{"product_id":"becoming-a-data-head-isbn-9781119741749","title":"Becoming a Data Head","description":"\u003cp\u003e\u003cb\u003e\"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful.\"\u003cbr\u003e\u003c\/b\u003eThomas H. Davenport, Research Fellow, Author of \u003ci\u003eCompeting on Analytics\u003c\/i\u003e, \u003ci\u003eBig Data @ Work\u003c\/i\u003e, and \u003ci\u003eThe AI Advantage\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eYou've heard the hype around data - now get the facts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eBecoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning\u003c\/i\u003e, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.\u003c\/p\u003e \u003cp\u003eYou'll learn how to:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThink statistically and understand the role variation plays in your life and decision making\u003c\/li\u003e \u003cli\u003eSpeak intelligently and ask the right questions about the statistics and results you encounter in the workplace\u003c\/li\u003e \u003cli\u003eUnderstand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence\u003c\/li\u003e \u003cli\u003eAvoid common pitfalls when working with and interpreting data\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBecoming a Data Head\u003c\/i\u003e is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.\u003c\/p\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 \u003cp\u003e\u003cb\u003eALEX J. GUTMAN, PhD,\u003c\/b\u003e is a Data Scientist, Corporate Trainer, and Accredited Professional Statistician. His professional focus is on statistical and machine learning and he has extensive experience working as a Data Scientist for the Department of Defense and two Fortune 50 companies.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJORDAN GOLDMEIER\u003c\/b\u003e is a Data Scientist, author, speaker, and community leader. He is a seven-time recipient of the Microsoft Most Valuable Professional Award and he has taught analytics to members of the Pentagon and Fortune 500 companies.\u003c\/p\u003e \u003cp\u003e\"\u003ci\u003eThis book is for anyone, or any organization, asking how to bring a data mindset to the whole company, not just those trained in the space.\"\u003c\/i\u003e\u003cbr\u003e\u003cb\u003e—Eric Weber,\u003c\/b\u003e Head of Experimentation \u0026amp; Metrics Research at Yelp\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA concise and readable guide to data and data science\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn \u003ci\u003eBecoming a Data Head\u003c\/i\u003e, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBecoming a Data Head\u003c\/i\u003e will show you how to:\u003cbr\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThink statistically and understand the role variation plays in your life and decision making.\u003c\/li\u003e \u003cli\u003eBecome data literate—speak intelligently and ask the right questions about the statistics and results you encounter in the workplace.\u003c\/li\u003e \u003cli\u003eUnderstand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence.\u003c\/li\u003e \u003cli\u003eAvoid common pitfalls when working with and interpreting data.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBecoming a Data Head\u003c\/i\u003e is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.  \u003c\/p\u003e\u003cp\u003e\"Big Data, Data Science, Machine Learning, Artificial Intelligence, Neural Networks, Deep Learning...It can be buzzword bingo, but make no mistake, everything is becoming “datafied” and an understanding of data problems and the data science toolset is becoming a requirement for every business person. Alex and Jordan have put together a must read whether you are just starting your journey or already in the thick of it. They made this complex space simple by breaking down the 'data process' into understandable patterns and using everyday examples and events over our history to make the concepts relatable.\"\u003cbr\u003e\u003cb\u003e– Milen Mahadevan\u003c\/b\u003e, President of 84.51°\u003c\/p\u003e \u003cp\u003e\"What I love about this book is its remarkable breadth of topics covered, while maintaining a healthy depth in the content presented for each topic. I believe in the pedagogical concept of 'Talking the Walk,' which means being able to explain the hard stuff in terms that broad audiences can grasp. Too many data science books are either too specialized in taking you down the deep paths of mathematics and coding ('Walking the Walk') or too shallow in over-hyping the content with a plethora of shallow buzzwords ('Talking the Talk'). You can take a great walk down the pathways of the data field in Alex and Jordan’s without fear of falling off the path. The journey and destination are well worth the trip, and the talk.\"\u003cbr\u003e\u003cb\u003e– Kirk Borne\u003c\/b\u003e, Data Scientist and Top Worldwide Influencer in Data Science\u003c\/p\u003e \u003cp\u003e\"The most clear, concise, and practical characterization of working in corporate analytics that I’ve seen. If you want to be a killer analyst and ask the right questions, this is for you.\"\u003cbr\u003e\u003cb\u003e– Kristen Kehrer\u003c\/b\u003e, Data Moves Me, LLC and LinkedIn Top Voices in Data Science \u0026amp; Analytics\u003c\/p\u003e \u003cp\u003e\"THE book that business and technology leaders need to read to fully understand the potential, power, AND limitations of data science.