{"product_id":"becoming-a-data-head-9781119741749","title":"Becoming a Data Head","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eForeword xxiii\u003c\/p\u003e \u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Thinking Like a Data Head\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 What Is the Problem? 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eQuestions a Data Head Should Ask 4\u003c\/p\u003e \u003cp\u003eWhy Is This Problem Important? 4\u003c\/p\u003e \u003cp\u003eWho Does This Problem Affect? 6\u003c\/p\u003e \u003cp\u003eWhat If We Don’t Have the Right Data? 6\u003c\/p\u003e \u003cp\u003eWhen Is the Project Over? 7\u003c\/p\u003e \u003cp\u003eWhat If We Don’t Like the Results? 7\u003c\/p\u003e \u003cp\u003eUnderstanding Why Data Projects Fail 8\u003c\/p\u003e \u003cp\u003eCustomer Perception 8\u003c\/p\u003e \u003cp\u003eDiscussion 10\u003c\/p\u003e \u003cp\u003eWorking on Problems That Matter 11\u003c\/p\u003e \u003cp\u003eChapter Summary 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 What Is Data? 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData vs. Information 13\u003c\/p\u003e \u003cp\u003eAn Example Dataset 14\u003c\/p\u003e \u003cp\u003eData Types 15\u003c\/p\u003e \u003cp\u003eHow Data Is Collected and Structured 16\u003c\/p\u003e \u003cp\u003eObservational vs. Experimental Data 16\u003c\/p\u003e \u003cp\u003eStructured vs. Unstructured Data 17\u003c\/p\u003e \u003cp\u003eBasic Summary Statistics 18\u003c\/p\u003e \u003cp\u003eChapter Summary 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Prepare to Think Statistically 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAsk Questions 22\u003c\/p\u003e \u003cp\u003eThere Is Variation in All Things 23\u003c\/p\u003e \u003cp\u003eScenario: Customer Perception (The Sequel) 24\u003c\/p\u003e \u003cp\u003eCase Study: Kidney-Cancer Rates 26\u003c\/p\u003e \u003cp\u003eProbabilities and Statistics 28\u003c\/p\u003e \u003cp\u003eProbability vs. Intuition 29\u003c\/p\u003e \u003cp\u003eDiscovery with Statistics 31\u003c\/p\u003e \u003cp\u003eChapter Summary 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Speaking Like a Data Head\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Argue with the Data 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Would You Do? 38\u003c\/p\u003e \u003cp\u003eMissing Data Disaster 39\u003c\/p\u003e \u003cp\u003eTell Me the Data Origin Story 43\u003c\/p\u003e \u003cp\u003eWho Collected the Data? 44\u003c\/p\u003e \u003cp\u003eHow Was the Data Collected? 44\u003c\/p\u003e \u003cp\u003eIs the Data Representative? 45\u003c\/p\u003e \u003cp\u003eIs There Sampling Bias? 46\u003c\/p\u003e \u003cp\u003eWhat Did You Do with Outliers? 46\u003c\/p\u003e \u003cp\u003eWhat Data Am I Not Seeing? 47\u003c\/p\u003e \u003cp\u003eHow Did You Deal with Missing Values? 47\u003c\/p\u003e \u003cp\u003eCan the Data Measure What You Want It to Measure? 48\u003c\/p\u003e \u003cp\u003eArgue with Data of All Sizes 48\u003c\/p\u003e \u003cp\u003eChapter Summary 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Explore the Data 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploratory Data Analysis and You 52\u003c\/p\u003e \u003cp\u003eEmbracing the Exploratory Mindset 52\u003c\/p\u003e \u003cp\u003eQuestions to Guide You 53\u003c\/p\u003e \u003cp\u003eThe Setup 53\u003c\/p\u003e \u003cp\u003eCan the Data Answer the Question? 