Efnisyfirlit

• Title Page
• Copyright
• Contents
• Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask
  • Some Sample Data
  • Moving Quickly with the Control Button
  • Copying Formulas and Data Quickly
  • Formatting Cells
  • Paste Special Values
  • Inserting Charts
  • Locating the Find and Replace Menus
  • Formulas for Locating and Pulling Values
  • Using VLOOKUP to Merge Data
  • Filtering and Sorting
  • Using PivotTables
  • Using Array Formulas
  • Solving Stuff with Solver
  • OpenSolver: I Wish We Didn’t Need This, but We Do
  • Wrapping Up
• Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base
  • Girls Dance with Girls, Boys Scratch Their Elbows
  • Getting Real: K-Means Clustering Subscribers in E-mail Marketing
    • Joey Bag O’ Donuts Wholesale Wine Emporium
    • The Initial Dataset
    • Determining What to Measure
    • Start with Four Clusters
    • Euclidean Distance: Measuring Distances as the Crow Flies
    • Distances and Cluster Assignments for Everybody!
    • Solving for the Cluster Centers
    • Making Sense of the Results
    • Getting the Top Deals by Cluster
    • The Silhouette: A Good Way to Let Different K Values Duke It Out
    • How about Five Clusters?
    • Solving for Five Clusters
    • Getting the Top Deals for All Five Clusters
    • Computing the Silhouette for 5-Means Clustering
  • K-Medians Clustering and Asymmetric Distance Measurements
    • Using K-Medians Clustering
    • Getting a More Appropriate Distance Metric
    • Putting It All in Excel
    • The Top Deals for the 5-Medians Clusters
  • Wrapping Up
• Chapter 3 Naïve Bayes and the Incredible Lightness of Being an Idiot
  • When You Name a Product Mandrill, You’re Going to Get Some Signal and Some Noise
  • The World’s Fastest Intro to Probability Theory
    • Totaling Conditional Probabilities
    • Joint Probability, the Chain Rule, and Independence
    • What Happens in a Dependent Situation?
    • Bayes Rule
  • Using Bayes Rule to Create an AI Model
    • High-Level Class Probabilities Are Often Assumed to Be Equal
    • A Couple More Odds and Ends
  • Let’s Get This Excel Party Started
    • Removing Extraneous Punctuation
    • Splitting on Spaces
    • Counting Tokens and Calculating Probabilities
    • And We Have a Model! Let’s Use It
  • Wrapping Up
• Chapter 4 Optimization Modeling: Because That “Fresh Squeezed” Orange Juice Ain’t Gonna Blend
  • Why Should Data Scientists Know Optimization?
  • Starting with a Simple Trade-Off
    • Representing the Problem as a Polytope
    • Solving by Sliding the Level Set
    • The Simplex Method: Rooting around the Corners
    • Working in Excel
    • There’s a Monster at the End of This Chapter
  • Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model
    • You Use a Blending Model
    • Let’s Start with Some Specs
    • Coming Back to Consistency
    • Putting the Data into Excel
    • Setting Up the Problem in Solver
    • Lowering Your Standards
    • Dead Squirrel Removal: The Minimax Formulation
    • If-Then and the “Big M” Constraint
    • Multiplying Variables: Cranking Up the Volume to 11
  • Modeling Risk
    • Normally Distributed Data
  • Wrapping Up
• Chapter 5 Cluster Analysis Part II: Network Graphs and Community Detection
  • What Is a Network Graph?
  • Visualizing a Simple Graph
  • Brief Introduction to Gephi
    • Gephi Installation and File Preparation
    • Laying Out the Graph
    • Node Degree
    • Pretty Printing
    • Touching the Graph Data
  • Building a Graph from the Wholesale Wine Data
    • Creating a Cosine Similarity Matrix
    • Producing an r-Neighborhood Graph
  • How Much Is an Edge Worth? Points and Penalties in Graph Modularity
    • What’s a Point and What’s a Penalty?
