Hands-On Prescriptive Analytics - Helion

ebook

Autor: Walter R. Paczkowski
ISBN: 9781098153137
stron: 412, Format: ebook
Data wydania: 2024-10-17
Księgarnia: Helion

Cena książki: 194,65 zł (poprzednio: 226,34 zł)
Oszczędzasz: 14% (-31,69 zł)

Osoby, które kupiły tę książkę, wybierały także »

Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty.

With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future.

While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you:

The distinction among descriptive, predictive, and prescriptive analytics
How predictive analytics produces a menu of action options
How prescriptive analytics narrows the menu of action options
The forms of prescriptive analytics: eight prescriptive methods
Two broad classes of these methods: non-stochastic and stochastic
How to develop prescriptive analyses for action recommendations
Ways to use an appropriate tool-set in Python

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Spis treści

Hands-On Prescriptive Analytics eBook -- spis treści

Preface
- The Books Topic
- The Books Audience
- What You Will Learnand How to Apply It
- The Books Structure
- Conventions Used in This Book
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
I. Introduction and Background
1. An Analytical Framework
- A Decision-Making Framework
- The Analytics Evolution
  - The Data Science Split
  - The Information Glue
  - How the Analytics Fit Together
  - Descriptive Analytics
  - Predictive Analytics
    - Forms of Predictive Analytics
    - Menu of prediction options
    - Creation of the menus
    - Natural Projects
    - What-if projects
    - Examples of decision menus
      - Vignette 1: Operational scale-view
      - Vignette 2: Tactical scale-view
      - Vignette 3: Strategic scale-view
- Uncertainty: Multiple Sources and Problems
  - Uncertainty Versus Risk
  - The Menu Cost of Uncertainty
- Probabilities and Uncertainty
- Prescriptive Analytics as a Separate Discipline
  - Decision-Makers Options
  - Business Goals and Constraints
  - Key Decision Question: What Is the Best Decision?
- The Analytics Flow
- Prescriptive Analytics and Decision Making
- Summary
2. Prescriptive Methods: Overview
- Introduction to Prescriptive Analytics Methods
  - Proprietary Methods
  - Public Domain Methods
- Summary of Prescriptive Analytics Methods
  - Methods Reviewed in the Literature
    - Probabilistic models
    - Machine learning and data mining
    - Mathematical programming
    - Evolutionary computation
    - Simulations
    - Logic-based models
  - Three Categories of Methods
    - Mathematical programming
    - Simulation
    - Decision trees
- Umbrella Classes: Non-Stochastic and Stochastic
  - Definition of Non-Stochastic and Stochastic Methods
  - Examples Based on Scale-Views
- The Role of Operations Research
- Summary
II. Essential Background Material
3. Python Essentials
- Python Structure: Overview
  - Worldwide Community
  - Extensive Array of Packages
  - Easy to Use
  - Operating Systems
  - A Programming Language
  - It Is Free
- Python Basics
  - Naming Conventions
  - Python Data Structures
    - Tuples
    - Lists
    - Dictionaries
  - Iterables
  - Basic Python Operators
  - Introduction to Built-In Functions
    - enumerate
    - round
    - print
    - range
  - Introduction to User-Defined Functions
    - Positional arguments
    - Named arguments
- Conditional Statements: if-else
- Python Looping Constructs
  - for loops
  - while loops
- Python Packages
  - Data Management
    - NumPy
    - pandas
  - Data Visualization
  - Statistical Analysis and Modeling
    - statsmodels
    - scikit-learn
    - SciPy
- Working with Python Packages
  - Using Anaconda
  - Updating Python Packages
  - Installing Packages
  - Importing Packages
- Go-To References
- Summary
4. Probability Essentials
- The World Is Ruled by Probabilities
- What Are Probabilities?
- Fundamental Probability Concepts
  - Frequency-Based Probabilities
  - Counting Functions
  - Independence and Conditional Probability
  - Summary of Probability Rules
- Limit Definition of Probabilities
- Subjective-Based Probabilities: Introduction
  - Bayes Theorem: Derivation
  - Bayes Theorem: Python Implementation
- Probability Distributions: Overview
