Business Intelligence and Decision Sciences
This guide covers multiple aspects of business intelligence and decision sciences including big data, data modelling and analysis, management information systems and more.
Getting started
This guide covers multiple aspects of business intelligence and decision sciences including big data, data modelling and analysis, management information systems and more.
What is business intelligence?
Business intelligence (BI) refers to the procedural and technical infrastructure that collects, stores, and analyzes the data produced by a company’s activities, according to Investopedia.
What is decision sciences?
According to the Decision Sciences Institute “Decision Sciences is the study of managerial decision making, using state-of-the-art data driven and behavioral approaches to solve real world problems in both the private and public sectors.”
Some prefer to refer to it as decision analysis, defined by Investopedia as “a systematic, quantitative, and visual approach to making strategic business decisions.”
Introduction to business intelligence and management information systems
Here is a list of books you can use if you need to familiarize yourself with business intelligence, analytics and management information systems related terms.
A Dictionary of Computer Science by
Previously named A Dictionary of Computing, this bestselling dictionary has been renamed A Dictionary of Computer Science, and fully revised by a team of computer specialists, making it the most up-to-date and authoritative guide to computing available. Containing over 6,500 entries and withexpanded coverage of multimedia, computer applications, networking, and personal computer science, it is a comprehensive reference work encompassing all aspects of the subject and is as valuable for home and office users as it is indispensable for students of computer science.Terms are defined in a jargon-free and concise manner with helpful examples where relevant. The dictionary contains approximately 150 new entries including cloud computing, cross-site scripting, iPad, semantic attack, smartphone, and virtual learning environment. Recommended web links for manyentries, accessible via the Dictionary of Computer Science companion website, provide valuable further information and the appendices include useful resources such as generic domain names, file extensions, and the Greek alphabet.This dictionary is suitable for anyone who uses computers, and is ideal for students of computer science and the related fields of IT, maths, physics, media communications, electronic engineering, and natural sciences.
ISBN: 9780199688975Publication Date: 2016-04-01
Please note some of these books might be reference books; reference books are only available for use in the library. You can identify those by looking at the Copies tab in the record of each book – the Location section should say Sherman Library Reference and the Status section should say LIB USE ONLY.
Here is a list of books you can use if you need to familiarize yourself with business intelligence, analytics and management information systems related terms.
Encyclopedia of Big Data Technologies by
The Encyclopedia of Big Data Technologies provides researchers, educators, students and industry professionals with a comprehensive authority over the most relevant Big Data Technology concepts. With over 300 articles written by worldwide subject matter experts from both industry and academia, the encyclopedia covers topics such as big data storage systems, NoSQL database, cloud computing, distributed systems, data processing, data management, machine learning and social technologies, data science.
ISBN: 9783319639628Publication Date: 2019
Encyclopedia of Business Analytics and Optimization by
As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.
ISBN: 9781466652033Publication Date: 2014-02-28
Encyclopedia of Machine Learning and Data Mining by
This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.
ISBN: 9781489976871Publication Date: 2017
Encyclopedia of Machine Learning and Data Science by
This authoritative, expanded and updated third edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 1000 entries – over 200 of them newly updated or added --are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.
ISBN: 9781489975027Publication Date: 2019
The Encyclopedia of Operations Management by
This is the perfect "field manual" for every supply chain or operations management practitioner and student. The field's only single-volume reference, it's uniquely convenient and uniquely affordable. With nearly 1,500 well-organized definitions, it can help students quickly map all areas of operations and supply chain management, and prepare for case discussions, exams, and job interviews. For instructors, it serves as an invaluable desk reference and teaching aid that goes far beyond typical dictionaries. For working managers, it offers a shared language, with insights for improving any process and supporting any training program. It thoroughly covers: accounting, customer service, distribution, e-business, economics, finance, forecasting, human resources, industrial engineering, industrial relations, inventory management, healthcare management, Lean Sigma/Six Sigma, lean thinking, logistics, maintenance engineering, management information systems, marketing/sales, new product development, operations research, organizational behavior/management, personal time management, production planning and control, purchasing, reliability engineering, quality management, service management, simulation, statistics, strategic management, systems engineering, supply and supply chain management, theory of constraints, transportation, and warehousing. Multiple figures, graphs, equations, Excel formulas, VBA scripts, and references support both learning and application.
ISBN: 9780132883702Publication Date: 2011-07-21
Please note some of these books might be reference books; reference books are only available for use in the library. You can identify those by looking at the Copies tab in the record of each book – the Location section should say Sherman Library Reference and the Status section should say LIB USE ONLY.
Here is a list of books you can use if you need to familiarize yourself with business intelligence, analytics and management information systems related terms.
Business Intelligence Guidebook by
Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled - projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget - turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. Provides practical guidelines for building successful BI, DW and data integration solutions. Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses Describes best practices and pragmatic approaches so readers can put them into action. Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.
ISBN: 9780124115286Publication Date: 2014-11-04
The Data Science Handbook by
A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: * Extensive sample code and tutorials using Python(tm) along with its technical libraries * Core technologies of "Big Data," including their strengths and limitations and how they can be used to solve real-world problems * Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity * A wide variety of case studies from industry * Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.
ISBN: 9781119092933Publication Date: 2017-01-20
The decision maker's handbook to data science by
With the second edition of The Decision Maker's Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective.
