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Business Analytics (2nd Edition) Fix


Leida Chen is a professor of information systems at California Polytechnic State University in San Luis Obispo. Dr. Chen earned a Ph.D. in Management Information Systems from University of Memphis. His research and consulting interests are in the areas of business analytics, technology diffusion, and global information systems. Dr. Chen has published over 50 research articles in leading information systems journals, over 30 articles and book chapters in national and international conference proceedings and edited books, and a book on mobile application development. He teaches business analytics at both the undergraduate and graduate levels. In his spare time, Dr. Chen enjoys hiking, painting, and traveling with his wife and son to interesting places around the world.




Business Analytics (2nd Edition)


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Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning. Coverage includes data warehousing, big data, social media, security, cloud technologies, and future trends, with expert insight on the practical aspects of the current state of the field.


Analytics helps businesses move forward. Extensive use of statistical and quantitative analysis alongside explanatory and predictive modeling facilitates fact-based decision making, and evolving technologies continue to streamline every step of the process. This book provides an essential update, and describes how today's tools make business analytics more valuable than ever.


This edition is supported with 72 learning activity videos, which will provide more in-depth discussions about the topics featured within the manuscript. In addition, starting files are provided with the data necessary for you to work along with the videos to complete the activities presented. This hands-on approach will allow you to actively practice and further develop skills related to business analytics as you watch the videos at your own pace.


JESPER THORLUND is a business intelligence consultant and frequent speaker on business intelligence, business analytics, and microeconomics throughout Europe. Permissions Request permission to reuse content from this site


Overview: Practical Analytics, 2nd ed. explains analytics concepts and activities in a way that provides real-world skill building while reinforcing fundamental concepts. This book provides a much needed approach to analytics through theory, applications, and hands-on experience using the latest industry tools. Although many books have been written on statistical data analysis, data mining, predictive analytics and business intelligence, these books are often too technical for a business user. The goal of this book is to provide a comprehensive and self-contained overview of analytics concepts and practical experience executing those concepts with market-leading enterprise software solutions. The reader will be able to learn and apply all the concepts in the book without excessive prerequisite knowledge or experience.


  • 6+ Hours of Video InstructionThis course is designed to bring a holistic, approachable, and best-practice-driven learning experience to predictive analytics.Overview:Updated and revamped, Predictive Analytics, 2nd Edition, provides comprehensive (yet easy-to-digest) coverage of business analytics concepts, applications, methods, and tools, with a special emphasis on predictive modeling and analysis. Over the course of the eight lessons, you will learn fundamental concepts, methods, and algorithms of business analytics and data mining, as well as their application areas and best practices. You also learn how to use a variety of software tools (both commercial as well as free/open source) and how to use those tools to discover knowledge from a wide variety of data sources.At the end of the course, you will not only know what predictive analytics is and what it can do for an organization but also develop basic skills to practice predictive analytics using numerous tools and platforms, most of which are free and open source. The course is designed to provide thorough coverage of the underlying concepts and definitions of predictive analytics in order to demystify the concepts and terminology of these popular evidence-based managerial decisioning trends and then help build hands-on skills with the most popular analytics tools and platforms using intuitive examples and data sets.Lesson Descriptions:Lesson 1: Introduction to Predictive Analytics

  • Lesson 2: Introduction to Predictive Analytics and Data Mining

  • Lesson 3: The Data Mining Process

  • Lesson 4: Data and Methods in Data Mining

  • Lesson 5: Data Mining Algorithms

  • Lesson 6: Text Analytics and Text Mining

  • Lesson 7: Big Data Analytics

  • Lesson 8: Predictive Analytics Best Practices

Skill Level:There is not a required minimum skill or knowledge level to take this course. Because of its holistic coverage, the course appeals to anyone (students and professionals) at any level of technical or managerial skill levels who is interested in learning about predictive analytics and its value propositions.Learn How To:The course provides thorough yet easy-to-digest coverage of predictive analytics concepts, theories, and best practices, followed by visual, intuitive, and highly practical hands-on illustrative examples using a variety of data sets and industry-leading software tools and platforms.Who Should Take This Course:This course is designed for anyone who is interested in learning about the best practices of predictive analytics and anyone who seeks to rapidly move into practical application of this popular technology with a minimal investment of time and resources.Course Requirements:There are no specific prerequisites or must-have requirements for this course. It is designed to attract and benefit anyone at any skill and managerial level who is interested in learning predictive analytics.About Pearson Video Training:Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.


Dr. Adair is a vocal advocate for the use of technology to enhance business processes, and is the author of numerous textbooks leveraging technology in the business decision-making processes, including Finance: Applications & Theory (5th ed, McGraw-Hill), Corporate Finance Demystified (2nd edition, McGraw-Hill), Business Analytics (forthcoming, John Wiley and Sons, Inc.), and Investments (forthcoming, Cengage Learning).


Practical Analytics (2nd Ed) explains analytics concepts and activities in a way that provides real-world skill building while reinforcing fundamental concepts. This book provides a much needed approach to analytics through theory, applications, and hands-on experience using the latest industry tools. Although many books have been written on statistical data analysis, data mining, predictive analytics and business intelligence, these books are often too technical for a business user. The goal of this book is to provide a comprehensive and self-contained overview of analytics concepts and practical experience executing those concepts with market-leading enterprise software solutions. The reader will be able to learn and apply all the concepts in the book without excessive prerequisite knowledge or experience.


Business Analytics is the practice of transforming data into business insights to allow for better decision-making. By employing the latest tools, models and techniques, Business Analytics can help evaluate complex situations, consider all available options, predict outcomes and present critical risks for business decision makers. Harnessing the power of mathematical models, Business Analytics uses advanced tools and technologies to help each organization make the most of its data. The methods and models of analytics draw from disciplines including statistics, operations research, computer science, information systems and others. Business Analytics continues to be among the fastest-growing areas in business education in the United States.


Students pursuing business analytics as a second major are required to complete the courses listed below. Non-GWSB students may declare business analytics as a second major directly with their home school advisor; a signature from a GWSB academic advisor is not required.


Casperson and Longhurst led the way with a first place finish in the business analytics event. The duo used skills learned just recently in a data mining class to predict whether a bank could expect a potential client to open a new bank account based on the number of marketing phone calls that person received.


Kickstarter is one of the popular crowdfunding platforms used to implement business ideas on the web. The success of crowdfunding projects such as Kickstarter is realized with future financial support. However, there is no platform where users can get decision support before presenting their projects to supporters. To solve this problem, a platform where users can test their projects is required. Within this scope, a business intelligence model that works on the web has been developed by combining business analytics and machine learning methods. The data used for business analytics has been brought to a state that can provide inferences through visualization, reporting and query processes. Within the scope of machine learning, various algorithms were applied for success classification and the best results were given by 91% Random Forest, 85% Decision Tree, 84% K-Nearest Neighbors (KNN) algorithms. F1-Score, Recall, Precision, Mean Squared Error (MSE), Kappa and AUC values were analyzed to determine the most successful models. Thus, Kickstarter users will be able to see their shortcomings and have a prediction about success before presenting their projects to their backers.


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