{"product_id":"data-smart-isbn-9781119931386","title":"Data Smart","description":"\u003cp\u003e\u003cb\u003eWant to jump into data science but don't know where to start?\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLet's be real, data science is presented as something mystical and unattainable without the most powerful software, hardware, and data expertise. Real data science isn't about technology. It's about how you approach the problem.\u003c\/p\u003e \u003cp\u003eIn this updated edition of \u003ci\u003eData Smart: Using Data Science to Transform Information into Insight\u003c\/i\u003e, award-winning data scientist and bestselling author Jordan Goldmeier shows you how to implement data science problems using Excel while exposing how things work behind the scenes.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Smart\u003c\/i\u003e is your field guide to building statistics, machine learning, and powerful artificial intelligence concepts right inside your spreadsheet.\u003c\/p\u003e \u003cp\u003eInside you'll find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eFour-color data visualizations that highlight and illustrate the concepts discussed in the book\u003c\/li\u003e \u003cli\u003eTutorials explaining complicated data science using just Microsoft Excel\u003c\/li\u003e \u003cli\u003eHow to take what you've learned and apply it to everyday problems at work and life\u003c\/li\u003e \u003cli\u003eAdvice for using formulas, Power Query, and some of Excel's latest features to solve tough data problems\u003c\/li\u003e \u003cli\u003eSmart data science solutions for common business challenges\u003c\/li\u003e \u003cli\u003eExplanations of what algorithms do, how they work, and what you can tweak to take your Excel skills to the next level\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Smart\u003c\/i\u003e is a must-read for students, analysts, and managers ready to become data science savvy and share their findings with the world.\u003c\/p\u003e \u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Everything You Ever Needed to Know About Spreadsheets but Were Too Afraid to Ask 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Sample Data 2\u003c\/p\u003e \u003cp\u003eAccessing Quick Descriptive Statistics 3\u003c\/p\u003e \u003cp\u003eExcel Tables 4\u003c\/p\u003e \u003cp\u003eFiltering and Sorting 5\u003c\/p\u003e \u003cp\u003eTable Formatting 7\u003c\/p\u003e \u003cp\u003eStructured References 7\u003c\/p\u003e \u003cp\u003eAdding Table Columns 10\u003c\/p\u003e \u003cp\u003eLookup Formulas 11\u003c\/p\u003e \u003cp\u003eVLOOKUP 11\u003c\/p\u003e \u003cp\u003eINDEX\/MATCH 13\u003c\/p\u003e \u003cp\u003eXLOOKUP 15\u003c\/p\u003e \u003cp\u003ePivotTables 16\u003c\/p\u003e \u003cp\u003eUsing Array Formulas 19\u003c\/p\u003e \u003cp\u003eSolving Stuff with Solver 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Set It and Forget It: An Introduction to Power Query 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Power Query? 27\u003c\/p\u003e \u003cp\u003eSample Data 28\u003c\/p\u003e \u003cp\u003eStarting Power Query 29\u003c\/p\u003e \u003cp\u003eFiltering Rows 32\u003c\/p\u003e \u003cp\u003eRemoving Columns 33\u003c\/p\u003e \u003cp\u003eFind \u0026amp; Replace 34\u003c\/p\u003e \u003cp\u003eClose \u0026amp; Load to Table 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Naïve Bayes and the Incredible Lightness of Being an Idiot 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe World's Fastest Intro to Probability Theory 39\u003c\/p\u003e \u003cp\u003eTotaling Conditional Probabilities 40\u003c\/p\u003e \u003cp\u003eJoint Probability, the Chain Rule, and Independence 40\u003c\/p\u003e \u003cp\u003eWhat Happens in a Dependent Situation? 