Angebote zu "Analytics" (292 Treffer)

Data Analytics with Hadoop
16,99 € *
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Provides a solid introduction to the world of clustered computing and analytics with Hadoop, focusing on particular analyses users can build, the data warehousing techniques that Hadoop provides and the higher order data workflows it can produce. Original.

Anbieter: buecher.de
Stand: 21.06.2017
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Interview Questions in Business Analytics
29,74 € *
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Discover relevant questions-and detailed answers-to help you prepare for job interviews and break into the field of analytics. This book contains more than 200 questions based on consultations with hiring managers and technical professionals already working in analytics. Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want fills a gap in information on business analytics for job seekers. Bhasker Gupta, the founder and editor of Analytics India Magazine , has come up with more than 300 questions job applicants are likely to face in an interview. Covering data preparation, statistics, analytics implementation, as well as other crucial topics favored by interviewers, this book: Provides 300+ interview questions often asked by recruiters and hiring managers in global corporations Offers short and to-the-point answers to the depth required, while looking at the problem from all angles Provides a full range of interview questions for jobs ranging from junior analytics to senior data scientists and managers Offers analytics professionals a quick reference on topics in analytics Using a question-and-answer format from start to finish, Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want will help you grasp concepts sooner and with deep clarity. The book therefore also serves as a primer on analytics and covers issues relating to business implementation. You will learn about not just the how and what of analytics, but also the why and when. This book will thus ensure that you are well prepared for interviews-putting your dream job well within reach. Business analytics is currently one of the hottest and trendiest areas for technical professionals. With the rise of the profession, there is significant job growth. Even so, its not easy to get a job in the field, because you need knowledge of subjects such as statistics, databases, and IT services. Candidates must also possess keen business acumen. Whats more, employers cast a cold critical eye on all applicants, making the task of getting a job even more difficult. What Youll Learn The 300 questions in this book cover such topics as: • The different types of data used in analytics • How analytics are put to use in different industries • The process of hypothesis testing • Predictive vs. descriptive analytics • Correlation, regression, segmentation and advanced statistics • Predictive modeling Who This Book Is For Those aspiring to jobs in business analytics, including recent graduates and technical professionals looking for a new or better job. Job interviewers will also find the book helpful in preparing interview questions. Bhasker Gupta is a seasoned analytics professional with around a decade experience in providing analytics consulting to the financial and retail industry. He is the founder and editor of the hugely popular online analytics publication Analytics India Magazine (www.analyticsindiamag.com). Considered a thought leader in the area of analytics, Bhasker has deep roots in the whole analytics ecosystem.

Anbieter: ciando eBooks
Stand: 12.09.2016
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Data Mining and Predictive Analytics
120,99 € *
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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition : Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. He has published several books, including Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Wiley, 2007) and Discovering Knowledge in Data: An Introduction to Data Mining (Wiley, 2005). In addition to his scholarly work, Dr. Larose is a consultant in data mining and statistical analysis working with many high profile clients, including Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. Chantal D. Larose is a Ph.D. candidate in Statistics at the University of Connecticut. Her research focuses on the imputation of missing data and model-based clustering. She has taught undergraduate statistics since 2011, and is a statistical consultant for DataMiningConsultant.com, LLC.

Anbieter: ciando eBooks
Stand: 27.12.2016
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Cognitive Computing and Big Data Analytics
40,99 € *
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A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computings underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Todays cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. Discover how cognitive computing evolved from promise to reality Learn the elements that make up a cognitive computing system Understand the groundbreaking hardware and software technologies behind cognitive computing Learn to evaluate your own application portfolio to find the best candidates for pilot projects Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction. Judith S. Hurwitz is President and CEO of Hurwitz & Associates, a research and consulting firm focused on emerging technology, and is a leading strategy consultant. Marcia Kaufman is a Principal Analyst and COO of Hurwitz & Associates, with leadership in big data and advanced analytics, information management, and business strategy. Adrian Bowles is President and CEO of STORM Insights, Inc., a market analytics firm providing research and advisory services for buyers, sellers, and investors in emerging technology markets.

Anbieter: ciando eBooks
Stand: 22.08.2016
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Practical Business Analytics Using SAS - A Hand...
47,59 € *
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Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries. Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before. Shailendra Kadre is a senior IT and management consultant from Bangalore, India. He is the author of a 2011 Apress book, Going Corporate: A Geek’s Guide, which covers IT operations management and the business aspects of IT. He has more than 20 years of experience in manufacturing, IT delivery and operations, program management, pre-sales, enterprise sales, and business analytics. Currently, he is working with Hewlett-Packard India as a solutions consultant. His current interests include business analytics and enterprise printing solutions.Shailendra earned his master’s degree in mechanical engineering from the Indian Institute of Technology (IIT), Delhi. He lives in Bangalore with his wife Meenakshi, daughter Neha, and son Vivek. He can be contacted at shailendrakadre@gmail.com.

Anbieter: ciando eBooks
Stand: 22.08.2016
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Big Data In-Memory Analytics explained by SAP HANA
12,99 € *
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Research Paper (undergraduate) from the year 2015 in the subject Computer Science - Commercial Information Technology, grade: 1,0, University of Applied Sciences Ansbach, course: Wissentschaftliches Arbeiten, language: English, abstract: Nowadays, people produce large amounts of data with talking via smartphones, reading e-mails or using platforms to find the appropriate partner. Conventional technologies no longer cope with the increasing amount of data and come to their limits. Therefore new technologies of Big Data are required for data processing to overcome the data flood. At the beginning, this paper clarifies what Big Data is, the technologies of Big Data, how Big Data differs from Business Intelligence and a distinction is made between Data Warehouse and Business Intelligence. Furthermore, the theory of the Big Data technology in-memory analytics is explained and an implementation of this technology called SAP HANA is consulted and reviewed. In conclusion, the potential of in-memory analytics will be classified.

Anbieter: ciando eBooks
Stand: 27.03.2017
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Data Mining and Learning Analytics - Applicatio...
112,99 € *
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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data minings four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDMs emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education. Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications. Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010. Osmar R. Zaïane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.

Anbieter: ciando eBooks
Stand: 13.12.2016
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Data Mining and Learning Analytics - Applicatio...
112,99 € *
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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data minings four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDMs emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education. Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications. Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010. Osmar R. Zaïane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.

Anbieter: ciando eBooks
Stand: 13.12.2016
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Real-Time Analytics - Techniques to Analyze and...
36,99 € *
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Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data , expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The books recipe layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website. BYRON ELLIS is CTO of Spongecell, where he heads research and development. Previously the Chief Data Scientist for LivePerson and CTO at AdBrite, Ellis holds a Ph.D. in Statistics from Harvard University, and a B.S. in Cybernetics from UCLA. He presents sessions on real-time analytics at Strata and other major conferences.

Anbieter: ciando eBooks
Stand: 22.08.2016
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Data Science and Big Data Analytics - Discoveri...
49,99 € *
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Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification

Anbieter: ciando eBooks
Stand: 22.08.2016
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