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• 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