Books
Internet of Things and Data Analytics Handbook by
This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view. Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing for IoT projects. It provides readers with knowledge in planning, designing, and implementing IoT projects. The book is written by experts on the subject matter, including international experts from nine countries in the consumer and enterprise fields of IoT. The text starts with an overview and anatomy of IoT, ecosystem of IoT, communication protocols, networking, and available hardware, both present and future applications and transformations, and business models. The text also addresses big data analytics, machine learning, cloud computing, and consideration of sustainability that are essential to be both socially responsible and successful. Design and implementation processes are illustrated with best practices and case studies in action. In addition, the book: Examines cloud computing, data analytics, and sustainability and how they relate to IoT overs the scope of consumer, government, and enterprise applications Includes best practices, business model, and real-world case studies Hwaiyu Geng, P.E., is a consultant with Amica Research (www.AmicaResearch.org, Palo Alto, California), promoting green planning, design, and construction projects. He has had over 40 years of manufacturing and management experience, working with Westinghouse, Applied Materials, Hewlett Packard, and Intel on multi-million high-tech projects. He has written and presented numerous technical papers at international conferences. Mr. Geng, a patent holder, is also the editor/author of Data Center Handbook (Wiley, 2015).
Call Number: TK5105.8857 I61ISBN: 9781119173649Publication Date: 2017-01-10
A General Introduction to Data Analytics by
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors--noted experts in the field--highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text's two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
Call Number: QA276.4 M838ISBN: 9781119296249Publication Date: 2018-07-18
Analysis of Messy Data Volume 1 by
A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researchers analyze the kinds of data sets encountered in the real world. Written by two long-time researchers and professors, this second edition has been fully updated to reflect the many developments that have occurred since the original publication. New to the Second Edition Several modern suggestions for multiple comparison procedures Additional examples of split-plot designs and repeated measures designs The use of SAS-GLM to analyze an effects model The use of SAS-MIXED to analyze data in random effects experiments, mixed model experiments, and repeated measures experiments The book explores various techniques for multiple comparison procedures, random effects models, mixed models, split-plot experiments, and repeated measures designs. The authors implement the techniques using several statistical software packages and emphasize the distinction between design structure and the structure of treatments. They introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. Bringing a classic work up to date, this edition will continue to show readers how to effectively analyze real-world, nonstandard data sets.
Call Number: QA279 M654ISBN: 9781584883340Publication Date: 2009-03-02
Digital Marketing Analytics by
Distill Maximum Value from Your Digital Data! Do It Now! Why hasn't all that data delivered a whopping competitive advantage? Because you've barely begun to use it, that's why! Good news: neither have your competitors. It's hard! But digital marketing analytics is 100% doable, it offers colossal opportunities, and all of the data is accessible to you. Chuck Hemann and Ken Burbary will help you chop the problem down to size, solve every piece of the puzzle, and integrate a virtually frictionless system for moving from data to decision, action to results! Scope it out, pick your tools, learn to listen, get the metrics right, and then distill your digital data for maximum value for everything from R&D to customer service to social media marketing! Prioritize--because you can't measure and analyze everything Use analysis to craft experiences that profoundly reflect each customer's needs, expectations, and behaviors Measure real digital media ROI: sales, leads, and customer satisfaction Track the performance of all paid, earned, and owned digital channels Leverage digital data way beyond PR and marketing: for strategic planning, product development, and HR Start optimizing digital content in real time Implement advanced tools, processes, and algorithms for accurately measuring influence Make the most of surveys, focus groups, and offline research synergies Focus new marketing investments where they'll deliver the most value * Identify and understand your most important audiences across the digital ecosystem "Chuck and Ken lead marketers clearly and efficiently through the minefield of digital marketing measurement. And they do so with a lightness of touch and absence of jargon so rare in this overhyped, much-misunderstood ecosystem." -- Sam Knowles, Founder & MD of Insight Agents; author of Narrative by Numbers: How to Tell Powerful & Purposeful Stories with Data
Call Number: HF5415.1265 H487ISBN: 9780789759603Publication Date: 2018-05-09
Pandas for Everyone by
The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they're easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas' advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the "best" Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning
