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.
Chris Chapman is a Senior Quantitative Researcher at Google. He is also a member of the editorial board of Marketing Insights magazine and the Marketing Insights Council of the American Marketing Association, and has served as chair of the AMA Advanced Research Techniques Forum and AMA Analytics with Purpose conferences. He is an enthusiastic contributor to the quantitative marketing community, where he regularly presents innovations in strategic research and teaches workshops on R and analytic methods.
Elea McDonnell Feit is an Assistant Professor at the LeBow College of Business at Drexel University. Her research focuses on leveraging customer data to make better product design and advertising decisions, particularly when data is incomplete, unmatched or aggregated. Much of her career has focused on building bridges between academia and practice, most recently as a Fellow of the Wharton Customer Analytics Initiative. She enjoys making quantitative methods accessible to a broad audience and regularly gives popular practitioner tutorials on marketing analytics, in addition to teaching courses at LeBow in data-driven digital marketing and design of marketing experiments.
The book explores security zoning the network, with an emphasis on isolated entry points for various classes of access. It shows how to use open source tools to test network configurations for malware attacks, DDoS, botnet, rootkit and worm attacks, and concludes with tactics on how to prepare and execute a mediation schedule of the who, what, where, when, and how, when an attack hits.
Network security is a requirement for any modern IT infrastructure. Using Network Performance Security: Testing and Analyzing Using Open Source and Low-Cost Tools makes the network stronger by using a layered approach of practical advice and good testing practices.Offers coherent, consistent guidance for those tasked with securing the network within an organization and ensuring that it is appropriately testedFocuses on practical, real world implementation and testingEmploys a vetted "security testing by example" style to demonstrate best practices and minimize false positive testingGives practical advice for securing BYOD devices on the network, how to test and defend against internal threats, and how to continuously validate a firewall device, software, and configurationProvides analysis in addition to step by step methodologies
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Chris Chapman and Stephen Ward continue to educate the profession with this masterful exposition of the differences between, and the potentials for combinations of, risk, uncertainty and opportunity. Particularly welcome is the way they integrate this trio into the project lifecycle – the bedrock of project management control and organization.
—Peter W.G. Morris
Head of School and Professor of Construction and Project Management University College London
Chris Chapman and Stephen Ward’s books on Project Risk Management have been an essential part of my repertoire for twenty years, and they are top of my recommended reading for the courses I do on that subject. In this book they have enhanced their previous work to focus on uncertainty management and emphasise more strongly opportunities for improving project performance, rather then just identifying what can go wrong. A structured process is an essential part of managing project uncertainty, and their process is one of the most powerful. This book will be added to my repertoire.
Professor of Project Management, SKEMA Business School Lille
A profoundly important book. With How to Manage Project Opportunity and Risk, Chris Chapman and Stephen Ward take a good thing and make it better. Members of the project management profession have been influenced for years by their insights into project risk management. With this latest instalment the authors demonstrate that risk and uncertainty needn’t be dreaded; in fact, the reverse side of the ‘risk coin’ has always been opportunity. My sincere appreciation to Chapman and Ward for turning this particular coin over and showing readers, academic and practitioner alike, the opportunity embedded in managing projects.
—Jeffrey K. Pinto
Andrew Morrow and Elizabeth Lee Black Chair in Management of Technology Sam and Irene Black School of Business, Penn State Erie