Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

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)
11
Index

The browser simulation


An HTML page has been supplied in the resources directory of the sample project. Open the page in any modern browser and you should see something similar to the following image:

The left of the page shows a dual histogram with the distribution of two samples, both taken from an exponential distribution. The means of the populations from which the samples are generated are controlled by the sliders at the top right corner of the web page in the box marked as Parameters. Underneath the histogram is a plot showing the two probability densities for the population means based on the samples. These are calculated using the t-distribution, parameterized by the degrees of freedom of the sample. Below these sliders, in a box marked as Settings, are another pair of sliders that set the sample size and confidence intervals for the test. Adjusting the confidence intervals will crop the tails of the t-distributions; at the 95 percent confidence interval, only the central 95 percent...