Author: Himanshu Sharma Publisher: Blurb ISBN: 9781364849184 Category : Computers Languages : en Pages : 430
Book Description
The role of maths and statistics in the world of web analytics is not clear to many marketers. Not many talk or write about the usage of statistics and data science in conversion optimization. This book has been written to fill this knowledge gap.This expert guide will teach you exactly what you need to know. It will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions which can quickly improve the bottom-line of your online business. Every topic covered in this book has something to do directly with your day to day job. This book covers vital topics on maths and statistics which every internet marketer/web analyst should get familiar with in order to achieve optimum results from their analysis, marketing campaigns and conversion optimization efforts. It explains some of the most useful statistics terms/concepts one by one and will also show you their practical use in web analytics and conversion optimization, so that you can take advantage of them straightaway. "
Author: Himanshu Sharma Publisher: Blurb ISBN: 9780368079610 Category : Languages : en Pages : 478
Book Description
Attribution modelling is the process of understanding the customer purchasing journey and determining the most effective marketing channels / campaigns for investment at a particular point in time. A lot has been said about attribution modelling over the years. However, talking about attribution is the easy bit, implementing it is the real challenge. This book has been written to help you implement attribution modelling in Google Ads (formerly Google AdWords) and Facebook. This expert guide will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and to determine the most effective marketing channels for investment.
Author: Tilman M. Davies Publisher: No Starch Press ISBN: 1593276516 Category : Computers Languages : en Pages : 833
Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Author: Himanshu Sharma Publisher: Blurb ISBN: 9781006493706 Category : Languages : en Pages : 480
Book Description
Attribution modelling is the process of understanding and assigning conversion credit to marketing channels. The primary objective of attribution modelling is to understand the buying behaviour of your website visitors and to determine the most effective marketing channels for investment at a particular point in time. A lot has been said about attribution modelling over the years. However, talking about attribution is the easy bit. Implementing it is the real challenge. This book has been written to help you implement attribution modelling in your organisation. This expert guide will help your organisation think about marketing holistically. It will teach you to leverage the knowledge of attribution modelling while allocating your marketing budget and helping you understand your users' buying behaviour. In this book, there is a strong focus on using Google Analytics and other Google tools and technologies, such as Google Ads (AdWords). I have explained various attribution models mainly in the context of Google Analytics. However, a large portion of this book does not deal with Google Analytics at all. So even if you have never used Google Analytics before, you can still benefit from this book. To get the maximum benefit from this book, you will need a working knowledge of Google Analytics and ecommerce. No knowledge of statistics or traditional marketing mix modelling is required. Any person who wants to improve the online performance of their business and marketing campaigns should read this book. Online marketers, web analysts, and data scientists will benefit the most from this book. If you have a strong background in traditional marketing mix modelling you will benefit tremendously from this book because it will enable you to integrate attribution modelling data with your MMM data. By feeding the attribution modelling data to your MMM model you can truly measure your overall marketing effectiveness and fix attribution issues.
Author: Marc Peter Deisenroth Publisher: Cambridge University Press ISBN: 1108569323 Category : Computers Languages : en Pages : 392
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Pablo Moscato Publisher: Springer ISBN: 3030062228 Category : Computers Languages : en Pages : 1000
Book Description
This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems. Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields. Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying these fields will find this handbook useful and helpful as a secondary textbook.
Author: Shai Shalev-Shwartz Publisher: Cambridge University Press ISBN: 1107057132 Category : Computers Languages : en Pages : 415
Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Peter Bruce Publisher: "O'Reilly Media, Inc." ISBN: 1491952911 Category : Computers Languages : en Pages : 322
Book Description
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
Author: Alvin C. Rencher Publisher: John Wiley & Sons ISBN: 0470192607 Category : Mathematics Languages : en Pages : 690
Book Description
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
Author: Clo Willaerts Publisher: Lannoo Meulenhoff - Belgium ISBN: 9401490767 Category : Business & Economics Languages : en Pages : 200
Book Description
How do you translate your business objectives into a digital marketing strategy? What are today's best practices for content marketing, email marketing, social media and online advertising? What is the practical use for new technologies like Augmented and Virtual Reality, the Internet of Things, Artificial Intelligence and chatbots? Digital Marketing like a PRO is an easy to follow, step-by-step marketing guide that helps you maximize all digital channels. Packed with models, information and examples to help you succeed, it is free of fluff and noise so you can get to work quickly. Whether you're new to marketing or a long-time professional seeking to boost your skills, this digital marketing handbook will help you save time, energy and resources as you learn from tested industry models and best practices.
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Author: Himanshu Sharma
Author: Himanshu Sharma
Author: Tilman M. Davies
Author: Himanshu Sharma
Author: Marc Peter Deisenroth
Author: Pablo Moscato
Author: Shai Shalev-Shwartz
Author: Peter Bruce
Author: Alvin C. Rencher
Author: Clo Willaerts