, coaches, and managers a better understanding of measurement and analytics as they relate to sports performance. To develop accurate measures, we need to know what we want to measure and why. There is great power in accurate measures and statistics. Research findings can show us how to prevent injuries, evaluate strengths and weaknesses, improve team cohesion, and optimize sports performance. This book serves many readers. People involved with sports will gain an appreciation for performance measures and analytics. People involved with analytics will gain new insights into quantified values representing physical, physiological, and psychological components of sports performance. And students eager to learn about sports analytics will have a practical introduction to the field. This is a thorough introduction to performance measurement and analytics for five of the world’s leading sports. The only book of its kind, it offers a complete overview of the most important concepts, rules, measurements, and statistics for each sport, while demonstrating applications of real-world analytics. You’ll find practical, state-of-the-art guidance on predicting future outcomes, evaluating an athlete’s market value, and more.

Statistical Modeling and Analysis for Database Marketing

Effective Techniques for Mining Big Data
Author: Bruce Ratner
Publisher: CRC Press
ISBN: 9780203496909
Category: Business & Economics
Page: 384
View: 3756

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Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models. By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.

Cutting-edge Marketing Analytics

Real World Cases and Data Sets for Hands on Learning
Author: Rajkumar Venkatesan,Paul Farris,Ronald T. Wilcox
Publisher: Pearson Education
ISBN: 0133552527
Category: Business & Economics
Page: 300
View: 7291

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Master practical strategic marketing analysis through real-life case studies and hands-on examples. In Cutting Edge Marketing Analytics, three pioneering experts integrate all three core areas of marketing analytics: statistical analysis, experiments, and managerial intuition. They fully detail a best-practice marketing analytics methodology, augmenting it with case studies that illustrate the quantitative and data analysis tools you'll need to allocate resources, define optimal marketing mixes; perform effective analysis of customers and digital marketing campaigns, and create high-value dashboards and metrics. For each marketing problem, the authors help you: Identify the right data and analytics techniques Conduct the analysis and obtain insights from it Outline what-if scenarios and define optimal solutions Connect your insights to strategic decision-making Each chapter contains technical notes, statistical knowledge, case studies, and real data you can use to perform the analysis yourself. As you proceed, you'll gain an in-depth understanding of: The real value of marketing analytics How to integrate quantitative analysis with managerial sensibility How to apply linear regression, logistic regression, cluster analysis, and Anova models The crucial role of careful experimental design For all marketing professionals specializing in marketing analytics and/or business intelligence; and for students and faculty in all graduate-level business courses covering Marketing Analytics, Marketing Effectiveness, or Marketing Metrics

Marketing Analytics

Data-Driven Techniques with Microsoft Excel
Author: Wayne L. Winston
Publisher: John Wiley & Sons
ISBN: 1118417305
Category: Computers
Page: 720
View: 1590

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Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.

Handbook of Marketing Analytics

Methods and Applications in Marketing Management, Public Policy, and Litigation Support
Author: Natalie Mizik,Dominique M. Hanssens
Publisher: Edward Elgar Publishing
ISBN: 1784716758
Category:
Page: 712
View: 4329

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Marketing Science contributes significantly to the development and validation of analytical tools with a wide range of applications in business, public policy and litigation support. The Handbook of Marketing Analytics showcases the analytical methods used in marketing and their high-impact real-life applications. Fourteen chapters provide an overview of specific marketing analytic methods in some technical detail and 22 case studies present thorough examples of the use of each method in marketing management, public policy, and litigation support. All contributing authors are recognized authorities in their area of specialty.

