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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Basics of statistics


The field of statistics is all about using mathematical procedures to summarize the raw facts and figures of a dataset in some meaningful way so that it makes sense to you. This includes, and is not limited to: gathering data, analyzing it, interpreting it, and representing it.

The field of statistics exists mainly because it is usually impossible to collect data for the entire population. So using statistical techniques, we estimate the population parameters using the sample statistics by addressing the uncertainties.

In this section, we will cover some basic statistics and analysis techniques on which we are going to build up our complete understanding of the concepts covered in this book.

The study of statistics can be broadly categorized into two main branches:

  • Descriptive statistics

  • Inferential statistics

The following diagram depicts these two terms and shows how we estimate the population parameters from samples:

Before we get started on these, it is important to get...

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