Alan Anderson, PhD, is a professor of economics and finance at Fordham University and New York University. He's a veteran economist, risk manager, and fixed income analyst.David Semmelroth is an experienced data analyst, trainer, and statistics instructor who consults on customer databases and database marketing.

• Introduction 1Part I: Introducing Big Data Statistics 7Chapter 1: What Is Big Data and What Do You Do With It? 9Chapter 2: Characteristics of Big Data: The Three Vs 19Chapter 3: Using Big Data: The Hot Applications 27Chapter 4: Understanding Probabilities 41Chapter 5: Basic Statistical Ideas 57Part II: Preparing and Cleaning Data 81Chapter 6: Dirty Work: Preparing Your Data for Analysis 83Chapter 7: Figuring the Format: Important Computer File Formats 99Chapter 8: Checking Assumptions: Testing for Normality 107Chapter 9: Dealing with Missing or Incomplete Data 119Chapter 10: Sending Out a Posse: Searching for Outliers 129Part III: Exploratory Data Analysis (EDA) 141Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143Chapter 12: A Plot to Get Graphical: Graphical Techniques 155Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191Chapter 15: Regression Analysis 215Chapter 16: When You’ve Got the Time: Time Series Analysis 243Part IV: Big Data Applications 269Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297Chapter 19: Seeking Free Sources of Financial Data 319Part V: The Part of Tens 331Chapter 20: Ten (or So) Best Practices in Data Preparation 333Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339Index 349

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Alan Anderson, PhD, is a professor of economics and finance at Fordham University and New York University. He's a veteran economist, risk manager, and fixed income analyst.David Semmelroth is an experienced data analyst, trainer, and statistics instructor who consults on customer databases and database marketing.

• Introduction 1Part I: Introducing Big Data Statistics 7Chapter 1: What Is Big Data and What Do You Do With It? 9Chapter 2: Characteristics of Big Data: The Three Vs 19Chapter 3: Using Big Data: The Hot Applications 27Chapter 4: Understanding Probabilities 41Chapter 5: Basic Statistical Ideas 57Part II: Preparing and Cleaning Data 81Chapter 6: Dirty Work: Preparing Your Data for Analysis 83Chapter 7: Figuring the Format: Important Computer File Formats 99Chapter 8: Checking Assumptions: Testing for Normality 107Chapter 9: Dealing with Missing or Incomplete Data 119Chapter 10: Sending Out a Posse: Searching for Outliers 129Part III: Exploratory Data Analysis (EDA) 141Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143Chapter 12: A Plot to Get Graphical: Graphical Techniques 155Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191Chapter 15: Regression Analysis 215Chapter 16: When You’ve Got the Time: Time Series Analysis 243Part IV: Big Data Applications 269Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297Chapter 19: Seeking Free Sources of Financial Data 319Part V: The Part of Tens 331Chapter 20: Ten (or So) Best Practices in Data Preparation 333Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339Index 349

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Alan Anderson, PhD, is a professor of economics and finance at Fordham University and New York University. He's a veteran economist, risk manager, and fixed income analyst.David Semmelroth is an experienced data analyst, trainer, and statistics instructor who consults on customer databases and database marketing.

• Introduction 1Part I: Introducing Big Data Statistics 7Chapter 1: What Is Big Data and What Do You Do With It? 9Chapter 2: Characteristics of Big Data: The Three Vs 19Chapter 3: Using Big Data: The Hot Applications 27Chapter 4: Understanding Probabilities 41Chapter 5: Basic Statistical Ideas 57Part II: Preparing and Cleaning Data 81Chapter 6: Dirty Work: Preparing Your Data for Analysis 83Chapter 7: Figuring the Format: Important Computer File Formats 99Chapter 8: Checking Assumptions: Testing for Normality 107Chapter 9: Dealing with Missing or Incomplete Data 119Chapter 10: Sending Out a Posse: Searching for Outliers 129Part III: Exploratory Data Analysis (EDA) 141Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143Chapter 12: A Plot to Get Graphical: Graphical Techniques 155Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191Chapter 15: Regression Analysis 215Chapter 16: When You’ve Got the Time: Time Series Analysis 243Part IV: Big Data Applications 269Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297Chapter 19: Seeking Free Sources of Financial Data 319Part V: The Part of Tens 331Chapter 20: Ten (or So) Best Practices in Data Preparation 333Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339Index 349

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