    • Setting Up the Score Sheet
  • Let’s Get Clustering!
    • Split Number 1
    • Split 2: Electric Boogaloo
    • And…Split 3: Split with a Vengeance
    • Encoding and Analyzing the Communities
  • There and Back Again: A Gephi Tale
  • Wrapping Up
• Chapter 6 The Granddaddy of Supervised Artificial Intelligence—Regression
  • Wait, What? You’re Pregnant?
  • Don’t Kid Yourself
  • Predicting Pregnant Customers at RetailMart Using Linear Regression
    • The Feature Set
    • Assembling the Training Data
    • Creating Dummy Variables
    • Let’s Bake Our Own Linear Regression
    • Linear Regression Statistics: R-Squared, F Tests, t Tests
    • Making Predictions on Some New Data and Measuring Performance
  • Predicting Pregnant Customers at RetailMart Using Logistic Regression
    • First You Need a Link Function
    • Hooking Up the Logistic Function and Reoptimizing
    • Baking an Actual Logistic Regression
    • Model Selection—Comparing the Performance of the Linear and Logistic Regressions
  • For More Information
  • Wrapping Up
• Chapter 7 Ensemble Models: A Whole Lot of Bad Pizza
  • Using the Data from Chapter 6
  • Bagging: Randomize, Train, Repeat
    • Decision Stump Is an Unsexy Term for a Stupid Predictor
    • Doesn’t Seem So Stupid to Me!
    • You Need More Power!
    • Let’s Train It
    • Evaluating the Bagged Model
  • Boosting: If You Get It Wrong, Just Boost and Try Again
    • Training the Model—Every Feature Gets a Shot
    • Evaluating the Boosted Model
  • Wrapping Up
• Chapter 8 Forecasting: Breathe Easy; You Can’t Win
  • The Sword Trade Is Hopping
  • Getting Acquainted with Time Series Data
  • Starting Slow with Simple Exponential Smoothing
    • Setting Up the Simple Exponential Smoothing Forecast
  • You Might Have a Trend
    • Holt’s Trend-Corrected Exponential Smoothing
      • Setting Up Holt’s Trend-Corrected Smoothing in a Spreadsheet
      • So Are You Done? Looking at Autocorrelations
  • Multiplicative Holt-Winters Exponential Smoothing
    • Setting the Initial Values for Level, Trend, and Seasonality
    • Getting Rolling on the Forecast
    • And...Optimize!
    • Please Tell Me We’re Done Now!!!
    • Putting a Prediction Interval around the Forecast
    • Creating a Fan Chart for Effect
  • Wrapping Up
• Chapter 9 Outlier Detection: Just Because They’re Odd Doesn’t Mean They’re Unimportant
  • Outliers Are (Bad?) People, Too
  • The Fascinating Case of Hadlum v. Hadlum
    • Tukey Fences
    • Applying Tukey Fences in a Spreadsheet
    • The Limitations of This Simple Approach
  • Terrible at Nothing, Bad at Everything
    • Preparing Data for Graphing
    • Creating a Graph
    • Getting the k Nearest Neighbors
    • Graph Outlier Detection Method 1: Just Use the Indegree
    • Graph Outlier Detection Method 2: Getting Nuanced with k-Distance
    • Graph Outlier Detection Method 3: Local Outlier Factors Are Where It’s At
  • Wrapping Up
• Chapter 10 Moving from Spreadsheets into R
  • Getting Up and Running with R
    • Some Simple Hand-Jamming
    • Reading Data into R
  • Doing Some Actual Data Science
    • Spherical K-Means on Wine Data in Just a Few Lines
    • Building AI Models on the Pregnancy Data
    • Forecasting in R
    • Looking at Outlier Detection
  • Wrapping Up
• Conclusion
  • Where Am I? What Just Happened?
  • Before You Go-Go
    • Get to Know the Problem
    • We Need More Translators
    • Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection
    • You Are Not the Most Important Function of Your Organization
  • Get Creative and Keep in Touch!
• Index

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