41\u003c\/p\u003e \u003cp\u003eBayes Rule 42\u003c\/p\u003e \u003cp\u003eSeparating the Signal and the Noise 43\u003c\/p\u003e \u003cp\u003eUsing the Bayes Rule to Create an AI Model 44\u003c\/p\u003e \u003cp\u003eHigh-Level Class Probabilities Are Often Assumed to Be Equal 45\u003c\/p\u003e \u003cp\u003eA Couple More Odds and Ends 46\u003c\/p\u003e \u003cp\u003eLet's Get This Excel Party Started 47\u003c\/p\u003e \u003cp\u003eCleaning the Data with Power Query 48\u003c\/p\u003e \u003cp\u003eSplitting on Spaces: Giving Each Word Its Due 50\u003c\/p\u003e \u003cp\u003eCounting Tokens and Calculating Probabilities 55\u003c\/p\u003e \u003cp\u003eWe Have a Model! Let's Use It 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Cluster Analysis Part 1: Using K-Means to Segment Your Customer Base 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDances at Summer Camp 65\u003c\/p\u003e \u003cp\u003eGetting Real: K-Means Clustering Subscribers in Email Marketing 70\u003c\/p\u003e \u003cp\u003eThe Initial Dataset 71\u003c\/p\u003e \u003cp\u003eDetermining What to Measure 72\u003c\/p\u003e \u003cp\u003eStart with Four Clusters 75\u003c\/p\u003e \u003cp\u003eEuclidean Distance: Measuring Distances as the Crow Flies 76\u003c\/p\u003e \u003cp\u003eSolving for the Cluster Centers 80\u003c\/p\u003e \u003cp\u003eMaking Sense of the Results 82\u003c\/p\u003e \u003cp\u003eGetting the Top Deals by Cluster 83\u003c\/p\u003e \u003cp\u003eThe Silhouette: A Good Way to Let Different K Values Duke It Out 86\u003c\/p\u003e \u003cp\u003eHow About Five Clusters? 95\u003c\/p\u003e \u003cp\u003eSolving for Five Clusters 96\u003c\/p\u003e \u003cp\u003eGetting the Top Deals for All Five Clusters 96\u003c\/p\u003e \u003cp\u003eComputing the Silhouette for 5-Means Clustering 99\u003c\/p\u003e \u003cp\u003eK-Medians Clustering and Asymmetric Distance Measurements 100\u003c\/p\u003e \u003cp\u003eUsing K-Medians Clustering 100\u003c\/p\u003e \u003cp\u003eGetting a More Appropriate Distance Metric 100\u003c\/p\u003e \u003cp\u003ePutting It All in Excel 102\u003c\/p\u003e \u003cp\u003eThe Top Deals for the 5-Medians Clusters 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Cluster Analysis Part II: Network Graphs and Community Detection 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Network Graph? 110\u003c\/p\u003e \u003cp\u003eVisualizing a Simple Graph 110\u003c\/p\u003e \u003cp\u003eBeyond GiGraph and Adjacency Lists 115\u003c\/p\u003e \u003cp\u003eBuilding a Graph from the Wholesale Wine Data 117\u003c\/p\u003e \u003cp\u003eCreating a Cosine Similarity Matrix 118\u003c\/p\u003e \u003cp\u003eProducing an R-Neighborhood Graph 121\u003c\/p\u003e \u003cp\u003eIntroduction to Gephi 123\u003c\/p\u003e \u003cp\u003eCreating a Static Adjacency Matrix 124\u003c\/p\u003e \u003cp\u003eBringing in Your R-Neighborhood Adjacency Matrix into Gephi 124\u003c\/p\u003e \u003cp\u003eNode Degree 128\u003c\/p\u003e \u003cp\u003eTouching the Graph Data 130\u003c\/p\u003e \u003cp\u003eHow Much Is an Edge Worth? Points and Penalties in Graph Modularity 132\u003c\/p\u003e \u003cp\u003eWhat's a Point, and What's a Penalty? 133\u003c\/p\u003e \u003cp\u003eSetting Up the Score Sheet 136\u003c\/p\u003e \u003cp\u003eLet's Get Clustering! 138\u003c\/p\u003e \u003cp\u003eSplit Number 1 138\u003c\/p\u003e \u003cp\u003eSplit 2: Electric Boogaloo 143\u003c\/p\u003e \u003cp\u003eAnd. . .Split3: Split with a Vengeance 145\u003c\/p\u003e \u003cp\u003eEncoding and Analyzing the Communities 146\u003c\/p\u003e \u003cp\u003eThere and Back Again: A Gephi Tale 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Regression: The Granddaddy of Supervised Artificial Intelligence 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredicting Pregnant Customers at RetailMart Using Linear Regression 158\u003c\/p\u003e \u003cp\u003eThe Feature Set 159\u003c\/p\u003e \u003cp\u003eAssembling the Training Data 161\u003c\/p\u003e \u003cp\u003eCreating Dummy Variables 163\u003c\/p\u003e \u003cp\u003eLet's Bake Our Own Linear Regression 165\u003c\/p\u003e \u003cp\u003eLinear Regression Statistics: R-Squared, F-Tests, t-Tests 173\u003c\/p\u003e \u003cp\u003eMaking Predictions on Some New Data and Measuring Performance 182\u003c\/p\u003e \u003cp\u003ePredicting Pregnant Customers at RetailMart Using Logistic Regression 192\u003c\/p\u003e \u003cp\u003eFirst You Need a Link Function 192\u003c\/p\u003e \u003cp\u003eHooking Up the Logistic Function and Reoptimizing 193\u003c\/p\u003e \u003cp\u003eBaking an Actual Logistic Regression 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Ensemble Models: A Whole Lot of Bad Pizza 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Started Using the Data from Chapter 6 203\u003c\/p\u003e \u003cp\u003eBagging: Randomize, Train, Repeat 204\u003c\/p\u003e \u003cp\u003eDecision Stump is Another Name for a Weak Learner 204\u003c\/p\u003e \u003cp\u003eDoesn't Seem So Weak to Me! 204\u003c\/p\u003e \u003cp\u003eYou Need More Power! 