Fundamental concept: Solving business problems with data science starts with analytical engineering: designing an analytical solution, based on the data, tools, and techniques available.
Exemplary technique: Expected value as a framework for data science solution design.
Targeting the Best Prospects for a Charity Mailing
The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces
A Brief Digression on Selection Bias
Our Churn Example Revisited with Even More Sophistication
The Expected Value Framework: Structuring a More Complicated Business Problem
Assessing the Influence of the Incentive
From an Expected Value Decomposition to a Data Science Solution
12. Other Data Science Tasks and Techniques
Fundamental concepts: Our fundamental concepts as the basis of many common data science techniques; The importance of familiarity with the building blocks of data science.
Exemplary techniques: Association and co-occurrences; Behavior profiling; Link prediction; Data reduction; Latent information mining; Movie recommendation; Bias-variance decomposition of error; Ensembles of models; Causal reasoning from data.
Co-occurrences and Associations: Finding Items That Go Together
Measuring Surprise: Lift and Leverage
Example: Beer and Lottery Tickets
Associations Among Facebook Likes
Profiling: Finding Typical Behavior
Link Prediction and Social Recommendation
Data Reduction, Latent Information, and Movie Recommendation
Bias, Variance, and Ensemble Methods
Data-Driven Causal Explanation and a Viral Marketing Example
13. Data Science and Business Strategy
Fundamental concepts: Our principles as the basis of success for a data-driven business; Acquiring and sustaining competitive advantage via data science; The importance of careful curation of data science capability.
Thinking Data-Analytically, Redux
Achieving Competitive Advantage with Data Science
Sustaining Competitive Advantage with Data Science
Formidable Historical Advantage
Unique Intellectual Property
Unique Intangible Collateral Assets
Superior Data Scientists
Superior Data Science Management
Attracting and Nurturing Data Scientists and Their Teams
Examine Data Science Case Studies
Be Ready to Accept Creative Ideas from Any Source
Be Ready to Evaluate Proposals for Data Science Projects
Example Data Mining Proposal
Flaws in the Big Red Proposal
A Firm’s Data Science Maturity
The Fundamental Concepts of Data Science
Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data
Changing the Way We Think about Solutions to Business Problems
What Data Can’t Do: Humans in the Loop, Revisited
Privacy, Ethics, and Mining Data About Individuals
Is There More to Data Science?
Final Example: From Crowd-Sourcing to Cloud-Sourcing