\"\u003cbr\u003e\u003cb\u003e– Jennifer L. L. Morgan\u003c\/b\u003e, PhD, Analytical Chemist at Procter and Gamble\u003c\/p\u003e \u003cp\u003e\"You've heard it before: 'We need to be doing more machine learning. Why aren't we doing more sophisticated data science work?' Data science isn't the magic unicorn that will solve all of your company's problems. \u003ci\u003eBecoming a Data Head\u003c\/i\u003e brings this idea to life by highlighting when data science is (and isn't) the right approach and the common pitfalls to watch out for, explaining it all in a way that a data novice can understand. This book will be my new 'pocket reference' when communicating complicated concepts to non-technically trained leaders.\" \u003cbr\u003e\u003cb\u003e– Sandy Steiger\u003c\/b\u003e, Director, Center for Analytics and Data Science at Miami University\u003c\/p\u003e \u003cp\u003e\"Individuals and organizations want to be data driven. They say they are data driven. \u003ci\u003eBecoming a Data Head\u003c\/i\u003e shows them how to actually become data driven, without the assumption of a statistics or data background. This book is for anyone, or any organization, asking how to bring a data mindset to the whole company, not just those trained in the space.\" \u003cbr\u003e\u003cb\u003e– Eric Weber\u003c\/b\u003e, Head of Experimentation \u0026amp; Metrics Research, Yelp\u003c\/p\u003e \u003cp\u003e\"What is keeping data science from reaching its true potential? It is not slow algorithms, lack of data, lack of computing power, or even lack of data scientists. \u003ci\u003eBecoming a Data Head\u003c\/i\u003e tackles the biggest impediment to data science success, the communication gap between the data scientist and the executive. Gutman and Goldmeier provide creative explanations of data science techniques and how they are used with clear everyday relatable examples. Managers and executives, and anyone wanting to better understand data science will learn a lot from this book. Likewise, data scientists who find it challenging to explain what they are doing will also find great value in \u003ci\u003eBecoming a Data Head\u003c\/i\u003e.\"\u003cbr\u003e\u003cb\u003e– Jeffrey D. Camm\u003c\/b\u003e, PhD, Center for Analytics Impact, Wake Forest University\u003c\/p\u003e \u003cp\u003e\"\u003ci\u003eBecoming a Data Head\u003c\/i\u003e raises the level of education and knowledge in an industry desperate for clarity in thinking. A must read for those working with and within the growing field of data science and analytics.\"  \u003cbr\u003e\u003cb\u003e– Dr. Stephen Chambal\u003c\/b\u003e, VP for Corporate Growth at Perduco (DoD Analytics Company)\u003c\/p\u003e \u003cp\u003e\"Gutman and Goldmeier filter through much of the noise to break down complex data and statistical concepts we hear today into basic examples and analogies that stick. \u003ci\u003eBecoming a Data Head\u003c\/i\u003e has enabled me to translate my team’s data needs into more tangible business requirements that make sense for our organization. A great read if you want to communicate your data more effectively to drive your business and data science team forward!\"\u003cbr\u003e\u003cb\u003e– Justin Maurer\u003c\/b\u003e, Engineering and Data Science Manager at Google\u003c\/p\u003e \u003cp\u003e\"As an aerospace engineer with nearly 15 years experience, \u003ci\u003eBecoming a Data Head\u003c\/i\u003e made me aware of not only what I personally want to learn about data science, but also what I need to know professionally to operate in a data-rich environment. This book further discusses how to filter through often overused terms like artificial intelligence. This is a book for every mid-level program manager learning how to navigate the inevitable future of data science.\"\u003cbr\u003e\u003cb\u003e– Josh Keener\u003c\/b\u003e, Aerospace Engineer and Program Manager\u003c\/p\u003e \u003cp\u003e\"A must read for an in-depth understanding of data science for senior executives.\"\u003cbr\u003e\u003cb\u003e– Cade Saie\u003c\/b\u003e, Chief Data Officer\u003c\/p\u003e \u003cp\u003e\"Gutman and Goldmeier offer practical advice for asking the right questions, challenging assumptions, and avoiding common pitfalls. They strike a nice balance between thoroughly explaining concepts of data science while not getting lost in the weeds. This book is a useful addition to the toolbox of any analyst, data scientist, manager, executive, or anyone else who wants to become more comfortable with data science.\"\u003cbr\u003e\u003cb\u003e– Jeff Bialac\u003c\/b\u003e, Senior Supply Chain Analyst at Kroger\u003c\/p\u003e \u003cp\u003e\"Gutman and Goldmeier have written a book that is as useful for applied statisticians and data scientists as it is for business leaders and technical professionals. In demystifying these complex statistical topics, they have also created a common language that bridges the longstanding communication divide that has — until now — separated data work from business value.\"\u003cbr\u003e\u003cb\u003e– Kathleen Maley\u003c\/b\u003e, Chief Analytics Officer at datazuum\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988796883173,"sku":"NP9781119741749","price":42.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119741749.jpg?v=1761781624","url":"https:\/\/k12savings.com\/products\/becoming-a-data-head-isbn-9781119741749","provider":"K12savings","version":"1.0","type":"link"}