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 binomial distribution


The preceding histogram looks a great deal like a normal distribution, but in fact it is a binomial distribution. The two distributions are very similar, but the binomial distribution is used to model cases where we want to determine how many times a binary event is expected to occur.

Let's plot both the binomial and the normal distribution on a histogram to see how they compare:

(defn ex-4-9 []
  (let [passengers (concat (repeat 127 0)
                           (repeat 339 1))
        bootstrap (s/bootstrap passengers i/sum :size 10000)
        binomial (fn [x]
                   (s/pdf-binomial x :size 466 :prob (/ 339 466)))
        normal (fn [x]
                 (s/pdf-normal x :mean 339 :sd 9.57))]
    (-> (c/histogram bootstrap
                     :x-label "Female Survivors"
                     :series-label "Bootstrap"
                     :nbins 20
                     :density true
                     :legend true)
        (c/add-function binomial...