207\u003c\/p\u003e \u003cp\u003eLet's Train It 208\u003c\/p\u003e \u003cp\u003eEvaluating the Bagged Model 220\u003c\/p\u003e \u003cp\u003eBoosting: If You Get It Wrong, Just Boost and Try Again 223\u003c\/p\u003e \u003cp\u003eTraining the Model—Every Feature Gets a Shot 224\u003c\/p\u003e \u003cp\u003eEvaluating the Boosted Model 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Forecasting: Breathe Easy: You Can't Win 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Sword Trade Is Hopping 236\u003c\/p\u003e \u003cp\u003eGetting Acquainted with Time-Series Data 236\u003c\/p\u003e \u003cp\u003eStarting Slow with Simple Exponential Smoothing 238\u003c\/p\u003e \u003cp\u003eSetting Up the Simple Exponential Smoothing Forecast 240\u003c\/p\u003e \u003cp\u003eYou Might Have a Trend 249\u003c\/p\u003e \u003cp\u003eHolt's Trend-Corrected Exponential Smoothing 250\u003c\/p\u003e \u003cp\u003eSetting Up Holt's Trend-Corrected Smoothing in a Spreadsheet 252\u003c\/p\u003e \u003cp\u003eSo Are You Done? Looking at Autocorrelations 258\u003c\/p\u003e \u003cp\u003eMultiplicative Holt-Winters Exponential Smoothing 266\u003c\/p\u003e \u003cp\u003eSetting the Initial Values for Level, Trend, and Seasonality 268\u003c\/p\u003e \u003cp\u003eGetting Rolling on the Forecast 274\u003c\/p\u003e \u003cp\u003eAnd. . .Optimize! 280\u003c\/p\u003e \u003cp\u003ePutting a Prediction Interval Around the Forecast 283\u003c\/p\u003e \u003cp\u003eCreating a Fan Chart for Effect 287\u003c\/p\u003e \u003cp\u003eForecast Sheets in Excel 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Optimization Modeling: Because That \"Fresh-Squeezed\" Orange Juice Ain't Gonna Blend Itself 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWait Is This Data Science? 294\u003c\/p\u003e \u003cp\u003eStarting with a Simple Trade-Off 295\u003c\/p\u003e \u003cp\u003eRepresenting the Problem as a Polytope 296\u003c\/p\u003e \u003cp\u003eSolving by Sliding the Level Set 297\u003c\/p\u003e \u003cp\u003eThe Simplex Method: Rooting Around the Corners 298\u003c\/p\u003e \u003cp\u003eWorking in Excel 300\u003c\/p\u003e \u003cp\u003eFresh from the Grove to Your Glass with a Pit Stop Through a Blending Model 305\u003c\/p\u003e \u003cp\u003eLet's Start with Some Specs 307\u003c\/p\u003e \u003cp\u003eComing Back to Consistency 308\u003c\/p\u003e \u003cp\u003ePutting the Data into Excel 309\u003c\/p\u003e \u003cp\u003eSetting Up the Problem in Solver 311\u003c\/p\u003e \u003cp\u003eLowering Your Standards 314\u003c\/p\u003e \u003cp\u003eDead Squirrel Removal: the Minimax Formulation 317\u003c\/p\u003e \u003cp\u003eIf-Then and the \"Big M\" Constraint 320\u003c\/p\u003e \u003cp\u003eMultiplying Variables: Cranking Up the Volume to 11,000 324\u003c\/p\u003e \u003cp\u003eModeling Risk 330\u003c\/p\u003e \u003cp\u003eNormally Distributed Data 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOutliers Are (Bad?) People, Too 340\u003c\/p\u003e \u003cp\u003eThe Fascinating Case of Hadlum v Hadlum 340\u003c\/p\u003e \u003cp\u003eTukey's Fences 341\u003c\/p\u003e \u003cp\u003eApplying Tukey's Fences in a Spreadsheet 342\u003c\/p\u003e \u003cp\u003eThe Limitations of This Simple Approach 345\u003c\/p\u003e \u003cp\u003eTerrible at Nothing, Bad at Everything 346\u003c\/p\u003e \u003cp\u003ePreparing Data for Graphing 347\u003c\/p\u003e \u003cp\u003eCreating a Graph 350\u003c\/p\u003e \u003cp\u003eGetting the k-Nearest Neighbors 351\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 1: Just Use the Indegree 352\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 2: Getting Nuanced with k-Distance 355\u003c\/p\u003e \u003cp\u003eGraph Outlier Detection Method 3: Local Outlier Factors Are Where It's At 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Moving on From Spreadsheets 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Up and Running with R 364\u003c\/p\u003e \u003cp\u003eA Crash Course in R-ing 366\u003c\/p\u003e \u003cp\u003eShow Me the Numbers! Vector Math and Factoring 367\u003c\/p\u003e \u003cp\u003eThe Best Data Type of Them All: the Dataframe 370\u003c\/p\u003e \u003cp\u003eHow to Ask for Help in R 371\u003c\/p\u003e \u003cp\u003eIt Gets Even Better Beyond Base R 372\u003c\/p\u003e \u003cp\u003eDoing Some Actual Data Science 374\u003c\/p\u003e \u003cp\u003eReading Data into R 374\u003c\/p\u003e \u003cp\u003eSpherical K-Means on Wine Data in Just a Few Lines 375\u003c\/p\u003e \u003cp\u003eBuilding AI Models on the Pregnancy Data 381\u003c\/p\u003e \u003cp\u003eForecasting in R 389\u003c\/p\u003e \u003cp\u003eLooking at Outlier Detection 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Conclusion 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhere Am I? What Just Happened? 