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

Downloading the code and data


This chapter makes use of data on individual income by the zip code provided by the U.S. Internal Revenue Service (IRS). The data contains selected income and tax items classified by state, zip code, and income classes.

It's 100 MB in size and can be downloaded from http://www.irs.gov/pub/irs-soi/12zpallagi.csv to the example code's data directory. Since the file contains the IRS Statistics of Income (SoI), we've renamed the file to soi.csv for the examples.

Note

The example code for this chapter is available from the Packt Publishing's website or https://github.com/clojuredatascience/ch5-big-data.

As usual, a script has been provided to download and rename the data for you. It can be run on the command line from within the project directory with:

script/download-data.sh

If you run this, the file will be downloaded and renamed automatically.

Inspecting the data

Once you've downloaded the data, take a look at the column headings in the first line of the file. One way...