54\u003c\/p\u003e \u003cp\u003eSet Expectations and Use Common Sense 54\u003c\/p\u003e \u003cp\u003eDo the Values Make Intuitive Sense? 54\u003c\/p\u003e \u003cp\u003eWatch Out: Outliers and Missing Values 58\u003c\/p\u003e \u003cp\u003eDid You Discover Any Relationships? 59\u003c\/p\u003e \u003cp\u003eUnderstanding Correlation 59\u003c\/p\u003e \u003cp\u003eWatch Out: Misinterpreting Correlation 60\u003c\/p\u003e \u003cp\u003eWatch Out: Correlation Does Not Imply Causation 62\u003c\/p\u003e \u003cp\u003eDid You Find New Opportunities in the Data? 63\u003c\/p\u003e \u003cp\u003eChapter Summary 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Examine the Probabilities 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTake a Guess 66\u003c\/p\u003e \u003cp\u003eThe Rules of the Game 66\u003c\/p\u003e \u003cp\u003eNotation 67\u003c\/p\u003e \u003cp\u003eConditional Probability and Independent Events 69\u003c\/p\u003e \u003cp\u003eThe Probability of Multiple Events 69\u003c\/p\u003e \u003cp\u003eTwo Things That Happen Together 69\u003c\/p\u003e \u003cp\u003eOne Thing or the Other 70\u003c\/p\u003e \u003cp\u003eProbability Thought Exercise 72\u003c\/p\u003e \u003cp\u003eNext Steps 73\u003c\/p\u003e \u003cp\u003eBe Careful Assuming Independence 74\u003c\/p\u003e \u003cp\u003eDon’t Fall for the Gambler’s Fallacy 74\u003c\/p\u003e \u003cp\u003eAll Probabilities Are Conditional 75\u003c\/p\u003e \u003cp\u003eDon’t Swap Dependencies 76\u003c\/p\u003e \u003cp\u003eBayes’ Theorem 76\u003c\/p\u003e \u003cp\u003eEnsure the Probabilities Have Meaning 79\u003c\/p\u003e \u003cp\u003eCalibration 80\u003c\/p\u003e \u003cp\u003eRare Events Can, and Do, Happen 80\u003c\/p\u003e \u003cp\u003eChapter Summary 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Challenge the Statistics 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eQuick Lessons on Inference 83\u003c\/p\u003e \u003cp\u003eGive Yourself Some Wiggle Room 84\u003c\/p\u003e \u003cp\u003eMore Data, More Evidence 84\u003c\/p\u003e \u003cp\u003eChallenge the Status Quo 85\u003c\/p\u003e \u003cp\u003eEvidence to the Contrary 86\u003c\/p\u003e \u003cp\u003eBalance Decision Errors 88\u003c\/p\u003e \u003cp\u003eThe Process of Statistical Inference 89\u003c\/p\u003e \u003cp\u003eThe Questions You Should Ask to Challenge the Statistics 90\u003c\/p\u003e \u003cp\u003eWhat Is the Context for These Statistics? 90\u003c\/p\u003e \u003cp\u003eWhat Is the Sample Size? 91\u003c\/p\u003e \u003cp\u003eWhat Are You Testing? 92\u003c\/p\u003e \u003cp\u003eWhat Is the Null Hypothesis? 92\u003c\/p\u003e \u003cp\u003eAssuming Equivalence 93\u003c\/p\u003e \u003cp\u003eWhat Is the Significance Level? 93\u003c\/p\u003e \u003cp\u003eHow Many Tests Are You Doing? 94\u003c\/p\u003e \u003cp\u003eCan I See the Confidence Intervals? 95\u003c\/p\u003e \u003cp\u003eIs This Practically Significant? 96\u003c\/p\u003e \u003cp\u003eAre You Assuming Causality? 