Publication Date: 2020
Handbook of Decision Analysis by
A ONE-OF-A-KIND GUIDE TO THE BEST PRACTICES IN DECISION ANALYSIS Decision analysis provides powerful tools for addressing complex decisions that involve uncertainty and multiple objectives, yet most training materials on the subject overlook the soft skills that are essential for success in the field. This unique resource fills this gap in the decision analysis literature and features both soft personal/interpersonal skills and the hard technical skills involving mathematics and modeling. Readers will learn how to identify and overcome the numerous challenges of decision making, choose the appropriate decision process, lead and manage teams, and create value for their organization. Performing modeling analysis, assessing risk, and implementing decisions are also addressed throughout. Additional features include: Key insights gleaned from decision analysis applications and behavioral decision analysis research Integrated coverage of the techniques of single- and multiple-objective decision analysis Multiple qualitative and quantitative techniques presented for each key decision analysis task Three substantive real-world case studies illustrating diverse strategies for dealing with the challenges of decision making Extensive references for mathematical proofs and advanced topics The Handbook of Decision Analysis is an essential reference for academics and practitioners in various fields including business, operations research, engineering, and science. The book also serves as a supplement for courses at the upper-undergraduate and graduate levels.
ISBN: 9781118515839Publication Date: 2013-01-30
Handbook of Research on Big Data Storage and Visualization Techniques by
The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.
ISBN: 9781522531432Publication Date: 2018-01-05
Handbook of Research on Innovative Management Using AI in Industry 5. 0 by
There is no industry left where artificial intelligence is not used in some capacity. The application of this technology has already stretched across a multitude of domains including law and policy; it will soon permeate areas beyond anyone's imagination. Technology giants such as Google, Apple, and Facebook are already investing their money, effort, and time toward integrating artificial intelligence. As this technology continues to develop and expand, it is critical for everyone to understand the various applications of artificial intelligence and its full potential. The Handbook of Research on Innovative Management Using AI in Industry 5.0 uncovers new and innovative features of artificial intelligence and how it can help in raising economic efficiency at both micro and macro levels and provides a deeper understanding of the relevant aspects of artificial intelligence impacting efficacy for better output. Covering topics such as consumer behavior, information technology, and personalized banking, it is an ideal resource for researchers, academicians, policymakers, business professionals, companies, and students.
ISBN: 9781799884996Publication Date: 2021-11-19
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).
ISBN: 9781119173632Publication Date: 2016-12-15
Operations Research and Management Science Handbook by
Operations Research (OR) began as an interdisciplinary activity to solve complex military problems during World War II. Utilizing principles from mathematics, engineering, business, computer science, economics, and statistics, OR has developed into a full fledged academic discipline with practical application in business, industry, government and military. Currently regarded as a body of established mathematical models and methods essential to solving complicated management issues, OR provides quantitative analysis of problems from which managers can make objective decisions. Operations Research and Management Science (OR/MS) methodologies continue to flourish in numerous decision making fields. Featuring a mix of international authors, Operations Research and Management Science Handbook combines OR/MS models, methods, and applications into one comprehensive, yet concise volume. The first resource to reach for when confronting OR/MS difficulties, this text - Provides a single source guide in OR/MS Bridges theory and practice Covers all topics relevant to OR/MS Offers a quick reference guide for students, researchers and practitioners Contains unified and up-to-date coverage designed and edited with non-experts in mind Discusses software availability for all OR/MS techniques Includes contributions from a mix of domestic and international experts The 26 chapters in the handbook are divided into two parts. Part I contains 14 chapters that cover the fundamental OR/MS models and methods. Each chapter gives an overview of a particular OR/MS model, its solution methods and illustrates successful applications. Part II of the handbook contains 11 chapters discussing the OR/MS applications in specific areas. They include airlines, e-commerce, energy systems, finance, military, production systems, project management, quality control, reliability, supply chain management and water resources. Part II ends with a chapter on the future of OR/MS applications.
ISBN: 9780849397219Publication Date: 2007-12-05
Operations Research Calculations Handbook by
A handbook in the truest sense of the word, the first edition of the Operations Research Calculations Handbook quickly became an indispensible resource. While other books available tend to give detailed information about specific topics, this one contains comprehensive information and results useful for real-world problem solving. Reflecting the br
ISBN: 9781420052411Publication Date: 2009-12-23
Please note some of these books might be reference books; reference books are only available for use in the library. You can identify those by looking at the Copies tab in the record of each book – the Location section should say Sherman Library Reference and the Status section should say LIB USE ONLY.
Required/recommended materials for selected business intelligence courses
|
Course |
Required or recommended materials |
| ISM 5150 | Pearlson, Keri E.; Saunders, Carol S. Managing and Using Information Systems: A Strategic Approach. (2013) |
| QNT 5000 | Lind, Douglas; Marchal, William; Wathen, Samuel. Statistical Technology in Business and Economics. |
| QNT 5160 | Hillier, Frederick. Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets. (2014) |
| QNT 5471 | Laursen, Gert H.N. Business Analytics for Managers. (2017) |
| QNT 5471 | O'neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. (2016) |
| QNT 5476 | Jukic, Nenad; Vrbsky, Susan; Nestorov, Svetlozar; Sharma, Abhishek. Database Systems: Introduction to Databases and Data Warehouses. |
|
Course |
Required or recommended materials |
| CSIS 3400 | Dale, Nell. C++ Plus Data Structures. (2003) |
| ISM 3660 |
Kroenke. Experiencing MIS. |
| QNT 2880 | Jaggia, Sanjiv. Business Analytics. |
| TECH 3100 |
Sharda, Ramesh; Delen, Dursun; Turban, Efraim. Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. |