Modern Analytics Methodologies

Driving Business Value with Analytics
Author: Michele Chambers,Thomas W Dinsmore
Publisher: Pearson Education
ISBN: 0133498832
Category: Computers
Page: 272
View: 1689

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Create a complete roadmap for capitalizing on analytics to grow topline revenue and build shareholder value in your unique organization! Modern Analytics Methodologies goes far beyond the classic Analytics Maturity Model to help you overcome the gaps between your current analytics capabilities and where you need to go. Pioneering analytics experts Michele Chambers and Thomas Dinsmore help you implement analytics that supports your strategy, aligns with your culture, and serves your customers and stakeholders. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. Writing for both professionals and students, they show how to: Leverage the convergence of macro trends ranging from "flattening" and "green" to Big Data and machine learning Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy Link key business objectives with core characteristics of your organization, value chain, and stakeholders Take advantage of game changing opportunities before competitors do Effectively integrate the managerial and operational aspects of analytics Measure performance with dashboards, scorecards, visualization, simulation, and more Prioritize and score prospective analytics projects Identify "Quick Wins" you can implement while you're planning for the long-term Build an effective Analytic Program Office to make your roadmap persistent Update and revise your roadmap for new needs and technologies Modern Analytics Methodologies will be an indispensable resource for any executive or professional concerned with analytics, including Chief Analytics Officers; Chief Data Officers; Chief Scientists; Chief Marketing Officers; Chief Risk Officers; Chief Strategy Officers; VPs of Analytics or Big Data; data scientists; business strategists; and line-of-business executives.

Data Science from Scratch

First Principles with Python
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."
ISBN: 1491904402
Category: BUSINESS & ECONOMICS
Page: 330
View: 319

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Advanced Customer Analytics

Targeting, Valuing, Segmenting and Loyalty Techniques
Author: Mike Grigsby
Publisher: Kogan Page Publishers
ISBN: 0749477164
Category: Business & Economics
Page: 264
View: 9666

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Advanced Customer Analytics provides a clear guide to the specific analytical challenges faced by the retail sector. The book covers the nature and scale of data obtained in transactions, relative proximity to the consumer and the need to monitor customer behaviour across multiple channels. The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modelling and discount strategies, as well as targeted marketing and loyalty design. A practical, no-nonsense approach to complex scenarios is taken throughout, breaking down tasks into easily digestible steps. The use of a fictional retail analyst 'Scott' helps to provide accessible examples of practice. Advanced Customer Analytics does not skirt around the complexities of this subject but offers conceptual support to steer retail marketers towards making the right choices for analysing their data.

Big Data Driven Supply Chain Management

A Framework for Implementing Analytics and Turning Information Into Intelligence
Author: Nada R. Sanders
Publisher: Pearson Education
ISBN: 0133762823
Category: Business & Economics
Page: 272
View: 9014

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Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance.

Practical Statistics for Data Scientists

50 Essential Concepts
Author: Peter Bruce,Andrew Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category: Computers
Page: 318
View: 5114

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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Architecting Experience

A Marketing Science and Digital Analytics Handbook
Author: Scot R. Wheeler
Publisher: World Scientific
ISBN: 9814678422
Category: Business & Economics
Page: 280
View: 5773

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"In a world with a seemingly infinite amount of content and scores of methods for consuming that content, marketing communication today is about appealing to individuals, person by person. Effectively appealing to customers requires delivery of brand experiences built on relevance and recognition of context. Just as in any conversation, delivering relevance in context requires understanding the person one is speaking with and shared environment. Wheeler answers the biggest question facing digital marketers today: "with an ever expanding array of digital touch points at one's disposal, how does one deliver content and experiences around one's brand that build relationships and drives results?" The quick answer to this is "through the application of data and analytics to drive highly relevant, contextual targeted content and adaptive experience", but since this answer is not as easy to achieve as it is to say, Architecting Experience has been designed to help readers develop the understanding of marketing data, technology and analytics required to make this happen."--

Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category: Computers
Page: 550
View: 4953

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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Financial Analytics with R


Author: Mark J. Bennett,Dirk L. Hugen
Publisher: Cambridge University Press
ISBN: 1107150752
Category: Business & Economics
Page: 390
View: 2309

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Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.