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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

Introducing AcmeContent

To help illustrate the concepts in this chapter, let's imagine that we've recently been appointed for the data scientist role at AcmeContent. The company runs a website that lets visitors share video clips that they've enjoyed online.

One of the metrics AcmeContent tracks through its web analytics is dwell time. This is a measure of how long a visitor stays on the site. Clearly, visitors who spend a long time on the site are enjoying themselves and AcmeContent wants its visitors to stay as long as possible. If the mean dwell time increases, our CEO will be very happy.

Note

Dwell time is the length of time between the time a visitor first arrives at a website and the time they make their last request to your site.

A bounce is a visitor who makes only one request—their dwell time is zero.

As the company's new data scientist, it falls to us to analyze the dwell time reported by the website's analytics and measure the success of AcmeContent's site.

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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