Amazon knows the products we’re interested in and shows us more to boost the size of our shopping cart. Google Maps knows the best route to get from A to B and recommends it to get us there in the shortest possible time. Netflix knows the media we enjoy most and recommends more to boost streaming time. However, many companies still fall short with their data analytics practices. This book focuses on how to embed advanced analytics directly into daily business operations and complement an enterprise system. This book can guide you in how traditional industries like retailers, banks and insurers can utilise and develop advanced analytics complementing their enterprise systems while embedding advanced analytics directly to optimise revenue and customer experience. Detailed in this book is a world-class analytics application used by loyalty point providers, banks, insurers and leading retailers. The title also provides a step-by-step implementation framework for Chief Digital and Artificial Intelligence Offices to develop their advanced analytics capabilities in tandem with legacy IT systems.
Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of R About This Book A comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and R Combine Tableau analytics and visualization with the power of R using this step-by-step guide Wondering how R can be used with Tableau? This book is your one-stop solution. Who This Book Is For This book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau. A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book. What You Will Learn Integrate Tableau's analytics with the industry-standard, statistical prowess of R. Make R function calls in Tableau, and visualize R functions with Tableau using RServe. Use the CRISP-DM methodology to create a roadmap for analytics investigations. Implement various supervised and unsupervised learning algorithms in R to return values to Tableau. Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions. In Detail Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples. Style and approach Tableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.
Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining. Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server, Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. You will learn to: Describe distribution of variables with statistical measures Find associations between pairs of variables Evaluate the quality of the data you are analyzing Perform time-series analysis on your data Forecast values of a continuous variable Perform market-basket analysis to predict customer purchasing patterns Predict target variable outcomes from one or more input variables Categorize passages of text by extracting and analyzing keywords.
Advanced Analytics Methodologies is today's definitive guide to analytics implementation for MBA and university-level business students and sophisticated practitioners. Its expanded, cutting-edge coverage helps readers systematically "jump the gap" between their organization's current analytical capabilities and where they need to be. Step by step, Michele Chambers and Thomas Dinsmore help readers customize a complete roadmap for implementing analytics that supports unique corporate strategies, aligns with specific corporate cultures, and serves unique customer and stakeholder communities. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore provide advanced applications and examples not available elsewhere, describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. They show how to: 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 This advanced text will serve the needs of students and faculty studying cutting-edge analytics techniques, as well as experienced analytics leaders and professionals 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 many line-of-business executives.
Plain English guidance for strategic business analytics and big data implementation In today's challenging economy, business analytics and big data have become more and more ubiquitous. While some businesses don't even know where to start, others are struggling to move from beyond basic reporting. In some instances management and executives do not see the value of analytics or have a clear understanding of business analytics vision mandate and benefits. Win with Advanced Analytics focuses on integrating multiple types of intelligence, such as web analytics, customer feedback, competitive intelligence, customer behavior, and industry intelligence into your business practice. Provides the essential concept and framework to implement business analytics Written clearly for a nontechnical audience Filled with case studies across a variety of industries Uniquely focuses on integrating multiple types of big data intelligence into your business Companies now operate on a global scale and are inundated with a large volume of data from multiple locations and sources: B2B data, B2C data, traffic data, transactional data, third party vendor data, macroeconomic data, etc. Packed with case studies from multiple countries across a variety of industries, Win with Advanced Analytics provides a comprehensive framework and applications of how to leverage business analytics/big data to outpace the competition.
Written by an in-the-trenches practitioner, this step-by-step guide shows you how to implement a successful Web analytics strategy. Web analytics expert Avinash Kaushik, in his thought-provoking style, debunks leading myths and leads you on a path to gaining actionable insights from your analytics efforts. Discover how to move beyond clickstream analysis, why qualitative data should be your focus, and more insights and techniques that will help you develop a customer-centric mindset without sacrificing your company’s bottom line. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Data Analytics - 7 BOOK BUNDLE!! Book 1: Data Analytics For Beginners In this book you will learn: What is Data Analytics Types of Data Analytics Evolution of Data Analytics Big Data Defined Data Mining Data Visualization Cluster Analysis And of course much more! Book 2: Deep Learning With Keras In this book you will learn: Deep Neural Network Neural Network Elements Keras Models Sequential Model Functional API Model Keras Layers Core Keras Layers Convolutional Keras Layers Recurrent Keras Layers Deep Learning Algorithms Supervised Learning Algorithms Applications of Deep Learning Models Automatic Speech and Image Recognition Natural Language Processing And of course much more! Book 3: Analyzing Data With Power BI In this book you will learn: Basics of data analysis processes Fundamental data analysis algorithms Basic of data and text mining, data visualization, and business intelligence Techniques used for analysing quantitative data Basic data analysis tasks Conceptual, logical, and physical data models Power BI service and data modelling Creating reports and visualizations in Power BI And of course much more! Book 4: Reinforcement Learning With Python In this book you will learn: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search And much, much more... Book 5: Artificial Intelligence Python In this book you will learn: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning And much, much more... Book 6: Text Analytics With Python In this book you will learn: Text analytics process How to build a corpus and analyze sentiment Named entity extraction with Groningen meaning bank corpus How to train your system Getting started with NLTK How to search syntax and tokenize sentences Automatic text summarization Stemming word and topic modeling with NLTK And much, much more... Book 7: Convolutional Neural Networks In Python In this book you will learn: Architecture of convolutional neural networks Solving computer vision tasks using convolutional neural networks Python and computer vision Automatic image and speech recognition Theano and TenroeFlow image recognition And of course much more! Download this book bundle NOW and SAVE money!!
Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.
Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics... how to become one of those deciders... and how to identify, foster, support, empower, and reward others to join you. Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes: How analytical and conventional decision making differ — and the challenging implications How to determine who your analytics deciders are, and ought to be Proven best practices for actually applying analytics to decision-making How to optimize your use of analytics as an analyst, manager, executive, or C-level officer Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.
