Numsense! Data Science For The Layman: No Math Added Pdf Download UPDATED

Numsense! Data Science For The Layman: No Math Added Pdf Download

Numsense! Data Science for the Layman

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Numsense! Data Science for the Layman

No Math Added

About the Book

About the Authors

Annalyn Ng

Annalyn Ng completed her MPhil at the University of Cambridge Psychometrics Centre, where she mined consumer data for targeted advertizing, and programmed cerebral tests for job recruitment. Annalyn was also an undergrad statistics tutor at the University of Michigan (Ann Arbor). Disney Research afterwards roped her into their behavioral sciences team, where she examined psychological profiles of consumers.

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Kenneth Soo

Kenneth Soo was the top pupil for all 3 years of his Math/OR/Stats/Econs (MORSE) degree at the University of Warwick. He is currently completing his MS in Statistics at Stanford University. He was a research assistant with the Operational Research & Management Sciences Group at University of Warwick, working on bi-objective robust optimization with applications in networks subject area to random failures.

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Table of Contents

• Foreword
• Preface
• Why Data Scientific discipline?
• 1. Basics in a Nutshell
  • one.1 Data Training
  • 1.2 Algorithm Option
  • ane.3 Parameter Tuning
  • i.4 Evaluating Results
  • 1.5 Summary
• ii. k-Means Clustering
  • 2.one Finding Customer Clusters
  • two.2 Example: Personality Profiles of Film Fans
  • 2.3 Defining Clusters
  • ii.4 Limitations
  • 2.5 Summary
• 3. Principal Component Analysis
  • 3.1 Exploring Nutritional Content of Food
  • 3.2 Principal Components
  • 3.3 Case: Analyzing Food Groups
  • 3.four Limitations
  • 3.5 Summary
• 4. Association Rules
  • 4.1 Discovering Purchasing Patterns
  • 4.ii Support, Confidence and Lift
  • iv.three Example: Transacting Grocery Sales
  • 4.4 Apriori Principle
  • 4.five Limitations
  • 4.6 Summary
• v. Social Network Assay
  • five.1 Mapping out Relationships
  • 5.2 Example: Geopolitics in Weapons Trade
  • five.three Louvain Method
  • 5.four PageRank Algorithm
  • five.5 Limitations
  • five.6 Summary
• half-dozen. Regression Analysis
  • vi.one Deriving a Tendency Line
  • 6.2 Case: Predicting House Prices
  • half dozen.3 Slope Descent
  • half-dozen.four Regression Coefficients
  • 6.v Correlation Coefficients
  • half dozen.6 Limitations
  • 6.vii Summary
• seven. chiliad-Nearest Neighbors and Anomaly Detection
  • seven.i Food Forensics
  • 7.2 Birds of a Feather Flock Together
  • 7.3 Example: Distilling Differences in Vino
  • seven.4 Anomaly Detection
  • vii.5 Limitations
  • vii.six Summary
• eight. Back up Vector Auto
  • 8.i "No" or "Oh No"?
  • 8.ii Example: Predicting Center Affliction
  • viii.3 Delineating an Optimal Boundary
  • viii.4 Limitations
  • eight.5 Summary
• 9. Determination Tree
  • ix.one Predicting Survival in a Disaster
  • ix.2 Instance: Escaping from the Titanic
  • 9.3 Generating a Decision Tree
  • 9.iv Limitations
  • nine.5 Summary
• 10. Random Forests
  • 10.1 Wisdom of the Crowd
  • 10.two Example: Forecasting Crime
  • ten.iii Ensembles
  • 10.4 Bootstrap Accumulation (Bagging)
  • 10.v Limitations
  • ten.6 Summary
• 11. Neural Networks
  • 11.i Building a Brain
  • 11.2 Example: Recognizing Handwritten Digits
  • eleven.3 Components of a Neural Network
  • 11.four Activation Rules
  • 11.5 Limitations
  • eleven.half-dozen Summary
• 12. A/B Testing and Multi-Armed Bandits
  • 12.ane Basics of A/B testing
  • 12.2 Limitations of A/B testing
  • 12.3 Epsilon-Decreasing Strategy
  • 12.4 Case: Multi-Arm Bandits
  • 12.v Fun Fact: Sticking to the Winner
  • 12.6 Limitations of an Epsilon-Decreasing Strategy
  • 12.7 Summary
• Appendix
  • A. Overview of Unsupervised Learning Algorithms
  • B. Overview of Supervised Learning Algorithms
  • C. List of Tuning Parameters
  • D. More than Evaluation Metrics
• Glossary
• Data Sources and References
• Nearly the Authors

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