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The fast and easy way to make sense of statistics for big data
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.


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