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Getting Started with Data Science: Making Sense of Data with Analytics

Name: Getting Started with Data Science: Making Sense of Data with Analytics
Author: Murtaza Haider
ISBN: 9780133991246

by Murtaza Haider

December 2015

Beginner

400 pages

17h 52m

English

IBM Press

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About This E-Book
Title Page
Copyright Page
Praise for Getting Started with Data Science
Dedication Page
Contents-at-a-Glance
Contents
Preface
Why I Wrote This BookWho Should Read This Book?About the BookThe Book’s Three Key Ingredients: Narrative, Graphs, and TablesThe Story Telling DifferentiatorUnderstanding Analytics in a 24/7 WorldA Quick Walkthrough of the Book
Acknowledgments
About the Author

Chapter 1. The Bazaar of Storytellers
Data Science: The Sexiest Job in the 21st CenturyStorytelling at Google and WalmartGetting Started with Data ScienceDo We Need Another Book on Analytics?Repeat, Repeat, Repeat, and SimplifyChapters’ Structure and FeaturesAnalytics Software UsedWhat Makes Someone a Data Scientist?Existential Angst of a Data ScientistData Scientists: Rarer Than UnicornsBeyond the Big Data HypeBig Data: Beyond CheerleadingBig Data HubrisLeading by MilesPredicting Pregnancies, Missing AbortionsWhat’s Beyond This Book?SummaryEndnotes
Chapter 2. Data in the 24/7 Connected World
The Liberated Data: The Open DataThe Caged DataBig Data Is Big NewsIt’s Not the Size of Big Data; It’s What You Do with ItFree Data as in Free LunchFREDQuandlU.S. Census Bureau and Other National Statistical AgenciesSearch-Based Internet DataGoogle TrendsGoogle CorrelateSurvey DataPEW SurveysICPSRSummaryEndnotes
Chapter 3. The Deliverable
The Final DeliverableWhat Is the Research Question?What Answers Are Needed?How Have Others Researched the Same Question in the Past?What Information Do You Need to Answer the Question?What Analytical Techniques/Methods Do You Need?The NarrativeThe Report StructureHave You Done Your Job as a Writer?Building Narratives with Data“Big Data, Big Analytics, Big Opportunity”Urban Transport and Housing ChallengesHuman Development in South AsiaThe Big MoveSummaryEndnotes
Chapter 4. Serving Tables
2014: The Year of Soccer and BrazilUsing Percentages Is Better Than Using Raw NumbersData CleaningWeighted DataCross TabulationsGoing Beyond the Basics in TablesSeeing Whether Beauty PaysData SetWhat Determines Teaching Evaluations?Does Beauty Affect Teaching Evaluations?Putting It All on (in) a TableGenerating Output with StataSummary Statistics Using Built-In StataUsing Descriptive StatisticsWeighted StatisticsCorrelation MatrixReproducing the Results for the Hamermesh and Parker PaperStatistical Analysis Using Custom TablesSummaryEndnotes
Chapter 5. Graphic Details
Telling Stories with FiguresData TypesTeaching RatingsThe Congested Lives in Big CitiesSummaryEndnotes
Chapter 6. Hypothetically Speaking
Random Numbers and Probability DistributionsCasino Royale: Roll the DiceNormal DistributionThe Student Who Taught Everyone ElseStatistical Distributions in ActionZ-TransformationProbability of Getting a High or Low Course EvaluationProbabilities with Standard Normal TableHypothetically YoursConsistently Better or HappenstanceMean and Not So Mean DifferencesHandling RejectionsThe Mean and Kind DifferencesComparing a Sample Mean When the Population SD Is KnownLeft Tail Between the LegsComparing Means with Unknown Population SDComparing Two Means with Unequal VariancesComparing Two Means with Equal VariancesWorked-Out Examples of Hypothesis TestingBest Buy–Apple Store ComparisonAssuming Equal VariancesExercises for Comparison of MeansRegression for Hypothesis TestingAnalysis of VarianceSignificantly CorrelatedSummaryEndnotes
Chapter 7. Why Tall Parents Don’t Have Even Taller Children
The Department of Obvious ConclusionsWhy Regress?Introducing Regression ModelsAll Else Being EqualHolding Other Factors ConstantSpuriously CorrelatedA Step-By-Step Approach to RegressionLearning to Speak RegressionThe Math Behind RegressionOrdinary Least Squares MethodRegression in ActionThis Just In: Bigger Homes Sell for MoreDoes Beauty Pay? Ask the StudentsSurvey Data, Weights, and Independence of ObservationsWhat Determines Household Spending on Alcohol and FoodWhat Influences Household Spending on Food?Advanced TopicsHomoskedasticityMulticollinearitySummaryEndnotes
Chapter 8. To Be or Not to Be
