About the Book
7+ Hours of Video Instruction
7+ Hours of Direct Instruction in Prescriptive Analytics Foundations, Methods, Applications, and Best Practices
Overview:
Prescriptive Analytics for Optimal Decision-Making LiveLessons is designed and developed to provide comprehensive coverage of the underlying concepts and definitions of business analytics, and specifically prescriptive analytics, in order to clarify the confusion about the already crowded terminology and buzzwords for these popular evidence-based managerial decisioning trends.
Prescriptive analytics are where the optimal decisions are made, often based on the information provided by descriptive and predictive analytics layers. The lesson structure in this course provides a natural progression of the foundational concepts, methods, and methodologies of prescriptive analytics as well as their application areas, the best practices, a variety of software tools, and how to use those tools to identify the best decision for a given, often overly complex, real-world problem.
Based on this foundational understanding, the course builds hands-on skills with a variety of popular prescriptive analytics tools and platforms (including Microsoft Excel) using intuitive examples and simplified data sets. The key idea is to build both awareness and in-depth understanding of prescriptive analytics best practices through intuitive, visual, and hands-on applications and case studies.
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 are interested in learning about prescriptive analytics and its value propositions.
Learn How To:
The course provides a thorough yet easy-to-digest coverage of analytics (business analytics in general and prescriptive analytics in specific) 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 prescriptive analytics and rapidly moving into practical extension of this popular technology of optimal decision-making 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 prescriptive analytics best practices, concepts, methods, tools and techniques.
Lesson Descriptions:
Lesson 1: Introduction to Prescriptive Analytics and Optimal Decision-Making
Lesson 2: Optimal Decision-Making with Linear Programming
Lesson 3: Heuristic Optimization with Evolutionary/Genetic Algorithms
Lesson 4: Simulation Modeling for Decision Making
Lesson 5: Multi-Criteria Decision-Making Methods
Lesson 6: Expert Systems-Based Decisioning Systems
Lesson 7: The Future of Prescriptive 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 http://www.informit.com/video.
Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
Table of Contents:
Introduction
Lesson 1: Introduction to Prescriptive Analytics and Optimal Decision-Making
1.1 Overview of Business Analytics and Data Science
1.2 An Overview of the Human Decision-Making Process
1.3 A Simple Timeline and Taxonomy for Business Analytics
1.4 Analytics Success Story: UPS’s ORION Project
Lesson 2: Optimal Decision-Making with Linear Programming
2.1 Introduction to Optimization and Linear Programming
2.2 Linear Programming
2.3 Graphic Solution for Linear Programming Problems
2.4 Solving Optimization Problems in Excel with Solver Add-In
Lesson 3: Heuristic Optimization with Evolutionary/Genetic Algorithms
3.1 Heuristic Programming
3.2 Genetic Algorithms
3.3 How Genetic Algorithms Work
3.4 GA Application in Excel
Lesson 4: Simulation Modeling for Decision-Making
4.1 Basics of Simulation Modeling
4.2 Applications and Types of Simulation Modeling
4.3 Simulation Development Process
4.4 Monte Carlo Simulation (with Excel)
4.6 Process Simulation (with Simio)
Lesson 5: Multi-Criteria Decision-Making Methods
5.1 MCDM and Types of Decisions
5.2 Weighted Sum Model
5.3 Analytic Hierarchy Process
5.4 Analytic Network Process
5.5 Fuzzy Logic for Imprecise Information and Reasoning
Lesson 6: Expert System-Based Decisioning Systems
6.1 Expert Systems fas Part of AI
6.2 Overview and Application of ES
6.3 Structure of an Expert System
6.4 Case-based Reasoning Systems
Lesson 7: The Future of Prescriptive Analytics
7.1 Big Data, Analytics, and the IoT Systems
7.2 Deep Learning versus Shallow Learning
7.3 Cognitive Computing and Searching
7.4 Demonstration of Big Data Technologies on the Cloud
Summary
About the Author :
Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, machine learning, and data mining. He is often invited to national and international conferences to deliver keynote presentations on topics related to data/text mining, business intelligence, decision support systems, business analytics, data science, and knowledge management. Prior to his appointment as a professor at Oklahoma State University in 2001, Dr. Delen worked for industry for more than 15 years, developing and delivering business analytics solutions to companies. His most recent industrial work was at a privately owned applied research and consulting company, Knowledge Based Systems, Inc. (kbsi.com), in College Station, Texas, as a research scientist. During his five years at KBSI, Dr. Delen led a number of projects related to decision support systems, enterprise engineering, information systems development, and advanced business analytics that were funded by private industry and federal agencies, including several branches of the Department of Defense, NASA, the National Science Foundation, National Institute for Standards and Technology, and the Department of Energy.
Today, in addition to his academic endeavors, Dr. Delen provides professional education, mentoring, and consulting services to businesses in assessing their analytics, data science. and information system needs, and helping them in developing state-of-the-art computerized decision support systems. He has published over 180 peer-reviewed research articles that appeared in major journals. He has also authored eleven books and textbooks in the broad area of business analytics, data science, and business intelligence. Dr. Delen regularly chairs tracks and mini-tracks at various business analytics and information systems conferences.
Currently, he is the editor-in-chief for the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards including the prestigious Fulbright scholar, Regents’ Distinguished Teacher and Researcher, President’s Outstanding Researcher, and big data mentor awards.
