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You're reading from  Clojure for Data Science

Product typeBook
Published inSep 2015
Reading LevelIntermediate
Publisher
ISBN-139781784397180
Edition1st Edition
Languages
Clojure
Concepts
Data Analysis
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Author (1)
Henry Garner
Henry Garner
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Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
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Mathematical folds with Tesser

We should now understand how to use folds to calculate parallel implementations of simple algorithms. Hopefully, we should also have some appreciation for the ingenuity required to find efficient solutions that will perform the minimum number of iterations over the data.

Fortunately, the Clojure library Tesser (https://github.com/aphyr/tesser) includes implementations for common mathematical folds, including the mean, standard deviation, and covariance. To see how to use Tesser, let's consider the covariance of two fields from the IRS dataset: the salaries and wages, A00200, the unemployment compensation, A02300.

Calculating covariance with Tesser

We encountered covariance in Chapter 3, Correlation, as a measure of how two sequences of data vary together. The formula is reproduced as follows:

A covariance fold is included in tesser.math. In the following code, we'll include tesser.math as m and tesser.core as t:

(defn ex-5-17 []
  (let [data (into [] (load-data...
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Clojure for Data Science
Published in: Sep 2015Publisher: ISBN-13: 9781784397180
© 2015 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner

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