Prescriptive Analytics LiveLessons

Prescriptive Analytics LiveLessons

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 39 Lessons (7h 23m) | 1.70 GB

Direct Instruction in Prescriptive Analytics Foundations, Methods, Applications, and Best Practices

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

Table of Contents

1 Prescriptive Analytics Introduction

Lesson 1 Introduction to Prescriptive Analytics and Optimal Decision-Making
2 Topics
3 Overview of Business Analytics and Data Science
4 An Overview of the Human Decision-Making Process
5 A Simple Timeline and Taxonomy for Business Analytics
6 Analytics Success Story UPS’s ORION Project

Lesson 2 Optimal Decision-Making with Linear Programming
7 Topics
8 Introduction to Optimization and Linear Programming
9 Linear Programming
10 Graphic Solution for Linear Programming Problems
11 Solving Optimization Problems in Excel with Solver Add-In

Lesson 3 Heuristic Optimization with EvolutionaryGenetic Algorithms
12 Topics
13 Heuristic Programming
14 Genetic Algorithms
15 How Genetic Algorithms Work
16 GA Application in Excel

Lesson 4 Simulation Modeling for Decision-Making
17 Topics
18 Basics of Simulation Modeling
19 Applications and Types of Simulation
20 Simulation Development Process
21 Monte-Carlo Simulation (with Excel)
22 Process Simulation (with Simio)

Lesson 5 Multi-Criteria Decision-Making Methods
23 Topics
24 MCDM and Types of Decisions
25 Weighted Sum Model
26 Analytic Hierarchy Process
27 Analytic Network Process
28 Fuzzy Logic for Imprecise Information and Reasoning

Lesson 6 Expert System-Based Decisioning Systems
29 Topics
30 Expert System as Part of AI
31 Overview and Application of ES
32 Structure of an Expert System
33 Case-based Reasoning Systems

Lesson 7 The Future of Prescriptive Analytics
34 Topics
35 Big Data, Analytics, and the IoT Systems
36 Deep Learning versus Shallow Learning
37 Cognitive Computing and Searching
38 Demonstration of Big Data Technologies on the Cloud

39 Prescriptive Analytics Summary