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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:
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