Business Intelligence Techniques
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| Author | : Murugan Anandarajan |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 271 |
| Release | : 2012-11-02 |
| Genre | : Business & Economics |
| ISBN | : 3540247009 |
Modern businesses generate huge volumes of accounting data on a daily basis. The recent advancements in information technology have given organizations the ability to capture and store data in an efficient and effective manner. However, there is a widening gap between this data storage and usage of the data. Business intelligence techniques can help an organization obtain and process relevant accounting data quickly and cost efficiently. Such techniques include: query and reporting tools, online analytical processing (OLAP), statistical analysis, text mining, data mining, and visualization. Business Intelligence Techniques is a compilation of chapters written by experts in the various areas. While these chapters stand on their own, taken together they provide a comprehensive overview of how to exploit accounting data in the business environment.
| Author | : Galit Shmueli |
| Publisher | : John Wiley & Sons |
| Total Pages | : 608 |
| Release | : 2019-10-14 |
| Genre | : Mathematics |
| ISBN | : 111954985X |
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
| Author | : Vasant Dhar |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1997 |
| Genre | : Artificial intelligence |
| ISBN | : 9780132820066 |
Information systems: past, present, and emerging; Intelligence density a metric for knowledge work; The vocabulary of intelligence density; Method one: data-driven decision support; Method two: evolving solutions: genetic algorithms; Method three: simulating the brain to solve problems: neural networks; Method four: putting expert resoning in a box: rule-based systems; Method five: dealing with linguistic ambiguity: fuzzy logic; Method six: soilving problems by analogy case-based resoning; Method seven: deriving rules from data: machine learning; Appendix saving time and money with object; Appendix case studies.
| Author | : David Loshin |
| Publisher | : Newnes |
| Total Pages | : 401 |
| Release | : 2012-11-27 |
| Genre | : Computers |
| ISBN | : 0123858909 |
Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. - Guides managers through developing, administering, or simply understanding business intelligence technology - Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge - Contains a handy, quick-reference to technologies and terminology
| Author | : Wang, Jue |
| Publisher | : IGI Global |
| Total Pages | : 405 |
| Release | : 2010-06-30 |
| Genre | : Computers |
| ISBN | : 1615206302 |
With the rapid development of economic globalization and information technology, the field of economic forecasting continues its expeditious advancement, providing business and government with applicable technologies. This book discusses various business intelligence techniques including neural networks, support vector machine, genetic programming, clustering analysis, TEI@I, fuzzy systems, text mining, and many more. It serves as a valuable reference for professionals and researchers interested in BI technologies and their practical applications in economic forecasting, as well as policy makers in business organizations and governments.
| Author | : Wilfried Grossmann |
| Publisher | : Springer |
| Total Pages | : 361 |
| Release | : 2015-06-02 |
| Genre | : Computers |
| ISBN | : 3662465310 |
This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.
| Author | : Marleen Meier |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 793 |
| Release | : 2021-05-31 |
| Genre | : Computers |
| ISBN | : 1800560745 |
Build, design, and improve advanced business intelligence solutions using Tableau's latest features, including Tableau Prep Builder, Tableau Hyper, and Tableau Server Key FeaturesMaster new features in Tableau 2021 to solve real-world analytics challengesPerform geo-spatial, time series, and self-service analytics using real-life examplesBuild and publish dashboards and explore storytelling using Python and R integration supportBook Description Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you'll be able to perform data preparation and handling easily. You'll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you'll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You'll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cover advanced use cases such as self-service analysis, time series analysis, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it. By the end of this Tableau book, you'll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain. What you will learnGet up to speed with various Tableau componentsMaster data preparation techniques using Tableau Prep BuilderDiscover how to use Tableau to create a PowerPoint-like presentationUnderstand different Tableau visualization techniques and dashboard designsInteract with the Tableau server to understand its architecture and functionalitiesStudy advanced visualizations and dashboard creation techniquesBrush up on powerful self-service analytics, time series analytics, and geo-spatial analyticsWho this book is for This book is designed for business analysts, business intelligence professionals and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. The book is ideal if you have a good understanding of Tableau and want to take your skills to the next level.
| Author | : G., Dileep Kumar |
| Publisher | : IGI Global |
| Total Pages | : 300 |
| Release | : 2018-07-06 |
| Genre | : Business & Economics |
| ISBN | : 1522535357 |
Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations. Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.
| Author | : Charles Halliman |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2001 |
| Genre | : Business intelligence |
| ISBN | : 9780967490625 |
| Author | : Sreedhar, G. |
| Publisher | : IGI Global |
| Total Pages | : 379 |
| Release | : 2018-02-02 |
| Genre | : Business & Economics |
| ISBN | : 1522536477 |
As the Internet becomes increasingly interconnected with modern society, the transition to online business has developed into a prevalent form of commerce. While there exist various advantages and disadvantages to online business, it plays a major role in contemporary business methods. Improving E-Commerce Web Applications Through Business Intelligence Techniques provides emerging research on the core areas of e-commerce web applications. While highlighting the use of data mining, search engine optimization, and online marketing to advance online business, readers will learn how the role of online commerce is becoming more prevalent in modern business. This book is an important resource for vendors, website developers, online customers, and scholars seeking current research on the development and use of e-commerce.
