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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

Measures of variation


It is good to have knowledge of the variation of values in the dataset. Various statistical functions facilitate:

  • span(arr): span is used to calculate the total spread of the dataset, which is maximum(arr) to minimum(arr):

  • variation(arr): Also called the coefficient of variance. It is the ratio of the standard deviation to the mean of the dataset. In relation to the mean of the population, CV denotes the extent of variability. Its advantage is that it is a dimensionless number and can be used to compare different datasets.

Standard error of mean: We work on different samples drawn from the population. We compute the means of these samples and call them sample means. For different samples, we wouldn't be having the same sample mean but a distribution of sample means. The standard deviation of the distribution of these sample means is called standard error of mean.

In Julia, we can compute standard error of mean using sem(arr).

Mean absolute deviation is a robust measure...

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