To Smoke or Not to Smoke: That Is the QuestionBinary OutcomesBinary Dependent VariablesLet’s Question the Decision to Smoke or NotSmoking Data SetExploratory Data AnalysisWhat Makes People Smoke: Asking Regression for AnswersOrdinary Least Squares RegressionInterpreting Models at the MarginsThe Logit ModelInterpreting Odds in a Logit ModelProbit ModelInterpreting the Probit ModelUsing Zelig for Estimation and Post-Estimation StrategiesEstimating Logit Models for Grouped DataUsing SPSS to Explore the Smoking Data SetRegression Analysis in SPSSEstimating Logit and Probit Models in SPSSSummaryEndnotes
Chapter 9. Categorically Speaking About Categorical Data
What Is Categorical Data?Analyzing Categorical DataEconometric Models of Binomial DataEstimation of Binary Logit ModelsOdds RatioLog of Odds RatioInterpreting Binary Logit ModelsStatistical Inference of Binary Logit ModelsHow I Met Your Mother? Analyzing Survey DataA Blind Date with the Pew Online Dating Data SetDemographics of AffectionHigh-TechiesRomancing the InternetDating ModelsMultinomial Logit ModelsInterpreting Multinomial Logit ModelsChoosing an Online Dating ServicePew Phone Type ModelWhy Some Women Work Full-Time and Others Don’tConditional Logit ModelsRandom Utility ModelIndependence From Irrelevant AlternativesInterpretation of Conditional Logit ModelsEstimating Logit Models in SPSSSummaryEndnotes
Chapter 10. Spatial Data Analytics
Fundamentals of GISGIS PlatformsFreeware GISGIS Data StructureGIS Applications in Business ResearchRetail ResearchHospitality and Tourism ResearchLifestyle Data: Consumer Health ProfilingCompetitor Location AnalysisMarket SegmentationSpatial Analysis of Urban ChallengesThe Hard Truths About Public Transit in North AmericaToronto Is a City Divided into the Haves, Will Haves, and Have NotsIncome Disparities in Urban CanadaWhere Is Toronto’s Missing Middle Class? It Has Suburbanized Out of TorontoAdding Spatial Analytics to Data ScienceRace and Space in ChicagoDeveloping Research QuestionsRace, Space, and PovertyRace, Space, and CommutingRegression with Spatial LagsSummaryEndnotes
Chapter 11. Doing Serious Time with Time Series
Introducing Time Series Data and How to Visualize ItHow Is Time Series Data Different?Starting with Basic Regression ModelsWhat Is Wrong with Using OLS Models for Time Series Data?Newey–West Standard ErrorsRegressing Prices with Robust Standard ErrorsTime Series EconometricsStationary Time SeriesAutocorrelation Function (ACF)Partial Autocorrelation Function (PCF)White Noise TestsAugmented Dickey Fuller TestEconometric Models for Time Series DataCorrelation DiagnosticsInvertible Time Series and Lag OperatorsThe ARMA ModelARIMA ModelsDistributed Lag and VAR ModelsApplying Time Series Tools to Housing ConstructionMacro-Economic and Socio-Demographic Variables Influencing Housing StartsEstimating Time Series Models to Forecast New Housing ConstructionOLS ModelsDistributed Lag ModelOut-of-Sample Forecasting with Vector Autoregressive ModelsARIMA ModelsSummaryEndnotes
Chapter 12. Data Mining for Gold
Can Cheating on Your Spouse Kill You?Are Cheating Men Alpha Males?UnFair Comments: New Evidence Critiques Fair’s ResearchData Mining: An IntroductionSeven Steps Down the Data MineEstablishing Data Mining GoalsSelecting DataPreprocessing DataTransforming DataStoring DataMining DataEvaluating Mining ResultsRattle Your DataWhat Does Religiosity Have to Do with Extramarital Affairs?The Principal Components of an Extramarital AffairWill It Rain Tomorrow? Using PCA For Weather ForecastingDo Men Have More Affairs Than Females?Two Kinds of People: Those Who Have Affairs, and Those Who Don’tModels to Mine Data with RattleSummaryEndnotes
Index
Code Snippets

Content preview from Getting Started with Data Science: Making Sense of Data with Analytics

Chapter 11. Doing Serious Time with Time Series

Time series data is a ubiquitous form of economic, environmental, and social data that intersects our lives on a daily basis. News media routinely discuss time series data and present time series charts, such as unemployment rate over the years. If you have read a news article about housing prices that carries a chart depicting the rise and fall in housing prices over time, you are already familiar with time series data.

The primary purpose of this chapter is to illustrate how one might analyze time series data. I first illustrate graphical methods to plot time series data. I then demonstrate using the familiar Ordinary Least Squares (OLS) model and its extensions to analyze time series data. This ...

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Getting Started with Data Science: Making Sense of Data with Analytics

by Murtaza Haider

Chapter 11. Doing Serious Time with Time Series

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.