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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Scalar statistics


Various functions are provided by Julia's package to compute various statistics. These functions are used to describe data in different ways as required.

Standard deviations and variances

The mean and median we earlier computed (in the describe() function) are measures of central tendency. Mean refers to the center computed after applying weights to all the values and median refers to the center of the list.

This is only one piece of information and we would like to know more about the dataset. It would be good to have knowledge about the spread of data points across the dataset. We cannot use just the min and max functions as we can have outliers in the dataset. Therefore, these min and max functions will lead to incorrect results.

Variance is a measurement of the spread between data points in a dataset. It is computed by calculating the distance of numbers from the mean. Variance measures how far each number in the set is from the mean.

The following is the formula for variance...

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