Statistics for big data for dummies
Material type:
TextPublication details: Wiley India Pvt. Ltd. New Delhi 2022Description: xii, 366 pISBN: - 9788126558223
- 006.312 AND
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Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.312 AND (Browse shelf(Opens below)) | 1 | Available | 004878 |
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| 006.31 WIC Machine learning: | 006.312 AGG Data mining: the textbook | 006.312 AND Doing data science in R: | 006.312 AND Statistics for big data for dummies | 006.312 BIF Machine learning for data streams: | 006.312 BRA Principles of data mining | 006.312 CHA Research analytics: |
Table of content
Part I: Introducing Big Data Statistics
Chapter 1: What is Big Data and What Do You Do With It?
Chapter 2: Characteristics of Big Data: The Three Vs
Chapter 3: Using Big Data: The Hot Applications
Chapter 4: Understanding Probabilities
Chapter 5: Basic Statistical Ideas
Part II: Preparing and Cleaning Data
Chapter 6: Dirty Work: Preparing Your Data for Analysis
Chapter 7: Figuring the Format: Important Computer File Formats
Chapter 8: Checking Assumptions: Testing for Normality
Chapter 9: Dealing with Missing or Incomplete Data
Chapter 10: Sending Out a Posse: Searching for Outliers
Part III: Exploratory Data Analysis (EDA)
Chapter 11: An Overview of Exploratory Data Analysis (EDA)
Chapter 12: A Plot to Get Graphical: Graphical Techniques
Chapter 13: You're the Only Variable for Me: Univariate Statistical Techniques
Chapter 14: To All the Variables We've Encountered: Multivariate Statistical Techniques
Chapter 15: Regression Analysis
Chapter 16: When You've Got the Time: Time Series Analysis
Part IV: Big Data Applications
Chapter 17: Using Your Crystal Ball: Forecasting with Big Data
Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer
Chapter 19: Seeking Free Sources of Financial Data
Part V: The Part of Tens
Chapter 20: Ten (or So) Best Practices in Data Preparation
Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA)
Index
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.
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