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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

Basic statistical summaries


Although, we are currently using RDatasets, about which we have sufficient details and documentation, these methods and techniques can be extended to other datasets.

Let's use a different dataset:

We are using another dataset from the RDatasets package. These are exam scores from Inner London. To get some information about the dataset, we will use the describe() function, which we have already discussed in previous chapters:

The columns are described as follows:

  • Length refers to the number of records (rows).

  • Type refers to the data type of the column. Therefore, School is of the Pooled ASCIIString data type.

  • NA and NA% refer to the number and percentage of the NA values present in the column. This is really helpful as you don't need to manually check for missing records now.

  • Unique refers to the number of unique records present in the column.

  • Min and Max are the minimum and maximum values present in the column (this does not apply to columns having ASCIIStrings...

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