Statistics for Big Data For Dummies

Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more.

Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool.

• Helps you to identify valid, useful, and understandable patterns in data
• Provides guidance on extracting previously unknown information from large databases
• Shows you how to discover patterns available in big data
• Gives you access to the latest tools and techniques for working in big data

If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.

Introduction 1

Part I: Introducing Big Data Statistics 7

Chapter 1: What Is Big Data and What Do You Do With It? 9

Chapter 2: Characteristics of Big Data: The Three Vs 19

Chapter 3: Using Big Data: The Hot Applications 27

Chapter 4: Understanding Probabilities 41

Chapter 5: Basic Statistical Ideas 57

Part II: Preparing and Cleaning Data 81

Chapter 6: Dirty Work: Preparing Your Data for Analysis 83

Chapter 7: Figuring the Format: Important Computer File Formats 99

Chapter 8: Checking Assumptions: Testing for Normality 107

Chapter 9: Dealing with Missing or Incomplete Data 119

Chapter 10: Sending Out a Posse: Searching for Outliers 129

Part III: Exploratory Data Analysis (EDA) 141

Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143

Chapter 12: A Plot to Get Graphical: Graphical Techniques 155

Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173

Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191

Chapter 15: Regression Analysis 215

Chapter 16: When You’ve Got the Time: Time Series Analysis 243

Part IV: Big Data Applications 269

Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271

Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297

Chapter 19: Seeking Free Sources of Financial Data 319

Part V: The Part of Tens 331

Chapter 20: Ten (or So) Best Practices in Data Preparation 333

Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339

Index 349

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.

Learn to:

• Collect, clean, and interpret data
• Effectively communicate data analysis
• Make good predictions

Big data making you dizzy? Relax—here's what it's all about

Big data figures into everything from weather forecasting to political polling. Don't let it give you a big headache; use this friendly book to learn about it in manageable, bite-size chunks. You'll get a handle on the statistical methods used when working with big data, applications for it, ways to organize and check data, and a whole lot more.

• Solving the big mystery—find out what big data is, characteristics that define it, how it's used, and what it makes possible
• How to handle it — explore statistical techniques used with big data, including probability distributions, regression analysis, time series analysis, and forecasting techniques
• Getting graphical — learn how big data can be analyzed with graphical techniques and how to identify valid, useful, and understandable patterns in data
• A variable approach — examine key univariate and multivariate statistical techniques for analyzing data
• Thinking ahead — discover techniques for forecasting the future values of a dataset
• There's a tool for that — learn about the best software packages and programming tools for analyzing statistical data

Open the book and find:

• Ways to extract previously unknown information from a database
• Tips for data collection and cleaning
• Techniques for analyzing time series data
• How to check data for missing information
• What to do with outliers in a dataset
• Some surprising uses for big data
• An overview of modeling techniques