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Clojure for Data Science

You're reading from Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

Product type Paperback

Published in Sep 2015

Publisher

ISBN-13 9781784397180

Length 608 pages

Edition 1st Edition

Languages

Clojure

Concepts

Data Analysis

Author (1):

Henry Garner

View More author details

Table of Contents (12) Chapters

Preface

1. Statistics FREE CHAPTER

2. Inference

3. Correlation

4. Classification

5. Big Data

6. Clustering

7. Recommender Systems

8. Network Analysis

9. Time Series

10. Visualization

Index

About the data

This chapter will make use of data about the passengers on the Titanic, which famously sank on her maiden voyage in 1912 after hitting an iceberg. The survival rates of passengers were strongly affected by a variety of factors, including class and sex.

The dataset is derived from a painstakingly compiled dataset produced by Michael A. Findlay. For more information about how the data was derived, including links to original sources, consult the book's wiki at http://wiki.clojuredatascience.com.

Note

The example code for this chapter is available from Packt Publishing's website or from https://github.com/clojuredatascience/ch4-classification.

The data is small enough to have been included together with the source code in the data directory.

The rest of the chapter is locked

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Authors (1)

Garner

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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