Getting started with data science : making sense of data with analytics / Murtaza Haider.

Includes bibliographical references and index.

Data Science: The Sexiest Job in the 21st Century -- Storytelling at Google and Walmart -- Getting Started with Data Science -- Do We Need Another Book on Analytics? -- Repeat, Repeat, Repeat, and Simplify -- Chapters' Structure and Features -- Analytics Software Used -- What Makes Someone a Data Scientist? -- Existential Angst of a Data Scientist -- Data Scientists: Rarer Than Unicorns -- Beyond the Big Data Hype -- Big Data: Beyond Cheerleading -- Big Data Hubris -- Leading by Miles -- Predicting Pregnancies, Missing Abortions -- What's Beyond This Book? -- Summary -- Endnotes -- The Liberated Data: The Open Data -- The Caged Data -- Big Data Is Big News -- It's Not the Size of Big Data; It's What You Do with It -- Free Data as in Free Lunch -- FRED -- Quandl -- U.S. Census Bureau and Other National Statistical Agencies -- Search-Based Internet Data -- Google Trends -- Google Correlate -- Survey Data -- PEW Surveys -- ICPSR -- Summary -- Endnotes -- The Final Deliverable -- What 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 Narrative -- The Report Structure -- Have You Done Your Job as a Writer? -- Building Narratives with Data -- "Big Data, Big Analytics, Big Opportunity" -- Urban Transport and Housing Challenges -- Human Development in South Asia -- The Big Move -- Summary -- Endnotes -- 2014: The Year of Soccer and Brazil -- Using Percentages Is Better Than Using Raw Numbers -- Data Cleaning -- Weighted Data -- Cross Tabulations -- Going Beyond the Basics in Tables -- Seeing Whether Beauty Pays -- Data Set -- What Determines Teaching Evaluations? -- Does Beauty Affect Teaching Evaluations? -- Putting It All on (in) a Table -- Generating Output with Stata -- Summary Statistics Using Built-In Stata -- Using Descriptive Statistics -- Weighted Statistics -- Correlation Matrix -- Reproducing the Results for the Hamermesh and Parker Paper -- Statistical Analysis Using Custom Tables -- Summary -- Endnotes -- Telling Stories with Figures -- Data Types -- Teaching Ratings -- The Congested Lives in Big Cities -- Summary -- Endnotes -- Random Numbers and Probability Distributions -- Casino Royale: Roll the Dice -- Normal Distribution -- The Student Who Taught Everyone Else -- Statistical Distributions in Action -- Z-Transformation -- Probability of Getting a High or Low Course Evaluation -- Probabilities with Standard Normal Table -- Hypothetically Yours -- Consistently Better or Happenstance -- Mean and Not So Mean Differences -- Handling Rejections -- The Mean and Kind Differences -- Comparing a Sample Mean When the Population SD Is Known -- Left Tail Between the Legs -- Comparing Means with Unknown Population SD -- Comparing Two Means with Unequal Variances -- Comparing Two Means with Equal Variances -- Worked-Out Examples of Hypothesis Testing -- Best Bu-Apple Store Comparison -- Assuming Equal Variances -- Exercises for Comparison of Means -- Regression for Hypothesis Testing -- Analysis of Variance -- Significantly Correlated -- Summary -- Endnotes -- The Department of Obvious Conclusions -- Why Regress? -- Introducing Regression Models -- All Else Being Equal -- Holding Other Factors Constant -- Spuriously Correlated -- A Step-By-Step Approach to Regression -- Learning to Speak Regression -- The Math Behind Regression -- Ordinary Least Squares Method -- Regression in Action -- This Just In: Bigger Homes Sell for More -- Does Beauty Pay? Ask the Students -- Survey Data, Weights, and Independence of Observations -- What Determines Household Spending on Alcohol and Food -- What Influences Household Spending on Food? -- Advanced Topics -- Homoskedasticity -- Multicollinearity -- Summary -- Endnotes -- To Smoke or Not to Smoke: That Is the Question -- Binary Outcomes -- Binary Dependent