96\u003c\/p\u003e \u003cp\u003eChapter Summary 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Understanding the Data Scientist’s Toolbox\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Search for Hidden Groups 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 102\u003c\/p\u003e \u003cp\u003eDimensionality Reduction 102\u003c\/p\u003e \u003cp\u003eCreating Composite Features 103\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis 105\u003c\/p\u003e \u003cp\u003ePrincipal Components in Athletic Ability 105\u003c\/p\u003e \u003cp\u003ePCA Summary 108\u003c\/p\u003e \u003cp\u003ePotential Traps 109\u003c\/p\u003e \u003cp\u003eClustering 110\u003c\/p\u003e \u003cp\u003e\u003ci\u003ek\u003c\/i\u003e-Means Clustering 111\u003c\/p\u003e \u003cp\u003eClustering Retail Locations 111\u003c\/p\u003e \u003cp\u003ePotential Traps 113\u003c\/p\u003e \u003cp\u003eChapter Summary 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Understand the Regression Model 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSupervised Learning 117\u003c\/p\u003e \u003cp\u003eLinear Regression: What It Does 119\u003c\/p\u003e \u003cp\u003eLeast Squares Regression: Not Just a Clever Name 120\u003c\/p\u003e \u003cp\u003eLinear Regression: What It Gives You 123\u003c\/p\u003e \u003cp\u003eExtending to Many Features 124\u003c\/p\u003e \u003cp\u003eLinear Regression: What Confusion It Causes 125\u003c\/p\u003e \u003cp\u003eOmitted Variables 125\u003c\/p\u003e \u003cp\u003eMulticollinearity 126\u003c\/p\u003e \u003cp\u003eData Leakage 127\u003c\/p\u003e \u003cp\u003eExtrapolation Failures 128\u003c\/p\u003e \u003cp\u003eMany Relationships Aren’t Linear 128\u003c\/p\u003e \u003cp\u003eAre You Explaining or Predicting? 128\u003c\/p\u003e \u003cp\u003eRegression Performance 130\u003c\/p\u003e \u003cp\u003eOther Regression Models 131\u003c\/p\u003e \u003cp\u003eChapter Summary 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Understand the Classification Model 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to Classification 133\u003c\/p\u003e \u003cp\u003eWhat You’ll Learn 134\u003c\/p\u003e \u003cp\u003eClassification Problem Setup 135\u003c\/p\u003e \u003cp\u003eLogistic Regression 135\u003c\/p\u003e \u003cp\u003eLogistic Regression: So What? 138\u003c\/p\u003e \u003cp\u003eDecision Trees 139\u003c\/p\u003e \u003cp\u003eEnsemble Methods 142\u003c\/p\u003e \u003cp\u003eRandom Forests 143\u003c\/p\u003e \u003cp\u003eGradient Boosted Trees 143\u003c\/p\u003e \u003cp\u003eInterpretability of Ensemble Models 145\u003c\/p\u003e \u003cp\u003eWatch Out for Pitfalls 145\u003c\/p\u003e \u003cp\u003eMisapplication of the Problem 146\u003c\/p\u003e \u003cp\u003eData Leakage 146\u003c\/p\u003e \u003cp\u003eNot Splitting Your Data 146\u003c\/p\u003e \u003cp\u003eChoosing the Right Decision Threshold 147\u003c\/p\u003e \u003cp\u003eMisunderstanding Accuracy 147\u003c\/p\u003e \u003cp\u003eConfusion Matrices 148\u003c\/p\u003e \u003cp\u003eChapter Summary 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Understand Text Analytics 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExpectations of Text Analytics 151\u003c\/p\u003e \u003cp\u003eHow Text Becomes Numbers 153\u003c\/p\u003e \u003cp\u003eA Big Bag of Words 153\u003c\/p\u003e \u003cp\u003eN-Grams 157\u003c\/p\u003e \u003cp\u003eWord Embeddings 158\u003c\/p\u003e \u003cp\u003eTopic Modeling 160\u003c\/p\u003e \u003cp\u003eText Classification 163\u003c\/p\u003e \u003cp\u003eNaïve Bayes 164\u003c\/p\u003e \u003cp\u003eSentiment Analysis 166\u003c\/p\u003e \u003cp\u003ePractical Considerations When Working with Text 167\u003c\/p\u003e \u003cp\u003eBig Tech Has the Upper Hand 168\u003c\/p\u003e \u003cp\u003eChapter Summary 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Conceptualize Deep Learning 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNeural Networks 172\u003c\/p\u003e \u003cp\u003eHow Are Neural Networks Like the Brain? 