Analytical Skills for Ai and Data Science Chorzów

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You...ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.Break business decisions into stages that can be tackled using different skills from the analytical toolboxIdentify and embrace uncertainty in decision making and protect against common human biasesCustomize optimal decisions to different customers using predictive and prescriptive methods and technologiesAsk business questions that create high value through AI- and data-driven technologies Spis treści: Preface Why Analytical Skills for AI? Use Case-Driven Approach What This Book Isnt Who This Book Is For Whats Needed Conventions Used in This Book Using Code Examples OReilly Online Learning How to Contact Us Acknowledgments 1. Analytical Thinking and the AI-Driven Enterprise What Is AI? Why Current AI Wont Deliver on Its Promises How Did We Get Here? The Data Revolution The three Vs Data maturity models Descriptive stage Predictive stage Prescriptive stage A Tale of Unrealized Expectations Analytical Skills for the Modern AI-Driven Enterprise Key Takeways Further Reading 2. Intro to Analytical Thinking Descriptive, Predictive, and Prescriptive Questions When Predictive Analysis Is Powerful: The Case of Cancer Detection Descriptive Analysis: The Case of Customer Churn Describing churn Predicting churn Prescribing courses of action to reduce churn Business Questions and KPIs KPIs to Measure the Success of a Loyalty Program An Anatomy of a Decision: A Simple Decomposition An Example: Why Did You Buy This Book? A Primer on Causation Defining Correlation and Causation Some Difficulties in Estimating Causal Effects Problem 1: We cant observe counterfactuals Problem 2: Heterogeneity Problem 3: Confounders Problem 4: Selection effects A/B testing Uncertainty Uncertainty from Simplification Uncertainty from Heterogeneity Uncertainty from Social Interactions Uncertainty from Ignorance Key Takeaways Further Reading 3. Learning to Ask Good Business Questions From Business Objectives to Business Questions Descriptive, Predictive, and Prescriptive Questions Always Start with the Business Question and Work Backward Further Deconstructing the Business Questions Example with a Two-Sided Platform Learning to Ask Business Questions: Examples from Common Use Cases Lowering Churn Defining the business question Descriptive questions Predictive questions Prescriptive questions Cross-Selling: Next-Best Offer Defining the business question Descriptive questions Predictive questions Prescriptive questions CAPEX Optimization Store Locations Who Should I Hire? Delinquency Rates Stock or Inventory Optimization Store Staffing Key Takeaways Further Reading 4. Actions, Levers, and Decisions Understanding What Is Actionable Physical Levers Human Levers Why Do We Behave the Way We Do? Levers from Restrictions Time restrictions Levers That Affect Our Preferences Genetics Individual and social learning Social reasons: strategic effects Social reasons: conformity and peer effects Framing effects Loss aversion Levers That Change Your Expectations The availability and representativeness heuristics Revisiting Our Use Cases Customer Churn Cross-Selling Capital Expenditure (CAPEX) Optimization Store Locations Who Should I Hire? Delinquency Rates Stock Optimization Store Staffing Key Takeaways Further Reading 5. From Actions to Consequences: Learning How to Simplify Why Do We Need to Simplify? First- and Second-Order Effects Exercising Our Analytical Muscle: Welcome Fermi How Many Tennis Balls Fit the Floor of This Rectangular Room? How Much Would You Charge to Clean Every Window in Mexico City? Fermi Problems to Make Preliminary Business Cases Paying our customers for their contact info Excessive contact attempts increase the probability of churn Should you accept the offer from that startup? Revisiting the Examples from Chapter 3 Customer Churn Cross-Selling CAPEX Optimization Price effect Quantity effect Store Locations Delinquency Rates Stock Optimization Store Staffing Key Takeaways Further Reading 6. Uncertainty Where Does Uncertainty Come From? Quantifying Uncertainty Expected Values Bidding for a highway construction contract Interpreting expected values Making Decisions Without Uncertainty Making Simple Decisions Under Uncertainty Decisions Under Uncertainty Is This the Best We Can Do? But This Is a Frequentist Argument Normative and Descriptive Theories of Decision-Making Some Paradoxes in Decision-Making Under Uncertainty The St. Petersburg Paradox Risk Aversion Putting it All into Practice Estimating the Probabilities Estimating unconditional probabilities Estimating conditional probabilities A/B testing Bandit problems Estimating Expected Values Frequentist and Bayesian Methods Revisiting Our Use Cases Customer Churn Cross-Selling CAPEX Optimization Store Locations Who to Hire Delinquency Rates Stock Optimization Key Takeaways Further Reading 7. Optimization What Is Optimization? Numerical Optimization Is Hard Optimization Is Not New in Business Settings Price and Revenue Optimization Optimization Without Uncertainty Customer Churn Cross-Selling CAPEX Investment Optimal Staffing Optimal Store Locations Optimization with Uncertainty Customer Churn Cross-Selling Optimal Staffing Tricks for Solving Optimization Problems Under Uncertainty Key Takeaways Further Reading 8. Wrapping Up Analytical Skills Asking Prescriptive Questions Understanding Causality Thinking outside the box Simplify Embracing Uncertainty Do-nothing approach The data-driven approach The model-driven approach Tackling Optimization Understanding the objective function Dealing with local optima Sensitivity to initial guesses Scaling and production issues The AI-Driven Enterprise of the Future Back to AI Learning how to make decisions Some problems with this approach to automatic decision-making Ethics Some Final Thoughts A. A Brief Introduction to Machine Learning What Is Machine Learning? A Taxonomy of ML Models Supervised Learning Unsupervised Learning Semisupervised Learning Regression and Classification Making Predictions Caveats to the Plug-in Approach Where Do These Functions Come From? Making Good Predictions From Linear Regression to Deep Learning Linear Regression Controlling for other variables Overfitting Neural Networks Activation functions: adding some extra nonlinearity The success of deep learning A Primer on A/B Testing A/B testing in practice Understanding power and size calculations False positives and false negatives Further Reading Index O autorze: Dr Daniel Vaughan od piętnastu lat zajmuje się rozwiązywaniem problemów przy użyciu metod predykcyjnych i normatywnych. Obecnie prowadzi dział data science w Airbnb w Ameryce Łacińskiej. Wcześniej był dyrektorem do spraw danych i kierownikiem działu data science w Telefónica México.