Variables -- Let's Question the Decision to Smoke or Not -- Smoking Data Set -- Exploratory Data Analysis -- What Makes People Smoke: Asking Regression for Answers -- Ordinary Least Squares Regression -- Interpreting Models at the Margins -- The Logit Model -- Interpreting Odds in a Logit Model -- Probit Model -- Interpreting the Probit Model -- Using Zelig for Estimation and Post-Estimation Strategies -- Estimating Logit Models for Grouped Data -- Using SPSS to Explore the Smoking Data Set -- Regression Analysis in SPSS -- Estimating Logit and Probit Models in SPSS -- Summary -- Endnotes -- What Is Categorical Data? -- Analyzing Categorical Data -- Econometric Models of Binomial Data -- Estimation of Binary Logit Models -- Odds Ratio -- Log of Odds Ratio -- Interpreting Binary Logit Models -- Statistical Inference of Binary Logit Models -- How I Met Your Mother? Analyzing Survey Data -- A Blind Date with the Pew Online Dating Data Set -- Demographics of Affection -- High-Techies -- Romancing the Internet -- Dating Models -- Multinomial Logit Models -- Interpreting Multinomial Logit Models -- Choosing an Online Dating Service -- Pew Phone Type Model -- Why Some Women Work Full-Time and Others Don't -- Conditional Logit Models -- Random Utility Model -- Independence From Irrelevant Alternatives -- Interpretation of Conditional Logit Models -- Estimating Logit Models in SPSS -- Summary -- Endnotes -- Fundamentals of GIS -- GIS Platforms -- Freeware GIS -- GIS Data Structure -- GIS Applications in Business Research -- Retail Research -- Hospitality and Tourism Research -- Lifestyle Data: Consumer Health Profiling -- Competitor Location Analysis -- Market Segmentation -- Spatial Analysis of Urban Challenges -- The Hard Truths About Public Transit in North America -- Toronto Is a City Divided into the Haves, Will Haves, and Have Nots -- Income Disparities in Urban Canada -- Where Is Toronto's Missing Middle Class? It Has Suburbanized Out of Toronto -- Adding Spatial Analytics to Data Science -- Race and Space in Chicago -- Developing Research Questions -- Race, Space, and Poverty -- Race, Space, and Commuting -- Regression with Spatial Lags -- Summary -- Endnotes -- Introducing Time Series Data and How to Visualize It -- How Is Time Series Data Different'? -- Starting with Basic Regression Models -- What Is Wrong with Using OLS Models for Time Series Data? -- Newey-West Standard Errors -- Regressing Prices with Robust Standard Errors -- Time Series Econometrics -- Stationary Time Series -- Autocorrelation Function (ACF) -- Partial Autocorrelation Function (PCF) -- White Noise Tests -- Augmented Dickey Fuller Test -- Econometric Models for Time Series Data -- Correlation Diagnostics -- Invertible Time Series and Lag Operators -- The ARMA Model -- ARIMA Models -- Distributed Lag and VAR Models -- Applying Time Series Tools to Housing Construction -- Macro-Economic and Socio-Demographic Variables Influencing Housing Starts -- Estimating Time Series Models to Forecast New Housing Construction -- OLS Models -- Distributed Lag Model -- Out-of-Sample Forecasting with Vector Autoregressive Models -- ARIMA Models -- Summary -- Endnotes -- Can Cheating on Your Spouse Kill You? -- Are Cheating Men Alpha Males? -- UnFair Comments: New Evidence Critiques Fair's Research -- Data Mining: An Introduction -- Seven Steps Down the Data Mine -- Establishing Data Mining Goals -- Selecting Data -- Preprocessing Data -- Transforming Data -- Storing Data -- Mining Data -- Evaluating Mining Results -- Rattle Your Data -- What Does Religiosity Have to Do with Extramarital Affairs? -- The Principal Components of an Extramarital Affair -- Will It Rain Tomorrow? Using PCA For Weather Forecasting -- Do Men Have More Affairs Than Females? -- Two Kinds of People: Those Who Have Affairs, and Those Who Don't -- Models to Mine Data with Rattle -- Summary -- Endnotes.