Call Number: QA76.9 D343 C518ISBN: 9780134546933Publication Date: 2017-12-26
Data Analytics by
Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data AnalyticsThe Comprehensive Beginner's Guide to Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business using the power of Data AnalyticsIs a book targeted for anyone interested in wanting a Comprehensive learning experience with data analytics, Furthermore, for people who want to improve the sales and revenue for their business, but needs a guide regarding Data Analytics, Then this book is perfect for you!Discover everything you need to know about with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data Analytics and let it a skill that you haveWith this book, you will improve your Date analytics skills: Overview of Date analytics. Application of Date analytics. Foundation of Date analytics. Mathematics and Analytics. Analysis and Analytics. Communicating Data Insights. Automated Data Services. Gathering data. Charts and Graphs. Measures of Central Tendency. Applying Data Analytics to Business and Industry. Importance of Business Intelligence. The Goals of Business Intelligence. How data analyst benefits from BI.And much more! Buy this book NOW to Learn Data Analytics with Practical Data Analytics and Statistical to empower and evolve any business, using the power of Data Analytics
Call Number: HD30.215 K92ISBN: 9781974459193Publication Date: 2017-08-11
E-books
Data Analytics by
If you are a manager who receives the results of any data analyst's work to help with your decision-making, this book is for you. Anyone playing a role in the field of analytics can benefit from this book as well. In the two decades the editors of this book spent teaching and consulting in the field of analytics, they noticed a critical shortcoming in the communication abilities of many analytics professionals. Specifically, analysts have difficulty in articulating in business terms what their analyses showed and what actionable recommendations were made. When analysts made presentations, they tended to lapse into the technicalities of mathematical procedures, rather than focusing on the strategic and tactical impact and meaning of their work. As analytics has become more mainstream and widespread in organizations, this problem has grown more acute. Data Analytics: Effective Methods for Presenting Results tackles this issue. The editors have used their experience as presenters and audience members who have become lost during presentation. Over the years, they experimented with different ways of presenting analytics work to make a more compelling case to top managers. They have discovered tried and true methods for improving presentations, which they share. The book also presents insights from other analysts and managers who share their own experiences. It is truly a collection of experiences and insight from academics and professionals involved with analytics. The book is not a primer on how to draw the most beautiful charts and graphs or about how to perform any specific kind of analysis. Rather, it shares the experiences of professionals in various industries about how they present their analytics results effectively. They tell their stories on how to win over audiences. The book spans multiple functional areas within a business, and in some cases, it discusses how to adapt presentations to the needs of audiences at different levels of management.
Call Number: HD30.2 D232ebISBN: 9781351973410Publication Date: 2019-02-18
Sharing Economy and Big Data Analytics by
The different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.
Call Number: HD2961 SE448ebISBN: 9781119694991Publication Date: 2020-01-09
Beginning Microsoft Power BI [electronic resource] : a practical guide to self-service data analytics by
Beginning Power BI is a hands-on, activity-based guide that takes you through the process of analyzing your data using the tools that that encompass the core of Microsoft's self-service BI offering.
Call Number: HD38.7 C592ebISBN: 9781484256206Publication Date: 2020-02-22- Sharing Economy and Big Data Analytics by
The different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.
Call Number: HD2961 SE448ebISBN: 9781119694991Publication Date: 2020-01-09
Adoption of Data Analytics in Higher Education Learning and Teaching by
The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all.
Call Number: LB2395.7 A239ebISBN: 9783030473921Publication Date: 2020-08-11
- Benefit communications: Getting started with data analytics. The article discusses the use of data analytics to create targeted benefit communications.
- Data-driven government. The article talks about the use of data analysis in local government and work of Mid-Atlantic StatNet (MASN) collaborative in it.
- Growth of data analysis zooms skyward. The article presents a report from the ABI Research on the increase in the data management and data analytics, along with information on the data analytics used to increase production, improve quality and reduce waste.
- The making of data commodities: Data analytics as an embedded process. This paper studies the process by which data are generated, managed, and assembled into tradable objects we call data commodities.
- Blockchain and the future of business data analytics. This paper presents a pencil-and-paper activity that can help students unfamiliar with blockchain-related technologies understand these systems, the inter-organizational databases that result from their use, and their potential impacts for the accounting profession.