172\u003c\/p\u003e \u003cp\u003eA Simple Neural Network 173\u003c\/p\u003e \u003cp\u003eHow a Neural Network Learns 174\u003c\/p\u003e \u003cp\u003eA Slightly More Complex Neural Network 175\u003c\/p\u003e \u003cp\u003eApplications of Deep Learning 178\u003c\/p\u003e \u003cp\u003eThe Benefits of Deep Learning 179\u003c\/p\u003e \u003cp\u003eHow Computers “See” Images 180\u003c\/p\u003e \u003cp\u003eConvolutional Neural Networks 182\u003c\/p\u003e \u003cp\u003eDeep Learning on Language and Sequences 183\u003c\/p\u003e \u003cp\u003eDeep Learning in Practice 185\u003c\/p\u003e \u003cp\u003eDo You Have Data? 185\u003c\/p\u003e \u003cp\u003eIs Your Data Structured? 186\u003c\/p\u003e \u003cp\u003eWhat Will the Network Look Like? 186\u003c\/p\u003e \u003cp\u003eArtificial Intelligence and You 187\u003c\/p\u003e \u003cp\u003eBig Tech Has the Upper Hand 188\u003c\/p\u003e \u003cp\u003eEthics in Deep Learning 189\u003c\/p\u003e \u003cp\u003eChapter Summary 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Ensuring Success\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Watch Out for Pitfalls 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBiases and Weird Phenomena in Data 194\u003c\/p\u003e \u003cp\u003eSurvivorship Bias 194\u003c\/p\u003e \u003cp\u003eRegression to the Mean 195\u003c\/p\u003e \u003cp\u003eSimpson’s Paradox 195\u003c\/p\u003e \u003cp\u003eConfirmation Bias 197\u003c\/p\u003e \u003cp\u003eEffort Bias (aka the “Sunk Cost Fallacy”) 197\u003c\/p\u003e \u003cp\u003eAlgorithmic Bias 198\u003c\/p\u003e \u003cp\u003eUncategorized Bias 198\u003c\/p\u003e \u003cp\u003eThe Big List of Pitfalls 199\u003c\/p\u003e \u003cp\u003eStatistical and Machine Learning Pitfalls 199\u003c\/p\u003e \u003cp\u003eProject Pitfalls 200\u003c\/p\u003e \u003cp\u003eChapter Summary 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Know the People and Personalities 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeven Scenes of Communication Breakdowns 204\u003c\/p\u003e \u003cp\u003eThe Postmortem 204\u003c\/p\u003e \u003cp\u003eStorytime 205\u003c\/p\u003e \u003cp\u003eThe Telephone Game 206\u003c\/p\u003e \u003cp\u003eInto the Weeds 206\u003c\/p\u003e \u003cp\u003eThe Reality Check 207\u003c\/p\u003e \u003cp\u003eThe Takeover 207\u003c\/p\u003e \u003cp\u003eThe Blowhard 208\u003c\/p\u003e \u003cp\u003eData Personalities 208\u003c\/p\u003e \u003cp\u003eData Enthusiasts 209\u003c\/p\u003e \u003cp\u003eData Cynics 209\u003c\/p\u003e \u003cp\u003eData Heads 209\u003c\/p\u003e \u003cp\u003eChapter Summary 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 What’s Next? 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndex 215\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407134761303,"sku":"9781119741749","price":26.4,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119741749.jpg?v=1730498302","url":"https:\/\/bookcurl.com\/products\/becoming-a-data-head-9781119741749","provider":"Book Curl","version":"1.0","type":"link"}