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Clojure for Data Science

Clojure for Data Science

By : Garner
5 (4)
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Clojure for Data Science

Clojure for Data Science

5 (4)
By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
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11
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...

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