  - Three Basic Probability Distributions: Binomial, Uniform, and Normal
    - Binomial distribution
    - Uniform distribution
    - Normal distribution
  - Key Distribution Parameters
- Summary
III. Non-Stochastic Prescriptive Analytic Methods
5. Mathematical Programming: Overview
- Background
  - Reason for Popularity
  - The Objective Function: Prescriptive Analytics Goal
- Linear Programming
  - Technical Overview
    - The simplex approach
    - Digression: The production function
    - Other solution methods
    - Handling special issues
  - Menus and Linear Programming
  - Python Use-Cases
    - Operational scale-view: Example 1
    - Operational scale-view: Example 2
- Integer Programming
  - Technical Overview
  - Menus and Integer Programming
  - Python Use-Cases
    - Tactical scale-view
      - Case 1: Select as many as needed
      - Case 2: Select a specific number
      - Case 2a: Select only one
      - Case 3: Select based on logical conditions
    - Strategic scale-view
      - Case 1: Select as many as needed
      - Case 2: Select a specific number
      - Case 2a: Select only one
      - Case 3: Select based on logical conditions
- Mixed Integer Programming
  - Technical Overview
  - Python Use-Case
- Summary
6. Decision Tree Analysis: Overview
- Extending the Menu into Time
- Introduction to Decision Trees
- Clarification of Decision Trees
- Background
  - Use of Trees in Decisions
  - Comparing the Two Decision Trees
- Python Use-Case
  - Use-Case Background
  - Use-Case Detailed Data
  - Role of the DADT
  - DADT Analysis
- Reaching a Decision Using a DADT
- Summary
- DADT Function
IV. Stochastic Prescriptive Analytic Methods
7. Simulation Essentials
- What Is a Simulation?
  - The Simulation Age
  - Types of Simulations
    - Non-stochastic
    - Stochastic
- Non-Stochastic Simulations: The Process
- Stochastic Simulations: The Process
  - Pseudo-Random Number Generators: A Brief Introduction
    - Linear congruential generator
    - Mersenne Twister generator
    - Generating random numbers in Python
      - random
      - NumPy
  - Simulation Models: Overview
  - KPMs to Measure
  - Aggregation Methods
- The Need for Stochastic Simulations
  - The Extent of Uncertainty
  - Randomness and the Degree of Uncertainty
- Summary
8. Simulation Examples
- Example 1: Coin Toss
- Example 2: Die Toss
- Example 3: Regression Analysis
- Example 4: Mathematical Programming
- Example 5: Decision Tree
- Summary
9. Developing Menu Options
- The Nature of What-If Questions
- Menu Generating Questions: A Deep Dive
  - The Structure of What-If Questions
  - What-If Analysis Versus Sensitivity Analysis
- Non-Stochastic Use-Cases
  - Pricing What-If Analysis: Basic
    - The pricing problem
    - Predictive model estimation
    - What-if analyses
  - Pricing What-If Analysis: Advanced
    - The pricing problem
    - Predictive model estimation
    - What-if analyses
- Stochastic Use-Case: Synthetic Data
  - Specifying the Process to Simulate
  - Example of a Systems Process Flow
    - The what-if questions
    - A diffusion model
    - Generating synthetic data
    - What-if analysis with the synthetic data
- Summary
10. Developing Menu Priors
- Background
- Digression on Beliefs and Priors
- Developing Probability Weights
- Eliciting Probability Distributions of Beliefs
  - Elicitation Method 1: Experimental Design-Based
    - A conjoint analysis
    - Discrete choice analysis
    - MaxDiff analysis
    - Methods problem
  - Elicitation Method 2: Direct Questioning
  - Elicitation Method 3: Activities
- Analyzing Elicited Probability Distributions
  - Elicitation Analysis 1: Averaging
  - Elicitation Analysis 2: CoDA
  - Elicitation Analysis 3: Bootstrapping
- Python Use-Case
  - Elicitation Example Analysis 1: Averaging
  - Elicitation Example Analysis 2: CoDA
  - Elicitation Example Analysis 3: Bootstrapping
- Summary
11. One-Time Decisions
- Evidence of the Problem
- Sequential Decisions: Introduction
  - Sequential Decisions 1: The Business Case
  - Sequential Decisions 2: Post-Business Case
- Sequential Analysis: Advanced Framework
  - Markov Decision Problem
  - Simulations and Reinforcement Learning
- Automating Sequential Decision Making
- Summary
Glossary
Bibliography
Index