397\u003c\/p\u003e \u003cp\u003eBefore You Go-Go 397\u003c\/p\u003e \u003cp\u003eGet to Know the Problem 398\u003c\/p\u003e \u003cp\u003eWe Need More Translators 398\u003c\/p\u003e \u003cp\u003eBeware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection 399\u003c\/p\u003e \u003cp\u003eYou Are Not the Most Important Function of Your Organization 401\u003c\/p\u003e \u003cp\u003eGet Creative and Keep in Touch! 402\u003c\/p\u003e \u003cp\u003eIndex 403\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJORDAN GOLDMEIER\u003c\/b\u003e is an award-winning author in analytics, data science, and data visualization, and 11-time Microsoft MVP winner. Jordan has served analytics solutions for global organizations like NATO, The World Bank and Habitat for Humanity, and Fortune 500 companies likes Principal Financial and H\u0026amp;M. He has taught as an instructor for Wake Forest University, and served as a volunteer Emergency Medical Technician in New York City.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJordan breaks down advanced concepts with an infectious simplicity that has become his signature style.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"Through the data-driven AI hype, \u003ci\u003eData Smart\u003c\/i\u003e injects clarity into the discussion with fresh, compelling examples. This is an indispensable guide to becoming truly data smart. The second edition continues the mission of the first—to be a beacon of clarity against the clamor of data science hysteria. Ready to usher in a new generation of analysts, this is your ticket to becoming truly data smart.\"\u003cbr\u003e—\u003cb\u003eAlex Gutman, PhD,\u003c\/b\u003e Director of Data Science, Author of \u003ci\u003eBecoming a Data Head\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAn absolute gem.\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eData Smart\u003c\/i\u003e solidifies Excel's enduring relevance in the age of AI. Analysts who embrace its full potential will continue to thrive.\"\u003cbr\u003e—\u003cb\u003eGeorge Mount,\u003c\/b\u003e Excel MVP, Founder at Stringfest Analytics, Author of \u003ci\u003eAdvancing into Analytics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMost people are approaching data all wrong. Here's how to do it right.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn the era of AI, data scientists appear as mystical practitioners of magical arts. But I'm here to tell you: Data science is something you can do. Really. This book shows you the significant data science techniques, how they work, how to use them, and how they benefit your business, large or small. It's about turning raw data into insight you can act upon and doing it as quickly and painlessly as possible.\u003c\/p\u003e \u003cp\u003eRoll up your sleeves and let's get going.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eRelax — it's just Excel\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn this newly revised edition featuring four-color visualizations, you'll learn about\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCutting-edge Excel tools Power Query and new functions like XLOOKUP, LET and LAMBDA\u003c\/li\u003e \u003cli\u003eCreating artificial intelligence using linear models, ensemble methods, and naïve Bayes\u003c\/li\u003e \u003cli\u003eMathematical optimization, including non-linear programming and genetic algorithms\u003c\/li\u003e \u003cli\u003ePrediction with time-series data and forecasting with exponential smoothing\u003c\/li\u003e \u003cli\u003eClustering with k-means, spherical k-means, and graph modularity\u003c\/li\u003e \u003cli\u003eQuantifying and addressing risk with Monte Carlo simulation\u003c\/li\u003e \u003cli\u003eDetecting outliers in single or multiple dimensions\u003c\/li\u003e \u003cli\u003eStatistical programming with R\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989026193637,"sku":"NP9781119931386","price":50.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119931386.jpg?v=1761782489","url":"https:\/\/k12savings.com\/products\/data-smart-isbn-9781119931386","provider":"K12savings","version":"1.0","type":"link"}